Open-source malware has changed shape.

What once focused on noisy cryptomining has moved toward something far more valuable: access.

Our recent data shows attackers are increasingly targeting credentials and secrets embedded in software dependencies, with UK organizations firmly in scope.

This shift marks a move away from opportunistic abuse toward deliberate supply-chain compromise. Instead of draining compute cycles, attackers are positioning themselves inside build pipelines and developer workflows.

The goal is persistence, not disruption.

For organizations that rely heavily on open source software, this fundamentally changes both the threat model and the potential impact.

This is what “shift left” actually means in 2026: controlling what enters the build, not just detecting what runs in production.

Why credential theft has overtaken cryptomining

More than half of malicious open-source packages now focus on stealing credentials and secrets, overtaking cryptomining as the dominant threat type. The reason is straightforward. Credentials offer lasting value. They provide persistent access, broader reach across environments, and a lower risk of detection than resource abuse. A stolen token or API key can unlock entire systems, not just a single machine.

Cryptomining, by contrast, is easy to spot and quick to shut down. It consumes resources and triggers alerts. Credential theft blends in and can be executed in seconds. It exploits the trust placed on developer workflows to operate in a safe environment.

For attackers looking to maximize return while minimizing exposure, this approach maximizes returns whilst doing away with the risk of being discovered.

The implication is clear: protecting runtime infrastructure is no longer enough. The security boundary now starts at dependency intake and at the developer environment.

Multi-stage malware becomes the norm

Modern open-source malware is rarely single-purpose. Our analysis shows dropper and loader behavior increasing by nearly 2,900 percent year over year in Q1 2025, signaling a shift toward engineered, multi-stage attacks.

Around 77 percent of malicious packages distributed through open source ecosystems now combine multiple threat types. Droppers appear in nearly all observed cases, while secret exfiltration features in close to two-thirds.

These packages are designed to evolve after installation, pulling in additional payloads or changing behavior over time. This reflects industrialized campaigns rather than opportunistic experimentation. Attackers are investing in resilience, stealth, and scale.

For defenders, this means signature-based thinking is outdated. If malware is modular and adaptive, controls must focus on provenance, behavior, and prevention before execution. Again, this is what “shift left” actually means: securing the build graph itself, not just the workloads it produces.

Supply chains under direct pressure

The widespread use of open source, particularly within the JavaScript ecosystem, creates systemic exposure. Modern applications routinely depend on hundreds of direct and transitive npm packages. That density of reuse creates efficiency, but also amplifies upstream risk.

Recent activity linked to the Lazarus group illustrates the threat. More than 200 malicious packages were identified, almost all concentrated in npm. When a single ecosystem underpins financial services platforms, government services, and critical national infrastructure, concentration risk becomes a strategic issue.

A compromised dependency does not stay isolated. It propagates through shared frameworks, internal libraries, and CI pipelines. In sectors built on speed and reuse, upstream compromise quickly becomes downstream impact. This is why dependency governance is no longer just a developer hygiene issue; it is a board-level supply-chain concern.

Automation turns one package into thousands of compromises

Today’s malware increasingly targets CI/CD pipelines and developer workflows optimized for automation. When a compromised dependency enters a build, it can quietly extract API keys, certificates, and access tokens without triggering runtime alerts. Automation does the rest.

What starts as a single poisoned package can spread across hundreds or thousands of builds. The very systems designed to accelerate delivery now accelerate compromise.

The practical takeaway is uncomfortable but necessary: if build systems are automated, security controls must be automated at the same level. Manual review cannot scale against automated distribution.

AI coding assistants and the hallucination problem

AI-assisted development introduces an additional layer of risk. Studies and testing have shown that large language models can, in a meaningful percentage of cases, suggest packages or functions that do not exist. Developers under time pressure may attempt to install or rely on these hallucinated dependencies, unknowingly expanding the attack surface.

Hallucinated package names, fabricated examples, and unsafe dependency suggestions can quietly undermine supply-chain integrity. Attackers are already exploiting naming conventions and trust models to seed packages that appear legitimate to both humans and machines.

Each hallucination creates rework, friction, and lost productivity. Much of this waste could be reduced if AI systems were grounded in authoritative, real-time package intelligence rather than pattern prediction alone.

Our recent research reinforces this point. The company found that smaller AI models augmented with live package intelligence significantly outperformed larger standalone models when handling dependency upgrades and package selection tasks. The findings suggest that real-time ecosystem context matters more than model size alone when developers are making security-sensitive decisions. It also helps smaller models are 70x cheaper compared to frontier models.

This has direct implications for software supply-chain defense. If AI coding assistants recommend dependencies without verifying package provenance, maintenance status, or ecosystem trust signals, they risk accelerating the spread of malicious or hallucinated packages into production environments.

In practice, secure AI-assisted development will depend less on increasingly large models and more on whether those models are connected to authoritative, continuously updated software intelligence.

Here too, the lesson is upstream control. Guardrails must sit at the point of dependency selection, not after the code ships.

Why defenders are falling behind

Many UK security controls remain focused on detecting threats after code is deployed. Attackers have moved upstream. They target the build process, the dependency graph, and the trust relationships developers rely on.

This mismatch leaves organizations well prepared for runtime incidents but exposed during development. As long as defenders assume malware announces itself loudly, supply-chain compromise will continue to slip through unnoticed.

“Shift left” is often treated as a slogan. In practice, it means enforcing policy before installation, validating provenance before execution, and blocking malicious packages before they enter the graph.

Stealing the keys, not the cycles

Open-source malware has evolved from stealing compute to stealing access. Credentials unlock ecosystems, not just machines. For UK organisations, this makes supply-chain security a strategic concern rather than a technical afterthought.

Preventing malicious code from entering the build is now more effective than responding after deployment. The quiet shift from coins to credentials has already happened. The question is whether defenses will adapt quickly enough to match it.

If it isn’t automated, it won’t scale.

What organizations should prioritize now

To respond effectively, UK organisations should focus on a small number of structural controls:

● Gate dependency intake with automated policy enforcement before packages enter CI/CD.

● Continuously monitor for secret exposure within build environments and revoke compromised credentials rapidly.

● Enforce provenance and integrity verification for open-source components, including transitive dependencies.

● Ground AI coding tools in authoritative package intelligence to prevent hallucinated or malicious dependency suggestions.

None of these measures eliminate risk. But together, they realign defenses with where attackers are actually operating: upstream, automated, and inside the supply chain.

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On 1st October, Microsoft will begin disabling its Exchange Web Services (EWS) API, ahead of a full shutdown in April 2027. For many organizations, EWS has long been part of the invisible plumbing behind everyday workplace tools. It allows applications to connect to Exchange mailboxes, making it possible to access and manage data from emails, calendars, contacts and folders.

But EWS is almost 20 years old. Microsoft says it no longer aligns with modern requirements for security, scale and reliability, and its involvement in 2024’s Midnight Blizzard attack has added urgency to its retirement. The wider risk is also clear: APIs, particularly older and less visible ones, have become attractive targets for cybercriminals. Recent research found that 99% of organizations encountered API security issues in the past year.

Seen in that context, Microsoft’s decision to move customers towards more modern APIs such as Microsoft Graph makes sense. But API modernization is rarely as simple as swapping out one connection for another, and many organizations may soon face a rude awakening.

You can’t migrate what you don’t know exists

The main challenge organizations will face when migrating from EWS to Microsoft Graph is visibility. Despite its limitations, EWS still sits behind many everyday workplace processes, particularly in larger and older organizations.

Booking meetings, syncing calendars or allowing a CRM to log email activity automatically may all depend on the API. After almost two decades of use, EWS has become deeply embedded in day-to-day operations.

That creates a problem. Many organizations may no longer have a clear view of where EWS is being used, by whom or for what purpose. Some integrations will have been built years ago by developers who have since left the business. Others may sit inside legacy workflows that IT teams rarely touch.

And third-party tools that are outside of the IT team’s control commonly connect to EWS. As a result, some of these hidden dependencies are almost certain to fall through the gaps during migration, because organizations can only replace the EWS integrations they know exist.

For some organizations, this lack of visibility may make migration feel daunting, especially when EWS is tied so closely to everyday operations. It may be tempting to fall back on the old “if it isn’t broken, don’t fix it” adage, particularly if a poorly managed migration could disrupt critical tools and workflows.

But as Microsoft phases out EWS and ends support, doing nothing is not a viable option. Vital applications could lose access to Exchange, while the security risks of leaving legacy APIs embedded in the business will only become more pronounced.

Other organizations may try to avoid a difficult migration by intercepting EWS calls and translating them into Microsoft Graph, effectively rerouting traffic rather than replacing the underlying integration. At first glance, this may look like a clever workaround. In reality, it is only a temporary fix.

The same security, visibility and integration challenges remain, while every additional dependency built around this approach adds another layer of complexity. At scale, that could quickly become unmanageable.

Moving beyond avoidance

Instead of burying their heads in the sand or looking for clever, but ultimately ineffective, ways to avoid migrating from EWS, organizations should treat its retirement as a reminder of the need for robust software lifecycle management.

Many teams evaluate an API thoroughly during implementation, then rarely review it again. But APIs evolve, security issues emerge and providers introduce updates that require action. Organizations that build resilience and lifecycle management best practices into their API estates will be better placed to respond quickly to forced migrations, minimize disruption and avoid being caught out by sudden changes.

In practice, that means treating APIs as part of the digital supply chain and applying the same level of scrutiny used for third-party suppliers when evaluating a potential integration.

Critical APIs should be continuously monitored so organizations know where they are in use, what they support and how they are performing. IT teams also need to stay informed about planned provider changes, however small they may seem, and assess how these could affect their wider API management program.

From there, organizations need a fully prepared API migration plan. Some changes may demand immediate action, such as when a third party issues a critical vulnerability disclosure. Others, such as Microsoft’s EWS retirement, may come with longer lead times but be broader in scope and scale.

In either case, organizations need a holistic strategy covering API discovery, dependency mapping, ownership and management, testing, and ongoing monitoring. Without that foundation, they risk migrating only the integrations they can see, while leaving hidden dependencies to fail later.

Preparing for the future today

Microsoft disabling EWS is not the first event of its kind, and it will not be the last. As technology estates evolve, more legacy APIs will be retired in favor of newer versions that integrate more effectively and securely with modern solutions. The retirement of EWS should therefore be treated as more than a one-off Microsoft deadline. It is a warning about what happens when critical integrations are allowed to become invisible.

That makes resilience essential. With robust software lifecycle management, organizations can move away from panicked, reactive migrations and take greater control over their API estates. Those that prepare now will be better placed to manage the EWS migration, and whatever comes next.

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IT security has traditionally been about keeping threat actors out and preventing breaches. However, while the volume of software vulnerabilities has gone up and up over the past few years, the biggest risk that many companies face is not about a hacker getting in. Instead, it is how long they can stay inside and how far they can move within that network.

The reason why threat actors can move laterally is how difficult it is to manage identities effectively. Identities and credentials are used all the time to allow access, but they also serve as ways to support provisioning and software deployment processes. Simple software tokens that can make life easier for staff are targeted for the access and permissions that they provide.

Companies are waking up to this threat. Omdia research estimates that spending on identity management will increase at 75 percent of organizations during 2026, compared to 57 percent in 2025. But what issues have to be resolved, and what is the fastest way to get to those resolutions?

Understanding attack paths and identity

Threat actors want to make money. They do this by stealing company data or encrypting files for ransom. They achieve their goals through getting initial access in one place - an endpoint that is missing an update, or through a phishing email and malware download, for instance. After this, they want to move laterally through the network to something valuable.

For defenders, this lateral movement, or attack path, represents an opportunity to deny access or block attacks. Attackers use the same methods that legitimate staff have to access assets: their identities. For many attackers, there is no need to use zero day attacks when they can simply log in and get what they want, or move closer to their goal.

Understanding attack paths can be hard. For defenders looking at the business crown jewels like Intellectual Property or mission-critical applications, attack paths are often visualized as direct routes from an initial access point to those valuable assets. While this is simple to understand, attackers don’t work that way.

They don’t know your internal network topology or the most direct route to what is valuable. Instead, they are sniffing out what they can access.

This is made more complicated because each account will have its own level of access, and different accounts will have more or less permissions. Each asset will have multiple accounts that can access it. The complexity scales up rapidly as networks grow larger.

For organizations with around 1,000 employees, we estimate that they have more than 5 million attack paths. Companies also have to reckon with the number of identities they have in place - an organization with 10,000 identities would have around 22 million potential attack paths to manage.

Attack path problem compounded by AI and NHIs

Adopting Artificial Intelligence (AI) and Non-Human Identities will increase this further, from the current ratio from around five identities per employee towards 20 or 40 identities per human employee. This imminent surge of identities will then grow at an expected 20 percent year on year.

That is a lot of numbers. To put this into perspective, looking at how you understand maps and graphs can help. For example, London has 330 stations in its public transport network covering the underground and rail networks, as well as buses and riverboats.

There are millions of different routes that travelers can take to get from any place, from the fastest and obvious journeys through to more circuitous and obscure routes that don’t make sense except in context. Travelers can also shift from one kind of transport network to another where they need to. Understanding all those networks, timetables and options is very hard for someone to hold in their brain all at one.

Using a map can show up the best routes and directions to take, including those paths that are a combination of different networks. From a defense perspective, understanding the most high profile locations in that network and where security is most important can help prevent attacks. Looking at attack paths and permissions, you can see the most important locations within the network based on the level of access that they have.

In the London transport network, this would be the equivalent of security at Piccadilly Circus or King’s Cross St Pancras, as these stations are the busiest. Securing those specific nexus points can prevent an attacker taking more potential routes to something valuable, reducing the risk that attackers can get to mission-critical systems or data. Once you have locked down those central hubs, you can then look for the next major point and secure this one as well.

Getting to a secure future

This process around locking down identities and access permissions removes whole swathes of potential attack paths, rather than looking at each attack path one after another.

There are too many individual paths to manage, so corralling issues and securing those environments as a whole reduces the number of ways that threat actors can move across a network. This forces attackers to make their presence more obvious, leading to them being removed from the network.

Attacks on IT are getting faster. Trying to fix all the gaps that exist is now impossible, and defenders cannot achieve perfect security. We need to live with these imperfections, and manage security as effectively as possible in advance of any issue coming up.

While security teams might be used to defending the direct paths between systems, threat actors look for the other routes that can get them to where they want to go. Instead, we have to manage our networks based on preventing both the most risks and the most impactful risks.

By understanding attack paths and identity risks, we can look at what the highest priority issues are and then secure them. Using identity attack paths, we can understand where the risks exist and what work needs to be done.

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• Michael Dell marked 42 years since PC's Limited by reposting its 31 July 1984 statement, showing ~$135,000 of net income on roughly a million dollars of sales from $1,000 of starting capital
• The document, which he also showed in March 2018, served as a baseline for him to persuade his parents to let him skip university
• The decision helped make him a billionaire and turned Dell into a company that is currently worth over a quarter of a trillion dollars thanks to an AI-centric surge of late

Michael Dell has posted a photograph of a fairly unique single sheet of paper to X and LinkedIn: the quarterly financial statement for PC's Limited, dated 31 July 1984, produced from a dorm room at the University of Texas at Austin.

The page shows roughly a million dollars in sales, about $198,000 in gross profit, and net income of $134,762.75 for the three-month period.

The then 19-year-old Dell had just finished his freshman year as a pre-med student, and had started the business with $1,000. He used the statement to persuade his parents that he should not go back for his sophomore year.

A multi-billion dollar decision backed by receipts

Nothing on the page suggests or requires a leap of faith. It suggests the opposite: a 19-year-old who had already been running a business through an academic year, who had generated close to a million dollars of turnover, who had kept books well enough to separate gross profit from net income, and who understood that the way to win an argument with two skeptical parents was to hand them a profit and loss statement.

The decision to leave college was not a gamble that happened to pay off. It was the conclusion the numbers already supported, and Dell was sharp enough at 19 to know that presenting them in a document would carry more weight than simply presenting them as enthusiastic conversation starters.

The decision that followed allows Michael Dell to command a $236.7 billion net worth, as reported by Forbes, and to lead a company with a market cap north of $270 billion. What makes Michael Dell's story unique, however, is that it isn't the typical dropout-founder story usually told as one about conviction outrunning evidence. The document he brought to the table is evidence outrunning conviction.

It raises an interesting parallel: The question one needs to ask is not whether to bet on yourself, but at what point do you have enough data to stop calling it a bet and start calling it a data-driven conclusion.

It is also worth noting that this is not the page's first outing. Dell posted the same statement to Twitter in March 2018, describing it then as the document he used to convince his parents "it was OK for me to not go back to college".

What the document shows, when read carefully, is a teenager who has figured out something about persuasion that many people twice his age never do. His parents were not going to be moved by a description of the market for IBM-compatible upgrades.

They were going to be moved by a number they could check, printed on a page, covering a period that had already happened. Dell did not ask them to believe in the future of the personal computer. He showed them what he had already earned and let them draw their own conclusions.

The result was a computer-centric giant that makes many of the desktops, laptops, and servers that power the modern world and is synonymous with reliable tech for many users. One could argue that the singular page did not change his life. The quarter behind it did. The page was just the format he could use to prove it, and the rest is history.

• A former FBI counterintelligence supervisor is accused of memorizing seed phrases from bureau systems
• The agent moved roughly $1 million out of wallets tied to a foreign adversary without having to resort to any sort of hacking
• Investigators recovered ChatGPT conversations in which he asked how to invest the money and how to gain EU residency, and the chatbot's replies recited his age, wife, child, and property plans back to him

Patrick Steven Yaroch, a supervisory special agent in the FBI's Counterintelligence and Espionage Division, has been arrested and charged with interstate transportation and receipt of stolen goods.

An affidavit filed in the Eastern District of Virginia, claims Yaroch took roughly a million dollars in cryptocurrency from wallets he encountered while investigating a foreign adversary and then used ChatGPT to determine what to do with it.

The theft, as described, required no technical sophistication whatsoever: Yaroch held a Top Secret clearance with SCI access and had spent 2017 to 2025 on a national security squad at the FBI's Boston division working against a single adversarial nation, which NBC News reports was Russia.

An atypical heist with the alleged mastermind acting out of "frustration"

Patrick Steven Yaroch encountered the cryptocurrency wallets tied to his work in November 2024. He had researched how wallets work, created one of his own, searched the FBI's holdings for the relevant account information, and memorized the recovery seed phrases.

He then made roughly 10-12 transfers to his own wallet. No encryption was broken, and no protocol was exploited because none was in place for a man with his security clearance; he simply read a phrase off an internal system and remembered it.

The incident is particularly interesting because he self-reported, effectively turning himself in to his colleagues: on July 28 2026, he contacted a Justice Department employee he had worked with in Boston over Signal, asking to meet. They met at FBI headquarters the next day, where Yaroch reportedly began breaking down almost immediately, and the conversation moved to the other man's office.

He said the situation was "eating him up inside" and that he wanted to give all the money back. He filed an online self-report to the FBI's Security Division and told headquarters personnel he had screwed up. When agents arrived at his Ashburn home that evening, he told them, unprompted and in blunter terms, that he had messed up.

His defense, as per the affidavit, however, is slightly different from what one would expect: His stated motive was not greed. He told his colleagues he had grown frustrated that the FBI could not or would not act against those accounts, described himself as "spinning out of control" at the time, and said he decided to take matters into his own hands.

Despite this, he seemingly had a change of heart after cooperating earlier, asking for a paper containing his wallet seed phrases, which he had volunteered to agents, while declining to continue the interview without a lawyer while asking for time over the next two days.

This culminated in agents obtaining warrants, executing them on July 31 with SWAT securing the house, and recovering an iPhone, a Trezor hardware wallet, the handwritten seed phrases, a Portuguese power of attorney dated June 15, and three passports, one of them diplomatic.

Ironically, the most potentially damning evidence of his intentions comes from his conversations with AI, still on his phone and directly linked to him.

His conversations with ChatGPT convey a very different thought process: On May 28, he asked how to invest or spend a million dollars to maximize profit and return. On June 4, he asked what someone with about a million dollars should do to leave the United States and become a resident or citizen of an EU country. On June 17 he asked whether an American connecting through Turkey needs a visa. On June 26 he asked for help drafting an email to an executive about a job opening and life in Greece.

There is more evidence that he might already have acted based on the answers he received: prosecutors have found a power of attorney authorizing two Portuguese lawyers to register him with the country's tax authority and obtain a Portuguese tax identification number, and unreported foreign travel to Germany in May, Portugal later that month, and Grenada in early July, all in breach of bureau reporting rules.

His current investments seem to be equally erratically reasoned: On July 23, five days before he first confessed, Yaroch moved roughly $1.02 million into Suilend, a lending protocol on the Sui blockchain, reaching it through the Slush wallet app, which he then deleted. He parked the funds there to earn interest. When asked why he chose that service, he said he liked its logo, a water droplet.

When agents looked, the position was worth $933,756, roughly 8% below its level a week earlier. His Kraken account held another $188,570, including about $5,000 in a token called Squid and $1.67 in Bitcoin. Agents ultimately swept $925,426 into government wallets, leaving about $165,582 behind because it was dollars and could not be moved to a crypto wallet.

Yaroch is charged under sections 2314 and 2315 of the federal criminal code, the general provisions on transporting and receiving stolen goods. He is not charged with espionage, with computer fraud, or with theft of government property.

The wallets were not the government's, and that might change how they are treated legally, even as it raises important questions about the security protocols at federal agencies regarding cryptocurrencies, since they both monitor and have seized increasingly large amounts of them over the past few years.

In Yaroch's case, if the allegations hold, government protocols failed to identify the theft for nearly eighteen months before the person responsible reported himself, making the case for a potential review by federal agencies about how they handle such matters.

• Japan ramps up drone production to reduce dependence on Chinese manufacturers
• Defense startups are shifting from civilian projects into military drone manufacturing rapidly
• Japan dramatically increases funding for interceptor drones and autonomous defense systems

Japan's drive toward large-scale drone manufacturing has gained fresh urgency, as a growing cluster of startups, including Eams Robotics and Terra Drone, are now shifting from civilian projects toward defense-oriented drone production.

A report from Nikkei Asia said Japanese Prime Minister Sanae Takaichi seeks to reduce the country's reliance on Chinese suppliers.

Chinese firms currently hold about 90% of the global industrial drone market, a dominance Tokyo now views as a strategic vulnerability worth addressing.

Startups pivot from civilian work

Eams Robotics began in 2016 as an aerial photography company serving television broadcasters before moving into drone manufacturing two years later.

The company, based in Minamisoma, has since worked with Sagawa Express, the University of Tokyo, and NTT DoCoMo on autonomous flight systems.

In December 2025, the company flew a fully autonomous drone across an active railway line without a ground observer present.

It now reportedly aims to partner with the American defense firm Anduril, mirroring a wider shift taking place across the industry.

Terra Drone, the more defense-focused of the two firms, is launching mass production of Japan-made batteries for defense and industrial customers.

Batteries account for more than a quarter of a drone's cost, and Japan currently lacks a domestic supplier able to meet defense-grade demands.

Budget and battlefield pressure build

Japan's budget for unmanned defense systems jumped this year to 277 billion yen (~$1.72 billion), up from 111 billion yen (v$705 million) in 2025.

Its total defense spending is also set to rise to 3.5% of GDP, a shift expected to channel further investment into unmanned systems.

The ruling Liberal Democratic Party approved a plan on June 9 to produce roughly 80,000 drones annually by 2030, though funding details remain sketchy.

Japan's Acquisition, Technology and Logistics Agency (ATLA), the defense ministry's procurement arm, has scheduled demonstrations next month, aiming to fast-track acquisition and deployment of interceptor drones near radar sites and bases.

These systems are meant to counter long-range suicide drones such as the Shahed-136, which have flown as far as 1,100 miles in the Middle East.

The push for domestic production comes as reports surface that Tomahawk missile deliveries to Japan could face delays, after the 38-day U.S. campaign against Iran strained the U.S stockpiles.

Defense officials in both Washington and Tokyo have denied any disruption, though Japan's defense minister said domestic missile development will continue.

Unconfirmed reports suggest Anduril has discussed converting Nissan's Oppama plant into a drone production site, amid concerns over a possible Taiwan Strait conflict.

This fits into Japan's wider five-year military buildup, set to wrap up in 2027, which also covers missile networks and unmanned ground and maritime systems.

Terra Drone CEO Toru Tokushige described interest from Japan's defense ministry as "very much positive," citing lessons drawn from Ukraine's battlefield experience.

However, whether that optimism will eventually translate into real, battle-tested capability against long-range drone threats remains genuinely uncertain.

The thought of infusing AI into weapons immediately evokes deep fears, not only based on the idea that autonomous weapons may make life-and-death decisions on our behalf, but also on the prospect of errors creeping in. But for Palmer Luckey, founder of Anduril Industries, there's no alternative but to add intelligence into the weapons systems of tomorrow.

Dumb vs smart weapons

Luckey was speaking in a Q&A portion of a TED talk when he made the comparison between the weapons of today and the AI-powered weapons of tomorrow.

The talk was themed around his notion that adding intelligence to weapons systems could prevent future catastrophes, based on the notion of deterrence preventing different superpowers from waging war.

On the subject of the ethics of incorporating AI into weapons, Luckey pointed out that 'dumb weapons' may indiscriminately kill without context or an understanding of a particular target.

He used a land mine as an example of a weapon that may kill innocents or combatants – possibilities that wouldn't be considered ethically superior to his vision for smart weapons.

Intelligence-driven warfare

The rise of AI weapons has been quick but consequential, with new technologies being deployed on battlefields across the world. The war between Russia and Ukraine, in particular, has shown the potential for automated systems to dominate conflicts.

But the use of AI doesn't end at weaponry. It also involves the use of machine learning, for example, to classify different moving elements from several sources of imagery. It's the holistic picture, Luckey argues, from which he draws his conclusions rather than isolated implementations.

• Ukraine develops ground robot firing nets against incoming FPV drone attacks
• VORON combines drone interception with logistics and battlefield evacuation capabilities effectively
• Net launchers attempt physical drone captures instead of explosive aerial interceptions

Ukrainian firm SMART BIRDS has introduced an Unmanned Ground Vehicle (UGV) called VORON, built specifically to intercept first-person view drones from multiple directions simultaneously.

The system relies on Pavuk net launchers, a defensive mechanism designed to trap incoming drones before they reach their intended target.

The system was unveiled during a closed demonstration organized by the Technological Forces of Ukraine.

Net-launching system functions for drone capture

Each VORON platform can carry up to eight net launcher modules, allowing coverage against threats approaching from any angle around the vehicle.

The system engages targets at distances reaching 45 meters, with nets fully deploying after traveling 5 meters and forming a defensive radius of 5 meters.

Every two-barrel module weighs 4 kg, and operators can reload and fire the launchers repeatedly during a single engagement.

A human operator triggers the system manually through a control panel, using an onboard camera to visually confirm each threat before firing.

"The UGV operator, having seen a threat through the camera, can fire a shot from the control panel,” a company representative said.

“The equipped net gun has two nets for shooting, installed at different angles relative to the horizon, which allows you to neutralize both the "waiting drone" and the drone on the approach. If necessary, the net gun can be recharged and fired repeatedly."

This dual-angle configuration suggests engineers anticipated drones adopting either stationary or approach-based attack patterns.

Additional roles beyond drone interception

According to SMART BIRDS, the VORON platform serves purposes extending well beyond countering aerial threats.

This system also serves for logistics support, casualty evacuation, reconnaissance, and minelaying operations.

The unmanned vehicle weighs 120 kg yet can transport payloads of up to 250 kg under standard conditions.

Figures released by the company claim that the carrying capacity can increase to as much as 400 kg over shorter distances.

"We expected that sometimes the UGV would also have to evacuate personnel, so we built in increased lifting capacity," says a company representative.

The UGV’s top speed reaches 15 kilometers per hour, while the platform's operational range extends to 30 kilometers on a single battery charge.

These specifications place VORON within a category of lightweight ground robots meant for frontline support rather than heavy transport duties.

The vehicle remains in the codification process required for formal military adoption, and the company has indicated plans to integrate further mission-specific modules later.

As of the time of writing, all figures on the interception range and reliability of this device rest solely on manufacturer statements.

Without independent verification from military testing bodies or combat deployment records, these figures remain uncertain.

Net-based counter-drone systems face inherent limitations, particularly against fast-moving or evasive drone maneuvers that could avoid capture entirely.

Whether VORON performs consistently across varied battlefield conditions — weather, terrain, electronic interference — remains an open question until broader field data emerges from actual deployment.

Via Oboronka

• Korean Air and Archer will modify the Midnight eVTOL for military use
• Archer handles technical support and certification assistance for the military version
• Midnight eVTOL carries four passengers and travels roughly 100 miles per charge

South Korea is moving ahead with plans to adapt electric flying taxis for military missions after Korean Air reached an agreement with California-based Archer Aviation.

Rather than developing an entirely new aircraft, both companies will modify Archer's existing Midnight eVTOL platform to meet South Korean military standards.

The Midnight eVTOL platform has previously been evaluated by the US Air Force, prompting speculation that the service could eventually pursue a similar aircraft.

Existing aircraft could speed military deployment

Korean Air will oversee modifications required for military operations, while Archer Aviation will provide technical support together with assistance throughout the required airworthiness certification process.

The modification plan will shorten deployment timelines and also reduce some engineering and certification challenges facing both companies.

The military version is expected to support troop transportation, cargo delivery, medical evacuation, search and rescue missions, and other special operations.

This military model is also required to support flexible short-range air mobility capabilities.

Midnight eVTOL currently carries four passengers, travels approximately 100 miles (161 Km), and reaches maximum speeds approaching 150 miles (241 Km) per hour.

These performance figures place the aircraft within a category suitable for relatively short missions.

The agreement also arrives as several armed forces continue exploring electric vertical takeoff and landing aircraft for missions requiring quieter operations and greater flexibility

Military partnership could influence South Korea's wider aviation plans

The agreement also extends beyond military transport because both companies expect certification work to support South Korea's broader ambitions for advanced air mobility (AAM) development.

"Military AAM development represents a blue ocean opportunity for Korea to secure leadership in future core aviation power and industry dominance," a Korean Air official said.

"Building on our cooperation with Archer, we aim to seize this critical window for AAM development and build a new model for defense exports."

Archer's ongoing certification process with the US Federal Aviation Administration could provide technical data helping South Korea establish its own certification framework for eVTOL aircraft.

That experience could also benefit the Korean Urban Air Mobility program, which plans to introduce commercial flying taxi services across South Korea during 2028.

Korean Air believes technical knowledge gained from the Midnight project could assist regulators while supporting future commercial operations using similar electric aircraft.

Military demand may also help companies improve production capabilities before commercial operators begin placing larger orders for advanced air mobility platforms.

Neither company has announced when military deliveries could begin, leaving certification schedules and procurement timelines still awaiting further clarification from both governments.

Via The Korean Herald

• Distillation sidesteps the logic of US export controls, which restrict the chips needed to train frontier models but cannot restrict the text those models produce
• Review of more than 80 Chinese papers and patents found PLA-linked researchers distilling outputs from OpenAI and Anthropic models into small systems they can run locally
• The Trump administration is increasingly critical of the approach and has claimed Chinese research lab-created Kimi K3 distilled Anthropic's Fable model

A new investigation has claimed Chinese military researchers have used outputs from American AI models built by OpenAI and Anthropic to train domestic systems intended to advance the country's defense capabilities.

The findings are based on a Reuters review of more than 80 Chinese academic papers and patents, incorporating research compiled by the Washington-based Jamestown Foundation shared exclusively with the news agency.

Reuters says it independently verified the literature and found a further two dozen military-linked case studies of its own.

A distillation problem in an AI race that continues to heat up

The mechanism at issue is model distillation, in which the outputs of a large, capable system are used as training data for a smaller one. The smaller model inherits selected behaviors at a fraction of the compute cost and, crucially, can run on modest local hardware.

That is the strategic point. Washington's export controls are built to deny China the chips needed to train frontier models. Distillation does not need those chips, because the expensive part has already been paid for by someone else. It sits alongside Beijing's other route around the controls: substituting domestic silicon for American designs, a shift that carries its own long-term threat to Nvidia and AMD.

Sunny Cheung, the Jamestown fellow who analyzed more than 60 of the papers, frames the value as reasoning rather than answers. Getting a model to produce the right output is comparatively easy, he told Reuters, but "teaching it the reasoning behind the answer is much harder."

The papers, on his reading, show Chinese military-linked researchers trying to move that expensive proprietary reasoning into small systems they can control and deploy themselves.

There is an irony in the pattern. Beijing has been moving to restrict consumer access to Western AI models on security grounds, and has pushed its own labs away from US-designed chips, even as its military researchers mine the outputs of American systems.

The asymmetry points at what is wrong with the US instrument. Export controls regulate objects: chips, tools, hardware that crosses a border and can be counted. The thing being transferred here is text a model produced- which crosses no border in any customs sense and cannot be enumerated.

The White House has registered the problem at both the chip and distillation end, but registering it is not the same as having a lever. For now, the debate over whether distillation is theft or standard practice may matter less than the fact that neither answer offers a workable control.

• Some Android apps contain invasive data-gathering SDKs that gather location data without user consent
• Location data is then sold to advertisers, or purchased by law enforcement for targeting and tracking US citizens
• Developers, regulators, and legislators should work together to remove the incentive for SDKs to gather this data

Monetizing an app is a key part of development. But developers frequently use third-party software development kits (SDKs) rather than develop their own monetization tools.

These SDKs often come preloaded with data-collection mechanisms that allow developers to choose what types of user data it would like to collect to sell to advertisers. Sometimes, developers leave every setting on.

According to a warning from the Electric Frontier Foundation, SDKs with their default data collection settings left on are harvesting highly invasive location data, which is then passed on to advertisers and data brokers. Critically, this data can then be purchased and used by intelligence agencies, law enforcement, and even the military.

Authorities using advertising location data for questionable operations

Location data is very lucrative for advertisers. It helps increase the bid-price for advertising space, allowing local businesses or services to target those nearby with adverts.

The level of invasive data collected depends on the permissions the app asks for. But crucially, any permissions granted to the app itself are also passed on to the SDK by default.

Where approximate location data determined by the device IP is accurate to about 1.2 square miles, precise location data can narrow that area down to about 160 feet, or in some circumstances as small as 10 feet.

In some cases, the SDKs include documentation for developers to ensure restricted data processing is turned on for users that are subject to GDPR and COPPA, but these settings are not turned on by default.

Also, some SDKs can gather and share location data without the user’s meaningful consent. Two apps analyzed by EFF that harvested location data through SDKs were downloaded more than 50 million and 10 million times, respectively. Neither app showed a notice or requested consent from the user, and both failed to declare that the apps would share location data with third parties in the Google Play Store Data Safety section.

Some apps require location data as part of their core functionality. For example, your chosen weather app, or a running app, may need your location data. But for many Android apps, the consent provided for this location tracking is also deemed to be consent for SDKs.

“App-level location permissions alone cannot signal meaningful consent to location collection and sharing by third-party advertising SDKs,” the EFF report says.

In many cases, location data sold by advertisers and data brokers has been used by intelligence agencies and law enforcement in highly-targeted operations.

The EFF includes a number of recommendations for developers, regulators, and legislators:

• Developers should have a responsibility to protect the privacy of their users. They should check third-party SDKs and ensure that user data is not shared by default
• Regulators should pursue app developers that fail to ensure user data is not unlawfully passed on to third parties. Additionally, regulators should pursue companies that deliberately develop SDKs that gather invasive amounts of user data by default
• Legislators in the US should work to develop a federal law similar to GDPR to protect users from privacy violations. Banning behavioral advertising would also remove the incentive from companies developing SDKs to gather as much data as possible

• A new bill introduced to Congress wants to ban data centers from federal land
• The bill has been co-sponsored by numerous representatives across the US
• Trump's data center buildout is worsening pollution and raising utility bills for millions of Americans

A bill that would see data centers banned from being built on US federal lands has been introduced to Congress by Rep. Rashida Tlaib (MI-12).

The ‘No AI Data Centers on Federal Lands Act’ would also see existing AI data centers removed from federal land, and the sites decontaminated to protect the local environment and water systems.

“Everywhere I go in our district, our residents tell me loud and clear, data centers are not welcome in our communities. AI data centers bring noise, light, air, and water pollution, and can increase energy costs by as much as 267%,” said Rep. Tlaib, co-founder of the People’s Environmental Justice Caucus.

AI at any cost

As part of the Trump administration’s plans to remain a world leader in AI technology, the Department of Defense has requested $30 billion in funding to build secure AI data centers as part of the Pentagon’s “AI Arsenal initiative”. This initiative is part of a move to equip the US military with AI systems to be integrated into battle management and warfare operations, threat detection and analysis, and supply chain logistics.

These military data centers could also be built without being subject to the Clean Air Act, meaning construction of the sites would be allowed to go ahead without any consideration for health risks to local populations.

The Department of Energy is also looking to build 16 new data centers under guidance from the Trump administration. The DoE data centers would be provided with fast-track approval for energy generation. Many new data centers are turning to polluting gas-burning turbine generators that produce noise levels similar to international airports, and cause sickness in local populations.

“Our residents are choking on smog and already paying too much for their utility bills, yet the Trump administration wants to place heavier cost burdens on the shoulders of hardworking Americans,” Rep. Tlaib said.

“Our corrupt, AI-addicted President cannot be allowed to sacrifice our federal lands so that Big Tech oligarchs can get richer. I will continue to push back against harmful AI data centers alongside our residents to protect our public lands, health, and environment.”

“While the Trump administration willingly serves up our environment on a platter to Big Tech and their data centers, Rep. Tlaib’s proposed legislation is drawing a line in the sand,” said Camden Weber, senior climate and energy policy specialist at the Center for Biological Diversity.

“Public lands belong to the people, not to Big Tech’s bottom line. This bill is a necessary step toward holding the industry accountable and protecting the air, water and places all species depend on.”

• Securonix uncovers SMOKE#SCREEN, a campaign tricking users into installing weaponized ScreenConnect via fake Zoom/Adobe updates and business docs
• Attackers gain persistent remote access, evolving tactics to disable protections and abuse trusted services like Dropbox/Cloudflare for delivery
• Victims observed on Windows and macOS; businesses urged to verify updates via official sites and train staff against unexpected installs

Security experts Securonix Threat Research have uncovered a new malicious campaign that tricks users into installing legitimate remote monitoring and management (RMM) software.

Dubbed SMOKE#SCREEN, the campaign uses fake Zoom and Adobe update messages, as well as a whole swathe of fraudulent business-related documents (document review requests, system maintenance tools, invoices, and similar), to convince the victims to run malicious files.

Victims who don’t see through the ruse and run the files end up installing ConnectWise ScreenConnect, a legitimate RMM tool that many IT teams use to provide technical support to their coworkers and clients. However, it is also one of the more abused solutions in the criminal cyber-underworld, since it can often fly under the radar of security products.

Dangerous evolution

After installation, attackers can remotely access compromised devices, potentially allowing them to steal data, install additional threats, or move deeper into an organization’s network.

At first glance, SMOKE#SCREEN looks like a fairly standard “phishing - install legitimate RMM - remote access” campaign. However, what makes it unique is how it evolved over time, Securonix explained. Earlier versions focused on hiding the malicious activity, while newer versions attempted to disable security protections and avoid detection by security software. The attackers also used trusted services such as Dropbox and Cloudflare to deliver their files, making the activity harder to block.

Victims were observed on both Windows and macOS ecosystems, it was added.

“The SMOKE#SCREEN campaign demonstrates a capable, actively maintained, and rapidly adapting threat actor who has built a diversified toolkit around a single objective: gaining persistent, legitimate-looking remote access to victim systems through weaponized ScreenConnect deployments,” the researchers explained.

“The use of multiple social engineering themes, rotating payload hashes, cross-platform coverage, and a live staging server that doubles as a ScreenConnect relay indicates a well-resourced actor with deliberate operational security practices.”

To minimize the risk of compromise, businesses should disable receiving software updates delivered through emails, verify update requests through official websites, and instruct their employees to be cautious when opening attachments or installing tools they were not expecting.

Via The Hacker News

• One.com has launched Aida, a new all-in-one AI website builder
• You can input your website URL and Aida will instantly modernize it
• Other tools include online sales, booking system, blog, custom domain email, and hosting.

One.com, one of the biggest web hosting providers, recently announced the launch of its new Aida AI website builder.

Aida takes an all-in-one approach to website building, leveraging conversational AI. Users can simply describe their business idea in chat, letting the AI work behind the scenes to turn it into a complete website – all without any design or coding knowledge.

What’s new with Aida?

Aida can help small businesses bring dated websites into the modern era with an instant modern redesign. In practice, this means users can input an existing URL, and Aida will instantly transform it into a modern website.

Tools like this offer a lifeline for some small businesses that don’t have the budget to undertake major modernization projects on their websites. Removing the need to pay website agencies to build simple websites and undertake routine maintenance.

Moving beyond website building, you can also use Aida to set up and manage operational tools including online stores, booking systems, blogs, custom domain email, and hosting.

While Aida handles the day-to-day tasks of design and setup, One.com maintains 24/7 human support, offering a crucial safety net for any complex issues that may need a human touch.

One.com joins a string of website builders including Wix, Squarespace, and GoDaddy that have launched all-in-one AI website building and business growth tools.

• Huntress flags phishing emails spoofing Bank of America, pushing victims into different infection chains on Windows vs. macOS
• Windows users were tricked into installing ScreenConnect RMM via fake “Account Guard,” while macOS users faced credential‑harvesting forms for identity theft
• Emails mimicked Bank of America branding but came from unrelated domains; users advised to verify sender addresses to spot scams

Bank of America customers have been warned to take extra caution after experts warned of hackers spoofing the bank into trick you into downloading unwanted software and granting them access to your computer.

Security researchers Huntress revealed how it received a phishing email in their honeypot (an address set up primarily to catch scammers) claiming to have come from Bank of America.

Obviously, the message came from a domain completely unrelated to the company, but looked almost identical to the real thing, with the company logos, color schemes, and other details, meticulously imitated.

Just another ScreenConnect scam

In the email, the researchers were warned that their account was about to be “restricted” unless they “confirmed” certain information.

Depending on the platform from which the victim views the email, both the infection chain and the end goal are different. For Windows users, victims are invited to install “Account Guard”, which is described as a “powerful tool designed to protect your financial data, prevent unauthorized transactions, and other cyber threats”.

This is no guard - this is a Visual Basic script that leads to an installation of the ScreenConnect Remote Monitoring and Management (RMM) tool. ScreenConnect is not malicious itself - it is a legitimate tool - but it is also one of the most abused software out there, leveraged to grant attackers unabated access to victim computers without triggering any alarms.

For macOS users, on the other hand, the goal is different. Instead of trying to deploy malware, the attackers try to steal sensitive data. First, the victims are asked to log in to their banking account (twice - the first attempt is scripted to fail, in case the victim purposely submits the wrong password the first time).

Then, once they “log in”, the second web page asks the victims to “confirm” their details - full name, mailing address, government ID details, Social Security number (SSN), and the payment card details. This is more than enough information for an identity theft attack, or even wire fraud.

Huntress is now warning all Bank of America users to double-check the sender address for any email claiming to be from the bank, since that is the best way to know if the email is legitimate, or a scam.

• Forescout’s Vedere Labs found 15 flaws in TP‑Link Omada business networking gear, exploitable for RCE when chained with prior CVEs
• Weak trust shortcuts in zero‑touch provisioning exposed devices to client‑side code execution, hijacking, spoofing, and encrypted comms compromise
• TP‑Link released firmware updates; admins should patch immediately, with 1,800+ Omada controllers exposed online

TP-Link has patched more than a dozen vulnerabilities across multiple business networking products which could have been chained to achieve remote code execution (RCE).

Security researchers at Vedere Labs from Forescout found the flaws and published an in-depth report on the issues, which particularly affect TP-Link Omada, the company’s business networking platform for centrally managing enterprise and small-business network infrastructure.

It includes cloud-managed Wi-Fi access points, routers, switches, gateways, and controllers, all of which can be monitored and configured from a single interface.

Enabling "concrete attacks"

These support zero-touch provisioning (ZTP), a mechanism that allows IT managers to deploy and maintain devices without needing to configure each one manually and on site.

However, ZTP has to establish trust between a factory-fresh device, and a controller with no human involved, so TP-Link used different shortcuts: from hard-coded keys and certificates shared across multiple devices, to default credentials, and from guessable serial numbers as “identity”, to weak session-key randomness.

Now, Forescout says 15 vulnerabilities its researchers discovered all allow for different ways of exploiting these shortcuts, meaning a flaw anywhere in the onboarding chain can compromise every device that goes through it. These bugs would need to be combined with two previously disclosed command-injection flaws, though.

“The vulnerabilities fall into four impact categories: client-side code execution, information disclosure, device hijacking and spoofing, and compromise of encrypted communications,” Forescout said. “Combined with two previously disclosed CVEs (CVE-2025-7850 and CVE-2025-7851), these flaws enable concrete attacks that let attackers infiltrate networks through controllers and client devices.”

Out of the 15 discovered flaws, 11 received CVE identifiers, and the rest did not receive a tracking number.

Forescout said there are more than 1,800 Omada controllers accessible from the wider internet. If you are using any of the devices from the platform, you should head over to TP-Link’s download portal and grab the latest firmware for your device model.

When Anthropic's CFO revealed that over 90 per cent of the company's code is now written by its own AI, it landed as a milestone.

Tasks that once consumed hours now take 30 minutes. The productivity gains are significant.

But Anthropic is an AI-native company with some of the world's best engineering talent.

For most enterprises, the question isn't whether AI can generate code at that speed. It's whether they can govern what it generates.

Shadow AI: The new shadow IT

"Vibe coding", the term for generating code via AI, has moved into the mainstream. By some estimates, almost half of all new global code is now AI-generated. Developer productivity is up. So is debt that nobody fully understands.

Part of what's driving this is necessity. AI is helping close the engineering talent gap. Teams that lack experienced developers are using it to build at the pace the business demands. The problem is that the same shortage that makes AI indispensable also means there aren't enough senior engineers to review AI-generated code.

Research across Fortune 50 enterprises found that AI-assisted developers introduce security vulnerabilities at ten times the rate of their peers. Forty-five per cent of AI-generated code contains OWASP Top 10 vulnerabilities. Independent analyses indicate that technical debt increases by 30–41 per cent following the adoption of AI tools.

Traditional technical debt is at least visible. Engineers who cut corners know they did it. Vibe coding debt is different: developers often don't realize they have incurred it, because the code looks correct – right up until it doesn't.

We used to worry about Shadow IT. The new threat is Shadow AI: code generated at pace without architectural review, security auditing or institutional understanding of what's been built.

Unlike Shadow IT, which was typically contained within a department, Shadow AI compounds across organizational boundaries. In enterprises where systems are still deeply siloed, complex problems span multiple departments and platforms; no single team has a complete picture of what's been generated or what it depends on.

Enterprises have spent decades paying for yesterday's shortcuts. AI risks creating the next generation of legacy systems, only much faster.

Repeating the COBOL mistake

The market is flooded with tools promising to read millions of lines of COBOL or Java and convert the functionality into a modern language. This is technically impressive, but strategically flawed.

Legacy systems are full of inefficiencies, redundancies and “swivel chair” workarounds baked in years ago to compensate for other systems' limitations. Translating code line-by-line replicates that bad logic in a newer language, now running on cloud infrastructure with a modern interface on top of old code and dated processes.

To get modernization right, organizations should avoid treating it as a technical exercise. Instead, they should step back and ask whether a process is still valid, not just how to replicate it.

True modernization is about reinventing how work gets done, designed around the employee and customer experience rather than the constraints of systems built decades ago. It is a state of consistent change, and it must be driven by the North Star of clear business objectives.

The agility layer

Probabilistic AI needs to operate within deterministic boundaries to be safe and useful at enterprise scale. AI that can generate anything is not the same as AI that generates the right thing reliably with full traceability, in a context where every decision might be scrutinized by a regulator.

This is why high-stakes organizations are looking for an agility layer: a governed process platform that imposes structure, ensures auditability and keeps AI outputs within maintainable boundaries. The US Army followed this approach for security with agility to operate with certainty at speed.

Ordering ammunition in disconnected field conditions is mission-critical, with zero tolerance for ungoverned outputs. Similarly, pharmaceutical giant Merck's clinical supply chain, where regulatory scrutiny is intense and errors have patient safety consequences, required a platform that could accelerate delivery without sacrificing auditability.

These are examples of organizations trying to address the hardest part of modernization: discovery. They are using AI to extract specifications from even the most poorly documented legacy applications, converting them into visual plans covering UI, data models and process flows.

They're generating software components rather than custom code to reuse in other applications, accelerating development time and reducing technical debt. AI agents then build against those specs under human supervision, with developers assigning tasks and iterating throughout – at roughly 25 per cent of the time traditional approaches require.

The future is bespoke, not general-purpose

There is a temptation to conclude that general-purpose AI will soon render enterprise platforms obsolete. This is premature and in regulated environments, dangerous. General-purpose models are extraordinary at generating plausible outputs. They are not equipped to ensure those outputs are auditable, compliant or maintainable by teams that did not generate them.

The vibe coding wave is real, and so is the debt it is creating. The organizations that navigate the next decade successfully and avoid falling into the same old legacy traps will not be the ones that generated code the fastest in 2025 and 2026.

They'll be the ones to build governance and process boundaries into how AI was used from the start – so what was built quickly can still be understood, maintained and trusted years later.

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• ZeroBEC observes Greatness PhaaS evolving to bypass MFA and phish Microsoft 365, iCloud, Yahoo, and Google Workspace accounts
• Attackers spoofed RingCentral emails post‑ShinyHunters breach, luring victims to fake Microsoft 365 logins that capture authentication tokens
• Greatness is sold on Telegram for $289/month, enabling access to Outlook, Teams, SharePoint, OneDrive, and more across multiple regions

Microsoft 365 users have been getting phishing emails spoofing RingCentral, designed to steal their accounts even if they were protected by multi-factor authentication (MFA), experts have warned.

Security researchers ZeroBEC claim to have observed a phishing-as-a-service (PhaaS) platform called Greatness evolve to also target MFA accounts, as well.

Greatness used to be a simple credential phishing platform. However, in recent times, it evolved to target not just Microsoft 365 accounts, but also those of iCloud, Yahoo, and Google Workspace.

Grabbing MFA-approved authentication tokens

ZeroBEC notes that RingCentral recently suffered a data breach at the hands of the infamous ShinyHunters hackers, meaning there is a good chance (although not confirmed) that the threat actors exfiltrated a list of emails belonging to RingCentral customers from that attack, and used it in this attack.

Now, RingCentral customers have been getting emails that look as if they are coming from the company itself, despite being mailed from an unknown mail server, and despite failing SPF and DMARC checks. The emails are the standard fake voicemail and performance-review notifications which, if clicked, redirect the victim to attacker-owned infrastructure spoofing the Microsoft 365 login page.

Through this malicious landing page, Greatness operators are able to capture MFA-approved authentication tokens, bypassing the login process entirely and moving straight into victim accounts.

From there, they would enumerate Outlook mailboxes, Teams conversations, and SharePoint sites. They would also access OneDrive files, contacts, calendars, and registered applications through Microsoft Graph.

The number of victims is unknown at the time, but ZeroBEC says Greatness has been active for at least four years now, targeting users in the US, UK, Australia, Canada, and South Africa.

According to BleepingComputer, the platform is being advertised for sale on Telegram channels with “thousands of subscribers”, and is currently being offered for a monthly fee of $289.

Via BleepingComputer

• Aikido researchers uncovers ChainDrop, a Shai‑Hulud variant infecting 1,300+ npm packages with an infostealer
• Attackers compromised GitHub accounts tied to popular libraries (Keyv, Cacheable, flat‑cache, file‑entry‑cache) and pushed tainted releases with 2B monthly downloads
• Malware exfiltrates developer/cloud credentials and secrets to a public GitHub repo; admins should treat affected systems as compromised even after removal

Another Shai-Hulud variant has been discovered in the wild, infecting more than 1,300 npm packages with an infostealer.

Security researchers Aikido reported finding “at least 868 packages (across 1381 versions) that have been compromised by the worm.”

Shai-Hulud is a self-propagating supply chain malware that targets software developers by compromising open-source packages and CI/CD pipelines. It steals credentials, API keys, and access tokens and then uses those stolen secrets to publish additional malicious packages.

What to do in case of an infection

In May 2026, actors claiming to be associated with the TeamPCP group publicly released the Shai-Hulud worm's source code, saying they were “open sourcing the carnage” and inviting other threat actors to adopt and modify the code. Since then, there were multiple copycat campaigns and variants, including this one which Aikido dubbed ‘ChainDrop’.

Aikido said the attackers compromised the GitHub account of the person maintaining Keyv and Cacheable, widely used open source JavaScript libraries for caching data in Node.js applications. From there, they were able to move into other popular utilities such as flat-cache and file-entry-cache, as well as packages associated with organizations such as Deliveroo, Ornikar, OneReach, Picsart, Qlik, and ServiceTitan.

The malware was pushed directly into the projects’ main branches, and then generated additional package releases. The compromised packages have a combined 2 billion monthly downloads.

Aikido says the infostealer grabs developer and cloud credentials, encrypts them, and then sends them to a public GitHub repository called “Shai-Hulud: Here We Go Again.”

It also steals local configuration files, GitHub PATs, workflow tokens, and other ghp_, gho_, and ghs_ tokens, certain npm tokens, GitHub Actions secrets, AWS credentials, Kubernetes secrets, and more.

The researchers are saying system admins who installed a tainted package should treat their developer workstation or CI/CD runner as compromised, even if they removed the package.

Not too long ago, one of the world's most data-driven companies admitted that its AI spending was becoming “harder to justify”. Uber's President and COO, Andrew Macdonald, told the Rapid Response podcast that the company had blown through its entire 2026 AI budget in roughly four months, with around 5,000 engineers leaning on Anthropic's Claude Code.

Uber isn’t alone. Forrester research found that enterprises are deferring around 25% of planned AI spend to 2027, as CFO scrutiny over ROI intensifies. And McKinsey's State of AI report summarized that while 62% of companies are experimenting with AI agents, only 23% have scaled them in even a single business function.

While these may look like the statistics of a technology that isn't working, they’re actually the statistics of a technology being used in the wrong way.

If we rewind back to 1987, Nobel laureate economist, Robert Solow, observed something that many at the time really resonated with: “You can see the computer age everywhere but in the productivity statistics.” Computers were everywhere across the innovative businesses that had invested heavily in them, but the productivity numbers didn’t move.

The returns only materialized years later, once organizations stopped bolting computers onto old processes and started fundamentally redesigning how they worked.

Many executives, alongside Uber’s COO, are at exactly that inflection point with AI - people are using it, running out of budgets to maintain usage, and at the same time, not really seeing the productivity boost they were hoping for.

This is the Solow Paradox repeating its course, which begs the question: will enterprises learn from previous mistakes?

The flatline behind the hype

There’s a critical distinction that most enterprise leaders are still failing to make: AI activity is not the same as AI maturity. You can run 40 pilots, adopt six platforms and report impressive usage statistics, and still be no closer to measurable business value.

Uber found this out in painful, public fashion, and has since openly questioned whether the rising cost of AI token usage is translating into proportional productivity gains. What makes Uber's situation instructive is not just the financial exposure, but how the organization approached adoption. Internal leaderboards were introduced to rank teams by AI tool usage, with the incentive being to use more tools.

The outcome was more usage, but the business impact slowly became harder to justify.

This is what happens when you gamify adoption without redesigning the workflows underneath it. You optimize the tool usage metric, not the business outcome. Uber has now joined several other top organizations, including Microsoft, Meta and Amazon, in capping AI usage to tackle the issue.

What’s interesting here is that when AI token usage is unconstrained, activity becomes the proxy for progress, but when it’s capped, organizations are forced to confront a harder question: what is each token actually producing?

In that sense, token spend behaves like an economic mirror. It scales immediately with adoption, while productivity only improves when workflows are redesigned. The gap between the two is where most AI ROI disappears.

The real culprit: Individual task optimization

When AI tools are deployed at the individual level, they tend to optimize the task, not the workflow.

A developer writes code faster, a marketer drafts copy in a fraction of the time, or a data analyst generates a summary report in minutes rather than hours.

All of these examples are real gains, but if the code still sits in a review queue for four days, if the draft still passes through three rounds of manual approval, or if the report still requires someone to manually transfer it into a decision-making dashboard - the time saved will pool at the next bottleneck.

Individual productivity gains that don’t translate into workflow redesign don’t compound. They stagnate, and this is the core of the maturity gap. AI maturity isn't about how many tools you've adopted, or how many pilots you've launched. It's about whether you've moved consistently from opportunity to outcome.

That shift requires a fundamentally different way of working.

Now, the term ‘production-ready AI’ gets used loosely. It’s worth being precise about what it actually means in practice, because most enterprise AI deployments fall short of the bar.

Production-ready AI has four characteristics. First, the output feeds directly into a downstream decision or action without manual transfer. It’s embedded in the workflow, not adjacent to it.

Second, the system has clearly defined failure modes, so the organization knows exactly what happens when the AI gets something wrong, and who’s accountable for remedying it. Third, there’s a named owner responsible for performance, adoption and iteration. Finally, and most critically, the surrounding process has been redesigned - not merely augmented.

The framework that closes the gap

So, how can businesses make this shift in practice? The answer is maintaining disciplined execution.

One way to build that discipline is a structured cadence we call the 3-3-3 framework: three days to prioritize, three weeks to prove value, and three months to launch a first release. The logic is deceptively simple and deliberately structured.

In the prioritization phase, the question is not “what can AI do?”, it’s “which specific opportunity, tied to a specific business outcome, has the right combination of value, feasibility, data readiness, and organizational sponsorship to pursue right now?”

That focus alone eliminates a significant proportion of AI initiatives that consume resources without clear purpose. It’s the antidote to the open-ended experimentation that left Uber burning through its budget before April was out.

In the proof phase, the focus is validation - not in technical terms, but in commercial ones. Can this solution create measurable value for users and for the business? A proof of concept that demonstrates technical possibility without demonstrating business value isn’t a proof of concept, it’s a prototype without a destination.

In the launch phase, the solution moves into a real environment. It integrates with existing systems, gets adopted by the people it was built for and gets measured against the outcome it was designed to improve. Not against token usage and not against adoption rates.

This rhythm isn’t a rigid formula, however. The shape of the work always depends on the business problem, the data environment, the technical complexity and the organization's appetite for change. What the framework provides is momentum and the discipline to keep that momentum anchored in value.

Shifting from adoption metrics to outcome metrics

The most consequential change enterprise leaders can make right now is a measurement decision.

Enterprises that are serious about closing the gap between AI activity and business impact need to make three shifts. The first is from tool deployment to operating model redesign. Rolling out AI tools is table stakes but building a repeatable operating model - a structured path from idea to proof to scale - is the competitive differentiator.

The second shift is from adoption metrics to outcome metrics. Usage rates, logins and token volumes tell you whether people are using the tools, but they don’t tell you whether the tools are working. Define success in business terms from the outset: cost reduction, time-to-decision, revenue impact, customer satisfaction, and build your measurement framework around those outcomes.

The third shift is from centralized experimentation to distributed accountability. The AI factory model - where a central operating model connects business priorities with delivery, adoption and value measurement - works precisely because it distributes accountability across the organization. Every initiative starts with a clear owner, a clear problem and a clear definition of what success looks like.

Winning the AI day

The productivity paradox Solow identified in 1987 eventually resolved itself. Not because computers got better - though they did - but because organizations learned to reorganize work around the technology, rather than fitting the technology around old ways of working.

The same resolution is available to enterprises deploying AI today. But it requires leaders to make a deliberate choice - to stop measuring success in terms of how many tools have been adopted and start measuring it in terms of how many outcomes have been delivered.

The organizations that win the AI era will not necessarily be those with the largest number of pilots, but rather the ones that build the maturity to turn the right ideas into value and then scale that value with confidence.

That kind of maturity doesn’t happen by accident. It requires structure, discipline and the willingness to ask harder questions about what AI is actually delivering, and what it’s not.

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Thanks to rapid developments around AI, cybersecurity pros seem due for a reminder that the threat landscape has fundamentally changed. This time it's OpenClaw ringing the alarm bells, but in truth we see the same cycle repeating itself time and time again, just with new technology.

Within days of OpenClaw’s open-source, researchers discovered exposed management interfaces and malicious "skills" packages designed to trick users into installing compromised functionality.

While the legitimate security community did what it always does, innovate rapidly, attackers moved just as fast.

That reality should force organizations that haven’t already done so to rethink a dangerous assumption: the belief that if something malicious appears inside our environment, internet security products will detect it quickly enough to stop serious damage.

That assumption is unrealistic in the world of AI-driven automation and open-source agent ecosystems.

The better question is: ‘Why should unknown software be allowed to run in the first place?’

Uncontrolled AI adoption is the core issue

OpenClaw highlights the much larger issue of Shadow AI running across organizations. Employees are downloading local AI agents, experimenting with open-source models, installing community-developed skills and connecting all of these tools directly to corporate resources.

Unlike traditional SaaS applications, many of these agentic platforms execute directly on endpoints. They request filesystem access, interact with browsers, connect to cloud services and automate business workflows.

Every new skill, extension or plugin expands the attack surface. While AI usage is an important progression of technology, the problem that’s emerging is that organizations frequently have little visibility or control over which AI tools are entering their environment, let alone what they're allowed to do once they arrive.

Detection can't keep up with automated attacks

The industry continues investing enormous resources into detecting threats faster and there is absolutely value in that; but OpenClaw illustrates why detection alone cannot be the primary strategy.

By the time a detection platform identifies suspicious behavior, an AI agent may already have accessed sensitive files, authenticated them to cloud services, downloaded additional components or exposed confidential information. Instead of asking how quickly we can detect something, organizations need to first ask whether it should have been able to execute at all.

Control AI without disrupting the business

To effectively mitigate AI-driven cybersecurity threats, the industry must embrace a Zero Trust approach that moves from an allow-by-default to a deny-by-default posture.

In the context of Shadow AI, application allowlisting ensures only approved agents run inside your environment, while application containment further limits what those trusted agents are allowed to do.

This way, if an agent is compromised, any unnecessary access to files, memory, scripting engines, networking functions or other applications is blocked entirely.

Together, these controls enforce the boundaries your business already intended to have.

One of the biggest misconceptions surrounding Zero Trust is that it requires organizations to lock everything down overnight. This isn’t true.

When done correctly, application control allows organizations to understand what's already running, establish normal operating behavior and gradually enforce policies without disrupting users.

Define what good looks like

Cybersecurity has traditionally focused on identifying bad behavior, yet attackers are constantly inventing new forms of it. A more sustainable model is to define what good looks like for your organization.

If a script or application like OpenClaw is not explicitly approved inside your environment, it shouldn’t be allowed to execute.

The bottom line is that if an AI agent doesn't require access to sensitive directories, cloud resources, PowerShell or credential stores, those interactions shouldn't be possible. This deny-by-default approach dramatically reduces the opportunities available to both attackers and compromised applications.

Rather than chasing an endless stream of new threats, you're enforcing known business requirements.

The leadership lesson

Open-source innovation has enormous value and will continue driving technological progress, but every major cyber incident teaches a lesson.

The Open Claw incident shows that organizations can no longer afford environments where any new tool is free to operate with minimal oversight. Leadership teams need to stop measuring success by how quickly they respond to breaches and start measuring how effectively they've reduced the opportunity for one to occur in the first place.

Open-source AI ecosystems will continue evolving at remarkable speed, new capabilities will continue to emerge. In tandem, attackers will continue to innovate their own methods. Fortunately, your security strategy doesn't need to change every time a new threat emerges.

The principles behind hardening your environment remain the same: know what belongs in your environment, and allow only what you've explicitly approved. Restrict what trusted applications can do, and treat every request for access as untrusted until proven otherwise.

Organizations that embrace that philosophy won't just be better prepared for OpenClaw, they’ll be ready for whatever comes next.

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• Microsoft aims to cut down on "tokenmaxxing" by employees
• New guidelines will look to control AI token use to focus on ROI
• This is despite Microsoft reporting record financial results recently

Microsoft has apparently been forced to introduce limits on how much AI usage its engineers are allowed followed reports that some have been taking things to extreme.

The software giant is looking to cut back on "tokenmaxxing" within the company - where employees use far more AI tokens that may be necessary.

In an email seen by 404 Media, Microsoft warned employees that new limits on token usage would be introduced as it looked to focus on getting the most out of its AI platforms.

New guidelines

“As we accelerate our use of GitHub Copilot to deliver on our goals, we all need to be aware of how we consume tokens,” the email to employees from Jay Parikh, an executive vice president at Microsoft said.

“Tokenmaxxing is not what we are optimizing for," he continued. "I want all of us focused on maximizing outcomes that move the needle for our customers and our business.”

“As such, we are updating our internal guidance and managing token spend with the same discipline we apply to every other critical resource."

In a bid to achieve “get greater value from our token investment”, Parikh went on to say Microsoft is making access to the cheaper OpenAI GPT-5.6 model the default model for internal use.

Employees were also reminded (via a link to updated internal Copilot guidelines) that as of July 2026 Microsoft divisions have an “AI token budget target,” with employees also able to track their individual AI spending.

“While there is no target spend value being shared at this time. The data shows that many engineers spend in the range of hundreds of dollars a month to a few thousand dollars in tokens,” 404 Media reported the guidelines as saying.

Parikh noted that Microsoft does not want to impair the company’s progress towards becoming “AI-first,” and that it will keep learning and adjusting its AI policies as models and products evolve.

“We are not optimizing for fewer tokens,” he said. “We are optimizing for more impact per token."

The restrictions may come as a surprise to some Microsoft employees, given that the company recently reported yet another bumper financial quarter, and in that respect should have money to splash out on AI usage.

However it is the latest step by Microsoft as it looks to focus internal AI usage. In May 2026, it was reported the company was reportedly canceling most of the Claude Code license it uses internally, with engineers being told to use GitHub Copilot CLI, with users given a June 30 2026 deadline to remove Claude Code from their workflows.

However tokenmaxxing has proven to be an issue at other tech giants - perhaps most notoriously at Uber, which was forced to admit it had exhausted its entire annual 2026 AI coding token budget in just four months due to massive employee adoption of agentic tools like Anthropic PBC's Claude Code and Cursor.

Amazon also recently revealed it had spent $1.8 million on an internal Claude Sonnet deployment intended for matching author details with product listings, after it ballooned far beyond its planned budget and was ultimately given menial tasks to do.

Quantum risk has moved from theoretical to operational in 2026, and this shift is now impossible for enterprises to ignore. Google has committed to completing its post-quantum migration by 2029, the NCSC 2035, and the G7 says 2034. NIST has finalized its first full suite of post‑quantum cryptographic standards, triggering mandatory migration planning across regulated industries.

The timelines might not be perfectly aligned, but the message they send is: the risk is real, and the time to act is now. Breakthroughs in quantum hardware and AI‑accelerated quantum optimization are bringing “Q‑Day”ever closer. This is elevating quantum‑safe visibility, cryptographic discovery, and encrypted‑traffic intelligence to a now‑priority for every enterprise.

This is not the first time the industry has faced a widely anticipated but imperfectly understood threat. The parallels between quantum computing and Y2K are difficult to ignore. What began as stories of a supermarket system rejecting food as 80 years out of date, or a 104-year-old being invited to school because a computer registered her as four, soon evolved into fear as the scale of the problem became clear.

By 1995 the New York Stock Exchange had spent over $30 million remediating its systems. Y2k evolved into a defining moment for risk management and the key lesson was that organizations must understand and respond to exposure quickly and decisively.

Successfully addressing the threat quantum computing poses to encryption demands the same mindset, although this time applied across a far more complex and interconnected digital landscape.

The invisible threat already underway

A key distinction between Y2K and quantum computing is that the latter is not anchored to a single moment in time. The phrase “harvest now, decrypt later” describes the practice of adversaries collecting encrypted data today with the expectation that quantum capabilities will allow them to decrypt it in the future.

The implications of this tactic are significant, with 87 percent of organizations expressing concern about such scenarios as quantum computing advances.

Financial records, personal data, and intellectual property retain their value long past creation and the information encrypted today may still be sensitive well into the future. Decisions made now about cryptographic resilience will directly shape an organization's future security and reputation.

Why PQC is fundamentally more complex than Y2K

At its core, the Y2K challenge was a remediation problem with a relatively well-defined scope. Migrating to post-quantum cryptography is fundamentally different.

Cryptographic controls are deeply embedded across modern digital infrastructure, underpinning applications, APIs, cloud services, IoT devices, operational technology, and a growing web of third-party integrations. In many cases, they operate invisibly and are poorly documented. Organizations are not simply upgrading known systems, but first discovering what encryption levels have been used and where.

Addressing this means building a comprehensive inventory of cryptographic assets. All weak cipher suites, expired certificates, and non-compliant encryption methods must be identified.

Once found, organizations should standardize on stronger protocols such as Transport Layer Security (TLS) 1.3. This faster, more streamlined and longer protocol is ultimately more secure than older versions like TLS 1.1 and TLS 1.2, which will be broken by quantum computers in a matter of hours, minutes, or even seconds.

You cannot secure what you cannot see

In addressing both Y2K and today’s cybersecurity challenges, one principle consistently determines success: visibility. As organizations plan for a post-quantum future, 91 percent report that visibility into encrypted traffic is critical for PQC readiness.

Network-derived telemetry provides a scalable way to gain this. By analyzing traffic flows and metadata, organizations can build a comprehensive picture of cryptographic usage across both managed and unmanaged assets. This outside-in perspective complements internal inventories and helps uncover dependencies that might otherwise remain hidden.

With improved visibility comes the ability to assess risk more accurately, prioritize remediation, and ensure that the adoption of quantum-resistant approaches does not introduce unintended vulnerabilities.

Avoiding a repeat of history

The response to Y2K ultimately succeeded because organizations acknowledged the threat and took action. It was a forcing function that led to massive technology infrastructure upgrades and tech stack modernization.

Like Y2K, today’s quantum challenge isn’t just the potential event itself, it’s the scale of the remediation effort required across systems, applications, and embedded technologies, which makes early action critical.

The transition to post-quantum cryptography will not be achieved overnight. It will require a coordinated effort across security, infrastructure, development, and compliance functions, alongside close collaboration with vendors and strategic partners.

More importantly, it requires a shift in how the challenge is framed. Organizations that take proactive steps now by establishing a comprehensive inventory of cryptographic assets, improving visibility across their environments, and developing structured transition plans will be far better positioned to navigate the shift.

They will retain control over their timelines and reduce the likelihood of disruptive, last-minute change.

Those that delay may find themselves in a position that feels uncomfortably familiar. A known problem with a shrinking window in which to respond.

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We’ve been sold a comforting idea about artificial intelligence: that it’s making us dramatically more productive. Faster outputs, smarter tools, less effort. A quiet revolution in how we work.

But step back for a moment and ask yourself a simple question.

Do you actually feel more productive? Not faster. Not busier. Productive.

Because for most professionals I speak to, the answer is no.

Work feels quicker, yes. But also more fragmented, more reactive, and oddly more exhausting. The promise of efficiency is there on paper, but the true experience tells a different story.

That disconnect is worth paying attention to.

A typical working day

Look at how most of us spend a typical working day. We move between email, calendar, tasks, notes, messaging platforms, documents. Each tool holds a piece of the puzzle, none of them are the full picture. So, we become the system that stitches it together.

We check an email, then jump to our calendar to understand the context. We open a task list, then search our notes to remember why that task exists. We respond to a message, then dig through previous threads to find what was agreed.

This is not the work itself. It’s the management of work.

Now add AI into the mix.

We have tools that can summarize emails, draft responses, transcribe meetings, generate notes, and even suggest tasks. Each of these capabilities is impressive in isolation. They save minutes here, seconds there.

But they don’t remove the fundamental problem. In many cases, they amplify it.

Instead of switching between tools, we now switch between tools and their respective AI layers. An assistant in your inbox. Another in your document editor. Another in your meeting tool. Each one helpful, but none aware of the others.

So, we’re still managing everything ourselves. We’re just doing it faster.

This is where the narrative around AI productivity starts to unravel.

Defining success

We’ve defined success as speed. How quickly can a tool help you write, summarize, respond, or organize? And to be fair, AI has delivered on that front.

But speed without context is a blunt instrument.

If you’re responding faster but to the wrong priorities, you’re not more productive. If you’re generating more output but not moving meaningful work forward, you’re simply accelerating noise.

The real friction in modern work isn’t the execution of tasks. It’s the constant need to decide what matters, to reconstruct context, to align fragmented information across multiple systems.

AI, as it stands today, rarely addresses that layer.

At warpSpeed, we’ve approached this from a slightly different angle.

We didn’t start by asking how to make tasks faster. We started by asking why work feels so disjointed in the first place.

The answer was fairly obvious: everything is scattered. Email lives in one place, calendar in another, tasks somewhere else, notes somewhere else again. Every decision requires jumping between them.

So, we focused on bringing those elements together into a single, connected environment. a system where context flows naturally between them, facilitated by AI.

Not as a feature bolted onto individual tools, but as something that can see across them. Something that understands not just a single email or a single note, but the relationship between your communications, your commitments, and your priorities.

The difference, while subtle, is meaningful. This is how I like to think a successful assistant would function.

For example, when someone asks, “What should I focus on today?”, the answer isn’t generated in isolation. It draws on overdue tasks, unread emails that require responses, upcoming meetings, and previous commitments. It reflects the reality of that person’s day.

Small changes

Similarly, we’ve seen how small changes in interaction design can shift behavior. One example is email. By rethinking how users move through their inbox, we’ve seen people process large volumes of emails in a fraction of the time they previously spent. Not because they’re working harder, but because the system reduces friction and surfaces what matters.

These are not dramatic, headline-grabbing transformations. They’re incremental improvements grounded in real workflows. And importantly, they’re imperfect. We’re still learning, still refining, still discovering where the real value lies.

But they point to something broader.

If AI is to genuinely deliver on its promise of productivity, we need to rethink what we’re asking it to do.

Right now, most tools are designed to assist with tasks. Write this. Summarize that. Suggest a response. Create a list.

What’s missing is a deeper understanding of context and personalization.

Who is this for? Why does it matter? What else is happening around it? What should take priority?

Without that layer, AI remains reactive. It responds to prompts, but it doesn’t help you navigate your day.

Moving forward

To move forward, the industry needs to shift in three ways.

First, from isolated tools to connected systems. The value of AI increases exponentially when it can operate across your entire workflow, not just within a single tool.

Second, from generic intelligence to personal context. The most useful AI will be shaped by how you work, what you care about, and how you make decisions.

Third, from output to outcome. It’s not enough to generate content or complete tasks. The goal should be to move work forward in a meaningful way.

None of this is easy. It requires rethinking product design, data architecture, and user experience at a fundamental level. It also requires a degree of restraint. Not every problem needs another feature. Sometimes it needs fewer moving parts.

Productivity at scale

The irony is that the more powerful AI becomes, the more important simplicity becomes. Productivity at scale depends on removing the need to think through complexity, making intuition more valuable than ever.

Because ultimately, productivity isn’t about doing more things. It’s about doing the right things with less friction.

AI isn’t broken.

But the way we’re using it might be.

If we continue to layer intelligence on top of fragmented systems, we’ll keep getting the same result: faster work, but not better work.

The real opportunity lies in something quieter, but far more impactful. Using AI to remove the need to manage work in the first place.

Not to help you keep up.

But to help you stay focused on what actually matters.

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Mention high-frequency semiconductors, and the conversation often begins with gallium arsenide (GaAs). For decades, GaAs has been the material of choice for high-performance radio frequency (RF) devices that enable a wide range of applications, including satellite communications, radar, and mobile networks.

It is a mature, well-understood technology, and large, high-quality wafers can be grown economically, enabling consistent device performance and scalable manufacturing.

Yet a transition is underway. Increasingly, the industry is turning away from GaAs and towards gallium nitride (GaN), a material that offers clear performance advantages, particularly at higher frequencies, higher power levels, and more demanding operating conditions.

The promise of GaN is well established. The challenge lies in making it reliable, scalable, and commercially competitive.

And, as with many emerging semiconductor technologies, meeting that challenge is as much about measurement as it is about materials.

A material with advantages, and constraints

GaN enables the development of devices that can operate at higher voltages, higher temperatures, and greater power densities than their GaAs counterparts. This makes it particularly attractive for next-generation RF systems, including 5G and 6G communications, defense applications, and space technologies – where extreme environments are commonplace.

In principle, GaN can offer a straightforward upgrade path. It is sometimes thought of as a functional replacement for GaAs to deliver improved performance. Yet, in practice, the situation is more complicated, and a whole-system approach is required to redesign a module. Furthermore, the manufacturing approach for GaN is fundamentally different.

The hidden cost of heteroepitaxy

Unlike GaAs, which can be grown as large, high-quality wafers using established methods, GaN presents a fundamental manufacturing challenge because producing large, defect-free GaN substrates remains difficult and expensive.

As a result, most GaN devices are produced using a process called heteroepitaxy, whereby a thin layer of GaN is grown on top of a different substrate, typically silicon or silicon carbide. This approach allows manufacturers to leverage existing wafer technologies. But it comes at a cost.

When GaN is grown on a dissimilar substrate, differences in lattice structure and thermal expansion introduce defects into the material. These defects can affect everything from electrical performance to long-term reliability.

This results in a difficult trade-off: GaN offers superior theoretical performance, but achieving that performance consistently across wafers and devices is far more challenging than with GaAs. The advantage of using GaN therefore increasingly depends on material quality, and on the ability to control and understand it.

That relies on measurement.

Measurement as a competitive tool

For companies developing GaN technologies, metrology plays three distinct and essential roles.

The first is in process development. Growing GaN through heteroepitaxy involves carefully balancing multiple parameters, including temperature, deposition rates, and substrate preparation. Small changes can improve or degrade material quality. Without reliable measurement, it is difficult to know whether a process adjustment has made the material better or worse. Metrology provides the feedback needed to refine growth techniques and reduce defect densities.

The second role is in demonstrating material quality. In a market where performance depends heavily on the underlying material, manufacturers must be able to show that their GaN is superior to that of competitors. This requires measurement methods that are not only accurate, but also comparable across organizations. Customers need confidence that a claim about material quality means the same thing, regardless of where it is measured.

The third role is in device performance validation. Ultimately, customers care about how a device behaves in real applications. For RF components, this includes metrics such as power output, efficiency, frequency response, and thermal stability. Linking these device-level characteristics back to material quality is essential. It allows manufacturers to demonstrate that improvements in material growth translate into tangible performance gains.

Across all three areas, measurement is not simply a supporting activity. It is a central part of how competitive advantage is created and communicated.

From materials to systems

The challenges associated with GaN are part of a broader shift in the semiconductor industry.

As devices become more specialized and operate under more demanding conditions, performance is increasingly determined by subtle interactions between materials, structures, and processes.

This is particularly true in RF systems, where small imperfections can have outsized effects on signal integrity and efficiency.

It also connects to a wider trend seen in other areas of semiconductor technology, including photonics. There, heterogeneous integration is bringing together different materials and device types within a single system, creating similar measurement challenges.

In both cases, success depends on the ability to understand and control complexity at multiple scales.

A strategic inflection point

For the RF semiconductor industry, the transition from GaAs to GaN represents more than a simple material substitution.

It marks a shift towards technologies where performance is less constrained by established manufacturing processes, and more dependent on how well new materials can be engineered and characterized. This creates an opportunity.

Countries with strong capabilities in materials science, process development, and measurement can play a defining role in shaping how GaN technologies evolve and are applied successfully in the semiconductor industry.

The ability to measure material quality, correlate it with device performance, and establish trusted benchmarks will influence how quickly GaN is adopted across global markets. This means the future of high-frequency RF semiconductors will not be determined by materials alone.

GaN may offer superior intrinsic properties, but those advantages must be realized in practice. That requires consistent, high-quality material growth, reliable device fabrication, and credible performance validation – all of which depend on effective measurement and standards. This is why metrology will become increasingly central to RF semiconductor innovation, not as a downstream check, but as an integral part of development, manufacturing, and market adoption.

In high-frequency semiconductors, as in photonics, the ability to measure well is no longer just a technical requirement. It is becoming a defining feature of competitiveness.

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Prompt engineering was briefly the face of the AI jobs boom.

In 2025, technology recruitment firm SPG Resourcing reported that UK job listings for AI prompt engineers had grown by 180% over the previous year.

It was an eye-catching figure that captured the mood of the first commercial wave of generative AI. Businesses were trying to understand how to talk to LLMs and turn early experiments into something useful.

That requirement has not disappeared.

With job site postings for specialist AI roles in the UK rising by 61% from last year according to PWC, it’s clear that good prompts still matter. But most of that new demand is for people who can apply AI inside a business, not just talk to a model.

Many organizations have moved past the first demo. They are now trying to build AI agents, retrieval-augmented generation (RAG) systems, and AI-enabled workflows that operate inside the business.

That creates a different skills gap.

This is the shift behind what I refer to as ‘context engineering’. Although that term is not universally used, the capability is becoming essential.

Companies that want useful AI agents and retrieval-augmented generation systems need people who can design the environment around the model, not just the prompt sent to it.

From better prompts to better context

Prompt engineering is about the instruction, while context engineering is about the world around that instruction.

A support agent does not only need a well-written prompt, it needs the right customer record, policy, product history, and permission boundary. Similarly, a developer agent needs the relevant code, tests, dependencies, and deployment constraints.

In both cases, output quality depends on context. Without it, the model is guessing from incomplete evidence. With too much of it, the system becomes noisy and difficult to govern. The job is to make context useful, current, and controlled.

That’s what makes context engineering distinct from prompt engineering.

Agents raise the stakes

The rise of AI agents makes this more urgent. A chatbot with poor context may give a weak answer, but that same poor context may result in another agent making a serious error.

Once an AI system can call tools, query business systems, maintain state, and act across several steps, context becomes an essential part of the production architecture. It decides what the agent can see, what it can do, and how much confidence the business can place in the outcome.

I’m reminded of a joke about boundary testing. A developer walks into a bar and orders a beer, then he orders five beers, then he orders 999,999,999,999 beers, then he orders -1 beer. The bartender blinks, but everything is okay. A user walks into the bar, asks where the bathroom is and the whole bar explodes.

The same principle applies to AI projects. The first prototype may work against a narrow set of examples, then become fragile when it meets real data. Customer information sits in one system, operational data in another, and important knowledge in documents and files. The agent is expected to reason across all of it, but the context layer has not been designed for that job.

A financial services team, for example, may need to connect CRM data, an existing data platform, and internal documents before an agent can answer accurately. The hard work is not only moving the data. It is shaping it so the agent can retrieve the right evidence and stay inside the right permission boundary.

The job title is still catching up

This creates an awkward hiring moment. The need for context engineering is becoming clearer, but the job title is still unsettled.

Some organizations may seek to specifically hire ‘context engineers’, but many will not. The capability is more likely to appear inside roles such as AI engineer, agent engineer, AI platform engineer, applied AI engineer, or data engineer. In other businesses, it will be a team responsibility shared across data, platform, security, and software engineering.

Leaders therefore need to hire for the work, not the label. A candidate does not need to have ‘context engineer’ on their CV to be useful. The better signal is whether they understand how data moves through systems, how permissions are enforced, and how a prototype becomes something reliable enough for production.

This also means the talent pool is wider than many companies assume. Machine learning expertise is valuable, but context engineering draws heavily on existing engineering disciplines. Data engineers understand pipelines and retrieval.

Platform engineers understand operational resilience. Security teams understand access control and auditability. Software engineers understand how to turn messy requirements into maintainable systems.

The best candidates may look like full-stack AI engineers. They do not need to be specialists in every model, database, or framework, but do need enough range to connect the model layer with the business systems around it.

The Kubernetes lesson and what leaders should do now

The shift has a parallel with the move to cloud-native architecture and Kubernetes. Many companies treated Kubernetes as something to install, then discovered that the harder work was changing how teams built and ran software.

AI creates a similar risk. Companies can buy tools and hire a handful of specialists, but still fail to change the engineering habits around them. Context engineering requires teams to think differently about everything from documentation, and data ownership, to access, testing, and accountability.

It also changes the culture of software development. Engineers are already using AI to write, review, and iterate code. That can improve productivity, but it does not remove responsibility. In areas where performance, reliability, or security matter, human judgement becomes even more important.

CTOs and CIOs should not wait for context engineering to become a mature hiring category. They should start identifying the capability now.

The first step is to examine where AI projects are failing. Is the model genuinely weak, or is the system retrieving poor context? Are permissions clear? Can the team explain why the agent produced a particular answer?

The second step is to build cross-functional teams. AI cannot sit apart from data, platform, security, and product. In many cases, the best approach will be to upskill existing engineers who already understand the organization's systems.

The final step is cultural. Engineers need to become fluent in AI-assisted development while staying accountable for the systems they ship. Leaders need to make room for experimentation, but they also need clear standards for review, evaluation, and governance.

The model is not enough

AI hiring is changing because AI itself is moving into production. Models will continue to improve, and businesses will have many ways to access them. The harder advantage will come from knowing how to connect those models to the right business context.

Companies that understand this will build agents and RAG systems that are more useful, safer, and easier to govern. Companies that ignore it will keep blaming the model when the real weakness is the environment it has to work in.

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There has never been a construction race like the one now under way in the data center industry.

AI's demand for compute is driving the largest and fastest infrastructure buildout the sector has ever seen, with hyperscalers and developers racing to bring capacity online faster than power grids, planning departments or supply chains can comfortably keep up.

In that scramble, enormous attention goes to the things visible on a spreadsheet: megawatts, cooling, chips, network and, rightly, cybersecurity.

The dimension that gets quietly deprioritized is the physical protection of the buildings themselves. It is the easiest thing to defer under deadline pressure, and the hardest to retrofit once the concrete is poured.

A regulatory change in the United States is about to make that blind spot worse, and it is worth understanding even if you never operate a US federal facility, because of what it signals.

On 30 September, the Federal Data Center Enhancement Act is due to expire, with no replacement waiting. It set minimum standards for federal data centers, including, unusually, protection against physical intrusion, and it was the operational mandate that forced data-center-specific assessment.

Broader frameworks such as FISMA and the NIST control catalogue still apply, but they provide the principle; the Enhancement Act provided the practice. Principles without a mechanism to enforce them tend to be interpreted generously.

And when the government's own floor is allowed to disappear, the benchmark private operators quietly measure themselves against tends to go with it.

Security baselines

This is not a hypothetical worry about whether the requirement comes back. The Act's predecessor lapsed in 2022 and only survived by being folded into the following year's defense bill. A rule that needs a legislative vehicle to return is one that can quietly fail to, and security baselines rarely erode through a single dramatic decision. They erode through the absence of one: a mandate that simply never gets renewed because nothing forces the issue.

It helps to be concrete about what is at stake, because physical security is not an abstraction. It is the contractor with unescorted access to a hall of servers; the unmonitored loading bay; the maintenance door propped open for convenience; the departed employee whose credential still opens the cage. These are the routes by which data is stolen, infrastructure is sabotaged, and a facility the size of a warehouse is taken offline.

The real exposure is not in the data centers we already have. Established operators keep that spending in place through existing contracts and their own risk appetite. It is in the new builds, the AI-era expansions specced and procured at extraordinary speed, most of them private, built by developers no mandate ever bound. Remove the assessment framework and physical security becomes something that can be scoped down in procurement to hit a budget or a timeline, with no compliance flag and no one formally alerted. The gap opens in the facilities we are racing to build.

There is a contradiction at the center of this. Governments increasingly classify data centers as critical national infrastructure, the UK now does, and rightly so. Reducing their security baseline at the same moment runs in two directions at once. You cannot call something critical and simultaneously make its protection optional.

A sensible fix

The fix is not simply more regulation, though a sensible renewal would help. It is to stop treating physical security as a compliance obligation that rises and falls with the statute book, and start treating it as core design.

The consistent lesson from securing large-scale critical facilities is that physical security fails when it is a collection of disconnected tools, a camera here, an access reader there, bolted on at the end of a project.

It works when it is designed from the start as one integrated system, where access control, video, identity and alarms inform each other and an anomaly anywhere triggers a coordinated response.

Treating the physical and the digital as separate problems is part of how the gap forms in the first place. In a modern data center they are the same problem: a propped door, a cloned badge or a rogue contractor is a cyber incident waiting to happen, and a facility that cannot correlate a door event with an access log or a camera feed will always be reacting after the fact rather than stopping an intrusion in progress.

For operators, the practical implication is simple: the physical security of an AI data center should be specified at the same moment as its power and cooling, not bolted on once the shell is up. Retrofitting protection into a live, fully-loaded facility is far harder, and far costlier, than designing it in.

The AI buildout is a genuine engineering achievement, and the energy and compute challenges are real. But the industry is optimizing hard for the risks it can measure and deferring the one it finds inconvenient. A statute lapsing in Washington should not be what decides whether the buildings holding the world's most critical compute are properly protected.

For IT infrastructure we have all agreed is critical, getting its physical protection right should be a given, not something we quietly leave to whoever is under the most deadline pressure.

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Texas Governor Greg Abbott has paused new data center connections to the state’s grid pending audits on the requirements for new and proposed constructions. The audits will cover the revenue lost from data center tax exemptions, power usage and generation, infrastructure upgrades, water usage, and the impact on the local communities and environment.

Texans have seen their electricity and water bills increase significantly over the past five years. Data from Electric Choice shows the average residential electricity price has risen by over 25%, and data from the Texas Municipal League demonstrates the average residential water bill has risen by over 26%.

But the prices in Texas sit below the national average. So why then are they rising so fast, and what does it mean for other states in the US?

Prices rapidly rising

As new industries and houses are constructed, new connections need to be added. These connections are constructed by power grid operators, with the cost of upgrades generally passed on to consumers through utility transmission fees.

Infrastructural upgrades are also paid for by grid operators, with the costs of construction again passed back down to consumers through their bills.

The rapid increase in data center construction projects has been a particular issue in this regard. As data centers require enormous amounts of electricity, they require far more complex and expensive connections than are required of a typical residential connection.

But alongside the moratorium on new data center construction, Gov. Abbott has required data center projects that pass the states’ audit will be required to pay for their own connections and infrastructure. In a letter to the Public Utility Commission of Texas and the Electric Reliability Council of Texas, Abbot highlighted that 90% of new grid connections are data centers.

Electricity prices are also rising due to an increasingly unpredictable climate. Soaring temperatures place increased demand on electricity grids, especially as cooling systems for homes and businesses are switched on during peak times. The heat also causes electricity infrastructure to fail at a higher rate, causing additional costs as grids are repaired and replaced.

As for water prices, Texas has been subject to a four year drought. Per Washington Post reporting, Texas has been seeking alternative sources of water to extend supply. The downside is that in one such project, the state ended up paying the contractor responsible for a new groundwater collection project an extra 40% on top of the originally proposed $500 million.

The increasingly unpredictable weather is also wreaking havoc on existing infrastructure, especially when there are short periods of torrential rainfall. Sudden downpours can quickly overwhelm water infrastructure, damaging it or washing it away. Water treatment plants can also become overwhelmed and damaged. The cost of repairing and replacing infrastructure, as well as funding the much needed projects to create new water sources, once again gets passed down to consumers.

Couple this with the rapid increase in data center projects, which requires water usage during construction and for cooling, and prices will further increase. Texas currently sits in the number two spot for active data centers, slightly behind Virginia, but the number of planned and approved projects for Texas means the Lone Star state could soon become the number one state for active data centers.

Lessons for the rest of the US

As with all new technology, the pace of regulation falls a few thousand steps behind.

Numerous states have already begun issuing moratoriums on tax exemptions for data centers as the actual loss of revenue far exceeds earlier predictions. In Ohio, the loss of revenue has been $1.6 billion - far higher than the projected $136 million.

The new regulations introduced in Texas requiring data centers to pass the state audit before being hooked up to the grid have presented a second problem. Data centers providing their own on-site power are not subject to new grid connections, and can therefore bypass the audit.

One of the most cost effective means for producing on-site power are gas burning turbine generators. The downside for local residents in the vicinity of these sites is that noise levels have been compared to living near an international airport, and there are under-studied phenomena surrounding the infra-sound produced by these power projects.

There are also the climate implications of these sites. Despite the name, natural gas is a highly polluting fuel source that distributes pollutants both into the local area and the atmosphere, causing damage to local livestock, fauna, and the rest of the world.

• Intel shared rare x86 chip design access with a brand-new startup
• RosaicLabs can now build custom processors using licensed Atom architecture
• Intel shifted its low-power strategy from Atom towards newer E-Core designs

Intel has given RosaicLabs access to part of its Atom processor technology in a rare move involving the company's x86 intellectual property.

Reports from Reuters claim the agreement involves register-transfer level, or RTL, code that describes processor functions before they are converted into physical silicon during manufacturing.

Atom was designed for low-power computing, making the technology suitable for systems requiring efficient performance without high energy consumption or excessive heat generation.

Why Atom technology fits the reported agreement

Intel's Atom processors have historically powered entry-level systems, networking equipment, embedded devices, and other products where power efficiency matters more than maximum performance.

The processor family runs on the x86 architecture and was developed to provide Intel-based computing for smaller and lower-power devices.

RosaicLabs is expected to receive RTL code for Atom technology, which could allow it to create custom processor designs using Intel's existing architecture.

A veteran semiconductor executive said such access provides "comprehensive exposure" to Intel's intellectual property, giving customers the ability to develop their own silicon based on licensed designs.

Providing Rosaic access to Atom technology is unusual because Intel has traditionally kept key parts of its x86 intellectual property under tighter control rather than widely licensing processor designs.

However, Intel's Atom portfolio has become smaller as the company shifted its low-power processor strategy toward E-Cores, which succeeded Atom-based designs in newer products.

E-Cores use newer microarchitectures such as Gracemont, meaning Intel may view selected Atom technology as suitable for external licensing while keeping its latest processor designs within its own roadmap.

The exact Atom technology Rosaic will receive remains unclear, although the startup could potentially gain access to Tremont, one of Intel's final Atom microarchitectures.

Tremont was used in products such as Elkhart Lake and Jasper Lake, which supported embedded systems, low-power laptops, and Chromebooks.

Rosaic has some connections with Intel

Rosaic is a startup incorporated in Delaware in May 2026, but details about the company remain limited beyond its reported agreement with Intel over Atom processor technology.

An amended corporate filing dated July 24 shows the company can pursue a $10 million seed funding round while approving capital expenditures below $5 million without investor or board approval.

The startup's founding team reportedly includes former Rivos employees, linking Rosaic with a semiconductor company that Meta acquired for roughly $2 billion last year.

Rosaic's connection with Intel also extends to its leadership because chief executive Amarjit Gill previously helped assemble Rivos' founding team alongside Intel chief executive Lip-Bu Tan, who chaired that company.

Gill and Tan also shared an investment history, making early bets on Nuvia before Qualcomm acquired the startup, adding another link between Rosaic's leadership and Intel's current chief executive.

These professional relationships have drawn attention to Rosaic, although neither company has publicly explained whether they influenced Intel's decision to share Atom processor technology.

After the announcement, Intel shares rose 12%, although no evidence directly linked those market gains with the reported licensing arrangement.

This agreement suggests Intel may see continued value in Atom's low-power x86 design, but its broader intentions remain uncertain until Rosaic reveals its future processor plans.

• Phishing campaign used fake Teams notifications to route victims to a genuine Microsoft sign-in page
• Rather than stealing passwords, attackers asked victims to approve permissions for an attacker-controlled app, gaining access to mail, files, Teams, SharePoint, OneDrive and calendars without defeating MFA
• Check Point says the technique has been commoditized in 2026 into a rentable service; the practical defense is restricting app consent rather than relying on users to spot a fake

A phishing campaign that ran from late June into July 2026 did something that breaks most of the advice organizations have spent a decade teaching their staff: it sent victims to a real Microsoft login page.

Check Point's email research team, which disclosed the campaign, identified more than 200 phishing emails targeting users across roughly 120 organizations worldwide.

The lure was a fake Microsoft Teams notification with a genuine destination; what actually compromised accounts was not a stolen password but a permissions prompt that the victim clicked through voluntarily.

A sophisticated attack that relied on tricking users into granting permissions

Check Point noted in its brief that what actually compromised the accounts was not a stolen password but a permissions prompt that the victim clicked through voluntarily. This is a reminder of a stark change in attackers' tactics: they have stopped forging Microsoft's front door and started walking through it.

The email appeared to be a Microsoft Planner task-assignment notification. The sender name read "There's New Activity On Team," the subject line claimed that HR had sent three messages via Teams chat, and the body referenced a payroll and benefits update, along with a counter showing four overdue employee tasks.

To someone who works in security, the message had its own telltale signs of being a typical phishing attempt: every link in the email, including both call-to-action buttons, routed through the same redirect. And the visible sender address belonged to the recipient's own organization, meaning the email appeared to have been sent to the same person it came from.

The link opened a real OAuth authorization URL on login.microsoftonline.com, not a look-alike domain. Signing in displayed a permissions prompt asking the user to approve the permissions or accept them on behalf of their organization.

If they did, Microsoft redirected the browser to the redirect address specified in the original request, which in this particular campaign was an AWS API Gateway endpoint under the attackers' control. The authorization code was delivered there, and the attackers exchanged it for access. No password was stolen at any point, and there was no fake page to spot.

This is not unlike how the phishing-as-a-service Kali365 platform compromises Microsoft accounts, but instead of stealing session cookies or OAuth tokens, it opts for a more permanent illicit grant of consent.

This runs counter to the usual security training checklist, which emphasizes adhering to norms rather than going against the grain; users are told to check the URL, look for the padlock, and watch for misspelled domains. None of those measures matter because there is nothing forged to catch. The domain and certificate are Microsoft's, while the sign-in page is the one the user sees every morning, offering a false sense of security to a user not looking for this particular attack vector.

Multi-factor authentication does not help either; it protects the login sequence but not access to the user's data post-login. The attacker never needs the password or the second factor because they walk away with a token granted by the user's valid session, a technique called 'consent phishing'.

There are many ways to prevent this, but the simplest two are asking users to check every single permission/consent screen they click (the phishing attempt still requires users to allow it) and limiting access to permissions for user accounts that applications can request via Microsoft Entra at the system administrator level.

It would be prudent to do the latter at a minimum, even as Check Point notes that the campaign is no longer active because the underlying technique it used is not going anywhere.

• New study puts average cybercrime victim losses at $9,468, making a global annual toll at just over $1.24 trillion across 130.9 million victims
• Cybercrime is still massively underreported by both victims and authorities, so official numbers might be 'softer' than the underlying problem
• Some of the countries published limited, if any, financial data regarding cybercrime, making losses an estimate at best

New figures have claimed cybercrime victims lose $9,468 in the average incident, showing the scale of an increasingly global problem.

The report from Comparitech claims 130.9 million people are hit each year, and that the annual global toll comes to just over $1.24 trillion.

A December 2023 study from the company put those figures at $8,069 per victim, 88.5 million people and $714 billion in total losses respectively, highlighting a growing trend that sees a mix of illegal activity moving the needle further.

A growing problem with regional caveats

The United States remains a favorite for cybercriminals, topping the charts with 6.7 million victims losing $138.9 billion, a per-victim loss amount of ~$20,731, more than twice that of the global average.

This is in stark contrast with the next four countries on the list (Spain, France, Sweden and Turkey), all of which offered an average of approximately $10,000 per victim.

Russia, coming in 6th, reports a much larger number of victims than the four countries ahead of it, but offers a much lower per-victim loss estimate of $3,659. Interestingly, the total number of cybercrimes committed in 2025 dropped to 663,000 from 775,000 in 2024, despite an ongoing conflict with Ukraine, which often sees cyberattacks at both the industrial and localized levels by both parties.

However, a weakening economic situation, as well as Russia localizing many of its communication applications and restricting banking, might also mean that Russia's figures also remain inadvertently capped by policy decisions the country has taken.

With 18.8 million victims, India has the highest number of scam victims worldwide, even though its pro-rata number is considerably lower than the mean at ~$835 per victim, which may be attributable to the country's lower GDP per capita.

Interestingly, China, with 1.2 million victims, a fraction of its neighbor, managed to lose approximately $11.5 billion, a pro-rata number of approximately $9583, in line with global estimates by Comparitech and possibly fueled by the country's heavy-handed approach to cybercriminals, which saw it apply increasing amounts of pressure on neighboring Myanmar that culminated in it convicting and executing scammers arrested across the border.

Comparitech's $1.24 trillion figure is conservative, and the study acknowledges this, noting that it covers only victim losses. It notes that experts anticipated the global cost reaching $10.5 trillion in 2025 and calls its own $1.24 trillion a drop in the ocean by comparison.

That $10.5 trillion comes from Cybersecurity Ventures' 2016 report, which estimated $3 trillion for 2015 and projected it forward at an assumed 15 percent compound annual growth rate, describing the result as the greatest transfer of economic wealth in history.

At a time when AI automation offers better security, often allowing users to screen calls or leverage security applications that adapt on a case-by-case basis, the inverse is also true with hackers and cybercriminals considerably upping their game when it comes to bypassing security altogether; Comparitech's figures provide a sobering reality: if the industry estimates it has hold and are compared to the GDP of entire countries, the firm says it would rank 20th in the world in those terms alone.

With AI advancing in capability and availability, many scientists and experts have begun highlighting the risks of deploying this emerging technology. There's an overriding desire to deploy AI ethically, or to instill models with human-based values, but it remains to be seen how to standardize such an approach or even whether to regulate the industry. That's where organizations like the Stanford Institute for Human-Centered AI (HAI) come in.

Computer says no

When interviewed for the journal Issues in Science and Technology, the so-called 'Godmother of AI' Fei-Fei Li tried to explain how scientists and engineers can build AI that truly adheres to our values.

Her thesis around the responsible use of AI centers not on anthropomorphizing AI or considering it an intelligence capable of navigating the world and arriving at its own independent conclusions. Rather, it will reflect the values that we instill into it, whether that's through training or continued use of these tools.

She added in this interview that it's also about recognizing that AI, as any tool, can both empower and harm humans.

Values-driven AI

The organization that the American computer scientist leads, HAI, is an interdisciplinary research center that focuses on how best to guide AI development so that it benefits humanity and doesn't incur or lead to harm.

In recent years, there's also been an increased emphasis on whether to regulate AI nationally (or even globally) and what shape that might take. Doing so, however, is far easier said than done, with progress occurring at different paces, in different guises, and inconsistently across oceans.

There are, for example, different values that different nations or groups of people may wish to instill into the AI models they build that clash with the values of others.

Beelink EQi Wildcat Lake: 30-second review

The Beelink EQi is essentially an ideal office machine. There’s nothing fancy about the design; in fact, it feels a little lightweight and plasticy; however, the balance of features is far greater than the specifications would initially have you believe.

As ever, this Mini PC is compact with its 126mm-square footprint that enables easy positioning around the desk; however, while the looks may not highlight anything special, it packs in an unusual amount of connectivity for a Mini PC at this price and with this level of CPU.

The major surprise here is that on the back, alongside the usual 2.5GbE LAN port, is a second, ultra-fast 10GbE port, which is the sort of specification you normally find on hardware costing far more, but also ideal if you’re connecting into a UGREEN IDX6011 NAS over a 10GbE network for archiving. Next to this network port are two Thunderbolt 4 ports at 40 Gbps, HDMI 2.0, and triple-display support, meaning that for integration into an office or studio this machine is highly tuned.

It’s also very neat with the dark grey ABS enclosure feeling cheap compared to aluminium rivals. Still, the 85W power supply is built into the chassis, so the only cable at the back is a mains lead, and those going to monitors and accessories, there’s no power brick to get in the way.

Open the base and the interior is exceptionally well organised, with both M.2 slots covered by a substantial metal heatsink that lifts off with two screws. The rear port layout is the odd decision, with the two Type-A ports placed on either side of the HDMI output rather than together, which is awkward if you use a wired keyboard and mouse.

Performance is well balanced by Intel's Core 3 304, though it has its limitations. It’s quick where it counts when browsing the internet, using office software, or playing media, and I've even found myself using it to offload image and video files to the server quickly and to book equipment and locations. Where it does fall is in graphics performance, as highlighted by the benchmarks: great for the office but really a no-go for gaming unless optimised for lower-specification systems.

Storage is also interesting with two M.2 slots. However, they support different lane counts, and a Crucial T710 Gen 5 drive returned 3510.25 MB/s read in slot one but only 1750.42 MB/s in slot two, which is slower than the UFS drive Beelink already fits, but then does enable you to boost the overall internal storage neatly.

Everything else is again well-balanced for office use. This is a nice, quiet machine with Windows 11 Pro pre-installed. The design is nothing flashy, but for dedicated office use with serious network potential it’s one of the best mini PCs I've tested.

Beelink EQi Wildcat Lake: Price and availability

• How much does it cost? From $509
• When is it out? Available now
• Where can you get it? Directly from Beelink and retailers such as Amazon

The EQi costs $509 for the 16GB LPDDR5 and 512GB configuration that I’ve looked at in this review. It's available direct from Beelink here in three configurations - 16GB LPDDR5, 24GB DDR5, and 32GB DDR5 RAM, all with 512GB SSD.

I'm also seeing several models on Amazon.com and Amazon.co.uk for around the same price.

• Value: 5 / 5

Beelink EQi Wildcat Lake: Specs

CPU: Intel Core 3 304, 5 cores/5 threads, up to 4.3GHz, 6MB cache, 15W TDP
Graphics: Intel Xe3-LPG integrated graphics
RAM: 16GB LPDDR5, or 24GB/32GB DDR5 at 5600MT/s; DDR5 version upgradeable to 64GB
Storage: 512GB UFS 3.1; two M.2 2280 slots (slot 1 PCIe 4.0 x2, slot 2 PCIe 4.0 x1), up to 4TB each
Left Ports: USB 3.2 Type-A, USB 3.2 Type-C, 3.5mm audio jack
Right Ports: 2x Thunderbolt 4, 2x USB 2.0, HDMI 2.0, 10GbE, 2.5GbE, AC input
Connectivity: Wi-Fi 6, Bluetooth 5.2, 10GbE Ethernet, 2.5GbE Ethernet
Audio: 3.5mm combined audio connection
Camera: None
Size: 126 x 126 x 44.2mm,
Weight: 500g
OS Installed: Windows 11 Pro
Accessories: HDMI cable, regional AC lead/power adapter and user manual

Beelink EQi Wildcat Lake: Design

If you’re looking for a machine that blends into the office background, then the Beelink EQi Wildcat Lake is a perfect option. The dark grey finish, rounded corners, and a lightly textured top panel are neat and strike a note that really highlights the machine's price point and specifications.

The size is also in line with what you’d expect from a standard Office Mini PC, with a relatively small form factor and a compact 126mm x 126mm footprint, at just 500g.

While the design might not be the most inspiring, it is generally well thought out and, surprisingly, has a built-in 85W power supply, so just a single power cable runs to the AC mains, with no need for a power brick.

On the front and back are a good range of ports, highly specified for a machine at this price. There’s also good air ventilation through the base, with airflow entering over the processor and passing through the storage.

Beelink quotes 32dB as the noise in normal use, and this seemed about right, with the ambient noise in the office slightly higher when ideal. This is also one of the neatest laid-out Mini PCs internally that I’ve seen, accessible through the base, which is held in place by four small cross-head screws. A small attention to detail is the small label that just helps to lift the base free, enabling easy access.

Once inside the interior, both M.2 slots are slightly hidden under a single large metal heatsink that lifts off with two screw. Once removed, your choice of M. 2 2280 SSD can be installed. This heatsink shows real consideration as a thermal solution for the installed drives, with the installation process taking no more than a couple of minutes.

The port selection and specification are where things really get interesting. Starting at the back, there are two USB 2.0 Type-A ports on either side of the HDMI Max 4.0 60Hz output rather than together, so a wired keyboard and mouse end up separated by a display cable.

Then there are two LAN ports, a 2.5GbE and a 10GbE, which is very unusual for a machine at this level; also unusual are the two Thunderbolt 4 ports. The front is a little more standard, with a USB 3.2 Type-A, a USB-C 10 Gbps, a 3.5mm audio jack, and finally the power button.

• Design: 4.5 / 5

Beelink EQi Wildcat Lake: Features

A quick check through the features highlights the intended audience for this PC. At the heart of the machine is the very respectable Intel Core 3 304; it’s nothing flashy, but this is a new-generation CPU so offers far more than entry-level CPUs in the past, and while not an AI powerhouse, does feature an integrated NPU as well as GPU, handy if you use Copilot on a day-to-day basis for work.

The CPU offers a five-core, five-thread processor with one performance core and four efficiency cores, 6MB of cache and a 15W TDP, turboing to 4.3GHz. Beelink quotes AI performance at 24 TOPS across the CPU, GPU and NPU for local AI processing.

While it’s great to see this entry-level office machine has AI potential, connectivity is what has surprised me about the EQi through this test. On the back are those two Thunderbolt 4 ports that run at 40 Gbps with DisplayPort 1.4, and if you need to connect more monitors, then there’s the more traditional HDMI 2.0, which I used to connect to my monitor for this review.

The two network ports, one 10GbE and one 2.5GbE, are a headline feature and enabled me to connect directly into the 10GbE LAN in the office. This meant that I could quickly offload video and image files through the machine's Thunderbolt 4 to the network. While this is an office-focused machine, I use an old Intel NUC 9 in the office for this exact task, and its speed over 1GbE and USB3.2 Gen 1 is unmatched by this.

Inside there’s a fixed 16GB LPDDR5 or upgradeable DDR5 at 24GB or 32GB, both at 5600 MT/s, with the DDR5 version supporting up to 64GB. Storage is 512GB of UFS 3.1, again fixed, but there is the option to install two M.2 2280 SSDs, both enabling up to 4TB. One point about these two slots is that they don’t support the same number of PCIe lanes, with one half as fast as the other.

Wireless runs over a MediaTek MT7922 module, with Wi-Fi 6 and Bluetooth 5.2.

• Features: 4.5 / 5

Beelink EQi Wildcat Lake: Performance

Getting started with the Beelink EQi was extremely straightforward, with a single power cord that plugs in, eliminating the need for the usual power brick. While this may seem small, it actually makes quite a difference, reducing cables and bulk to deal with.

Once the initial setup with the mouse, keyboard, and 4K monitor was complete and Windows 11 Pro was set up, the machine was installed with the usual array of applications. Microsoft Office, Adobe Creative Suite and a few games for good measure.

Starting with the main office applications, performance proved solid, running PowerPoint, Word, and Excel without issue, with Copilot there to support me as I put together a presentation for September. The speed is good, and despite the CPU's entry-level position, it handled everything impressively.

Checking out the results of the Geekbench 2,337 single-core and 6,303 multi-core results and, more importantly, the PCMark 10 came in at 4,733, showing that while this machine doesn’t excel, it’s where you would want it for a competent everyday computer that won’t get bogged down under heavy processing.

During the test, I edited a few images in Lightroom and Photoshop, and both ran surprisingly well, even with the images stored on the internal UFS 3.1 drive. While this drive isn’t the fastest compared with the latest SSDs, it is fast enough for most office-based work, with CrystalDiskMark results of 1977.4 MB/s read and 806.79 MB/s write.

What’s interesting is that inside there are two PCIe 4.0 slots, one listed as PCIe 4.0 x2 and the other as PCIe 4.0 x1, so I fitted a Micron Crucial T710 Gen 5 NVMe to each in turn to check out the speeds of each, just as a note you can add up to 4TB in each, giving a total of 8.5TB of storage which is impressive.

The first slot recorded 3510.25 MB/s read and 3530.98 MB/s write. The same drive in slot two returned 1750.42 MB/s read and 1766.17 MB/s write. That's roughly twice the speed in the first compared with the second and still very acceptable for the intended end user. While I used a PCIe 5.0 SSD, a respectable PCIe 4.0 SSD which is far cheaper should gain a similar result.

Switching to the Xe3-LPG graphics, things start to fall slightly. The integrated graphics are ideal for everyday work, watching back multimedia and light photo and video editing.

However, when it comes to gaming, the potential is limited. Hogwarts Legacy ran slowly, and Indiana Jones and the Great Circle was a no-go. Other games ran, but due to the lack of space on the internal storage, I ran all from the faster Crucial in the first of the two spare SSD slots.

After running the Mini PC in the office for a week, using it mainly for Word and research work with some light photo editing, a few features became apparent that set this Mini PC and test apart from most others. The speed of those connections just enabled the EQi to integrate perfectly with the studio; it’s nothing flashy, but it turned out to be the fastest machine for transferring image and video data to the NAS.

After a shoot, I plugged the CFExpress Type-B card reader into the Thunderbolt 4 port and transferred the files at speed over to the Ugreen IDX6011 NAS. The connection speed over the network, due to that 10GbE port, made the transfer speed far exceed that of any other machine I know.

I usually use an old Intel Nuc 9 Ghost Canyon that I can leave for a few hours before network editing on the MacBook Pro; however, here, with this small, inexpensive machine, it handled everything at speed.

While the 24-core TOPS, as quoted by Beelink, is marginal on the AI processing front, and while I was able to use Co-Pilot, running a local LLM was limited to a 3-4B model, I opted for Qwen3. The main issue here was that the RAM is only single-channel, which limits the speed; however, for checking through documents and basic summary tasks, it actually worked surprisingly well.

• Performance: 4 / 5

Beelink EQi Wildcat Lake: Final verdict

The Beelink EQi is an impressive office-focused machine that underwhelms with its design but outperforms in performance. It packs in the usual office-based Mini PC features such as being able to support three displays, 2.5GbE networking, a base level of storage and RAM, but then it surprises by offering more.

The 10GbE networking and Thunderbolt 4 ports are surprising and completely change what’s possible with this machine, and for something with such a small footprint, those two specifications instantly make it interesting.

When you then add in the fact that you can expand the internal storage with two 4TB SSDs, it then becomes even more interesting.

For me, the 10GbE and Thunderbolt ports are what make this Mini PC go from something that would blend into the background to something that’s actually very useful. The speed of those two ports enables me to hook this directly into an existing 10GbE network and to take advantage of the speeds that it offers. The issue, of course, is that you need the network infrastructure in place to make the most of this speed.

That brings me round to who the Beelink EQi is best for? Straight down the line, it would be for Office work. It has excellent single-core performance, runs pretty much silently, supports up to three displays, lets you upgrade the internal storage, and has an ultra-fast network connection that is beyond what most desktops offer. It’s also not really usable for playing games, and, on the surface, if you work in the creative sector, it’s not going to make an impact there either.

However, as I found with a cheapish mini PC that offers solid performance, the Thunderbolt 4 and 10GbE network make it great for offloading large video and image files to the NAS. Realistically, however, this is a Mini PC perfectly suited to anyone whose work involves documents, spreadsheets, browsers, and moving large files across a network; it is one of the more capable small machines I have tested this year.

Should I buy the Beelink EQi Wildcat Lake?

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For more professional-grade desktops, we've tested the best business computers.

• A spent Falcon 9 upper-stage rocket will hit the Moon near the Einstein Crater on 5 August at 5,400 mph, releasing roughly the energy equivalent of three tons of TNT
• The impact, though unintentional, was predicted since September 2025, giving NASA's LRO and South Korea's Danuri time to plan before-and-after imaging of the new crater
• Its scientific value is a rehearsal rather than a measurement: no seismometers have operated on the moon since 1977, so the event mainly tests impact-localization methods for future experiments

At roughly 06:35 UTC on August 5 2026, a spent Falcon 9 upper stage will stop being space junk and start being a crater.

The object, cataloged as 2025-010D, is projected to strike the lunar surface near the crater Einstein at 2.43 kilometers per second, which converts to about 8,700 kph or 5,400 mph.

While this was not intentionally aimed at the moon, almost a year of advance notice separates it from the usual category of orbital accident, even as NASA's Lunar Reconnaissance Orbiter and South Korea's Danuri are being prepared to take before-and-after images of the area on the moon where it is expected to make its impact.

An increasingly crowded atmosphere

The object itself is the spent upper stage of the Falcon 9 which launched from Kennedy Space Center on January 15, 2025, carrying two commercial landers: Firefly Aerospace's Blue Ghost Mission 1, which went on to make the first fully successful private lunar touchdown that March, and ispace's Hakuto-R Mission 2, which was lost shortly before landing in June.

Independent astronomer Bill Gray, whose Project Pluto software produced the original prediction, puts the stage at roughly 12 meters long, about the height of a five-story building, with a mass near 4,900 kg.

After separation, it stayed in an elongated Earth orbit with a 26-day period, swinging between roughly 220,000 and 510,000 km. Its path and the moon's path cross, but for 18 months the two objects have never met at the crossing point.

Gray expects a single crater around 17 meters across, reasoning by analogy with the double crater left by a Chang'e-5 T1 upper stage in 2022. The observation-planning paper led by Benjamin Fernando points higher, at roughly 27 meters across and 5 meters deep. Either way, the impact should release energy equivalent to about three tons of TNT.

There are limits to what can be measured from it. When NASA deliberately crashed Saturn V third stages and lunar modules into the moon between 1969 and 1972, those impacts were seismic sources fired into an emplaced network of four seismometers. That network was switched off in 1977, and nothing has replaced it yet. So while orbiters will photograph the resulting crater, the impact cannot be recorded seismically at all.

This will be only the second dead rocket known to have hit the moon by accident, after the 2022 Chang'e-5 T1 event, which China has never publicly acknowledged. Gray, for his part, says the episode points to a certain carelessness about how leftover space hardware gets disposed of. Objects in low Earth orbit eventually meet the atmosphere and burn up. The moon has no such cleaning mechanism, so lunar impacts are permanent.

• Amazon's Claude Sonnet project cost $1.8 million, 860% over budget
• The overspending remained undetected internally for nearly five months straight
• A financial auditing tool project exceeded its budget by $541,000

Amazon has confirmed an internal Claude Sonnet deployment intended for matching author details with product listings ballooned far beyond its planned budget.

The Financial Times found the project ultimately cost the company $1.8 million, marking an increase of 860% over the original allocation.

The overspending went undetected for roughly five months, raising fresh questions about how closely Amazon tracks its growing AI expenditures internally.

Mounting costs across multiple projects

Amazon's Claude-related overspend was not an isolated incident within the company's broader push toward deploying autonomous coding agents across various internal teams.

A separate project meant to build a financial auditing tool reportedly exceeded its budget by $541,000, compounding concerns about unchecked automated spending.

Another logistics-focused initiative, designed to shorten delivery times across Amazon's broader distribution network, incurred an additional $134,000 in unplanned costs.

Company insiders explained that mistakes once considered trivially cheap have grown catastrophically expensive as token-based pricing models replaced older subscription arrangements.

This shift coincides with AI agents gaining broader autonomy, allowing errors to multiply quickly before human reviewers noticed the growing financial damage.

Amazon pushes back on the narrative

In response, Amazon issued an internal statement acknowledging ongoing experimentation while disputing characterizations of these incidents as routine business practice.

"As with any new technology, we're experimenting, learning and improving how we use it, including how we drive cost efficiencies," the company said.

The company further stated that isolated examples portrayed as business as usual do not accurately capture how its teams actually apply AI tools.

Amazon's quarterly revenue exceeds $181 billion, meaning the disclosed AI overspending accounts for less than 0.1% of one month's earnings.

This pattern of AI-driven cost overruns follows earlier troubles at AWS, where automated coding bots triggered several unexpected service outages this year.

Amazon responded to those outages by restricting AI agent permissions rather than granting them access equal to senior engineers overseeing similar tasks.

The company also discontinued an internal leaderboard once used to track employee AI usage, as rising costs prompted a reconsideration of that approach.

Executives across the industry continue framing AI spending as a necessary investment despite growing evidence of inconsistent returns.

Amazon's continued experimentation with AI-driven coding tools comes despite skepticism from other corners of the tech industry regarding its actual business value.

Uber's chief technology officer, whose company also operates a substantial logistics network, has publicly stated no clear connection exists between heavy AI adoption and successful software delivery.

This skepticism stands in contrast to Amazon's own internal messaging, which frames the company's approach as ongoing experimentation rather than a fully validated cost-saving strategy.

Some analysts now argue that unchecked automation could quietly erode profit margins long before leadership notices meaningful financial impact.

For a company of Amazon's scale, these overruns remain financially minor, though they suggest oversight gaps that smaller companies may struggle to absorb.

Fanxiang PS2000W: 30-second review

Fanxiang is a Chinese storage brand, most commonly known as Shenzhen Fanxiang Information Technology. The company grew out of a team with a decade of prior experience in storage design, firmware, and product sales, and they've built their range around four main product lines: solid-state drives, mobile storage, memory modules, and storage cards.

It’s been making external SSD storage for about five years, with a wide range of devices and specifications.

The PS2000W appeared in 2025, and delivers capacities from 500Mb to 4TB in a slim aluminium case. It comes with a soft carry pouch and a cable that has an integrated adapter for both USB-A and USB-C connections.

The quoted performance is 2000MB/s reads, and 1800MB/s writes, but that assumes you have a USB 3.2 Gen 2x2 port, and that’s not a common standard.

More realistically, a USB 3.2 Gen 2 port will achieve a little over 1000 MB/s reads, and 950 MB/s writes with either a USB-C or USB-A connection.

For those wanting faster transfers, you either need a system with the rare USB 3.2 Gen 2x2 port, or choose a different drive that supports the USB4 standard, like the Fanxiang PS3000 portable SSD.

However, that option costs $350 for the 1TB model, which is substantially more than the $150 the PS2000W is typically priced at for that capacity.

The problem the PS2000W doesn’t address is that this drive is double the price it was only eight months ago, and unless you have a system that can make the most of its enhanced performance, there are cheaper alternatives. In fact, if you have a spare NVMe drive, then buying an external enclosure for USB4 is probably a better choice.

Not the best portable SSD, or the worst, but sadly impacted by the outrageous cost of flash memory in 2026.

Fanxiang PS2000W: Pricing and availability

• How much does it cost? Starts from $90
• When is it out? Available now
• Where can you get it? Sold through online retailers and also directly from Fanxiang.

The Fanxiang PS2000W portable SSD comes in capacities starting at 500 GB, doubling each step to include 1TB, 2TB, and 4TB drives.

Directly from the maker, these are priced at $112.99, $203.99, $379.99 and $495.99 for the largest capacity. That pricing makes the 4TB the best value, and the 500MB the worst.

But the maker prices aren’t especially good, and I was able to find it much cheaper on Amazon. On Amazon.com in the US, the 500MB model is only $89.99, the 1TB model is $149.99, and the 4TB model is $419.99. There wasn’t a price for the 2TB version.

UK pricing via Amazon.co.uk is £64.60, £119.99, £169.99, and £319.99, and Amazon EU sites are €94.99, €139.99, €229.99, and €379.99, respectively.

For those curious, the UK is the cheapest place to buy this hardware, followed by the EU, and the most expensive is the USA.

While officially Crucial has exited the retail market, its equivalent product, the X10, is still available from many retailers. At $207 for the 1TB option, and $299.99 for the 2TB option, the X10 is slightly more expensive. But it is rated as water-resistant, and while there isn’t a 500GB model, there are 6TB and 8TB options.

A cheaper option with the same spec is the PNY RP60, but the 1TB model is still $10 more than the PS2000W.

But many of the familiar brands are more expensive, such as the SanDisk Extreme Pro V2, Samsung T9, Kingston XS2000 and LaCie Rugged Mini. Western Digital has its My Passport SSD, but it’s the same price as the PS2000W, but only offers half the performance.

Those looking for a cheaper option should consider the $82 Orico S20 with 512GB storage.

It’s tempting to look at the pricing of the PS2000W and see it as a good deal, when it’s far from that.

Price tracking the 1TB model: it was $149.99 at the time of writing; it was $75.99 in November of 2025. That’s nearly a 100% markup.

Now, I should say that Fanxiang doesn’t control the price of Flash memory, which has spiked since AI companies and speculators decided to buy all of it. But in good conscience, I can’t recommend people spend this sort of money on devices that cost half as much a year ago.

• Value: 3 / 5

Fanxiang PS2000W: Specs

Fanxiang PS2000W: Design

• Aluminium enclosure
• Dual USB standards cable

Some brands, and you know the names, will try to make their external SSDs and HDDs as cheaply as possible, typically by using mostly plastic construction.

Thankfully, Fanxiang didn’t go down that path with the PS2000W, and this device appears to be entirely made of metal. And, based on its relatively low weight, it uses aluminium.

The package is a simple lozenge shape with curved sides and flattened ends, measuring 85mm long by 50mm wide and less than a centimetre thick.

Out of the box, it comes with a 30cm USB-C to USB-C cable that has a USB-A adapter permanently attached. The drive and cable fit neatly into a soft drawstring bag, also provided, making for a neat and easily pocketable solution.

The review model was a 2TB capacity drive, which translated into 1863GiB of usable space, and it connects to the host device using USAP (Serial ATA).

To achieve the best performance, it requires a USB 3.2 Gen 2x2 port, but the drive is backwards compatible with USB 3.2 Gen 2, Gen 1, and even USB 2.0. However, as you move down the USB interface pecking order, the expected speed decreases.

As for features, this drive is basic. It supports S.M.A.R.T. NCQ, TRIM and GPL, but it doesn’t offer hardware encryption like on the Kingston XS2000 or the Crucial X10 Pro.

There are also no software tools included, though you can download a utility for SSD management from the Fanxiang website. I didn’t notice any means to update the installed firmware, or any firmware releases.

To be blunt, this is a basic USB SSD with a Gen 2x2 interface, but otherwise it’s unremarkable. And therefore, if you don’t have that interface, there appears to be no compelling reason why you would choose this one over another brand.

• Design: 3.5 / 5

Fanxiang PS2000W: Performance

• Needs USB 3.2 Gen 2x2
• Performs within quoted specification

Let’s be brutally honest: USB 3.2 Gen 2x2 was a dead standard walking from its inception, and USB4 didn’t really include it in its repertoire of supported prior standards. There are a few exceptions, but in general, if you don’t actually have a port to the 2x2 standard, then you won’t see the true performance that this drive can muster.

For that reason, I’ve presented the performance via a Gen 2x2 port and a much more common Gen 2 variety. How does it compare with the best Gen 2x2 drives?

Well, read speed is below the Crucial X10 Pro, ADATA Elite SE880 and LaCie Rugged Mini SSD, but write speed is close to what all those achieve.

Given the different controllers and implementations of Gen 2x2 interfaces, the Fanxiang PS2000W portable SSD doesn’t have a compelling reason to be chosen over any other drive in Gen 2x2 mode, but it can get close to the maximum realistic speed for this interface.

But if speed is the prerequisite, then a USB4 or Thunderbolt external drive, like the Corsair EX400U or the SanDisk Extreme PRO with USB4, would be a better choice if you have a USB4 port. These drives can hit read speeds of 3800MB/s or higher, and 3650MB/s writes.

And, if I were rushing to catch a plane or train, and needed to transfer files, I know which I’d prefer.

• Performance: 4 / 5

Fanxiang PS2000W: Final verdict

If the Fanxiang PS2000W portable SSD had appeared during the golden age of Gen 2x2, when the Kingston XS2000 appeared back in early 2022, and at a reasonable price, then it might have been something I’d recommend.

And the fine irony of that comparison is that the price of the PS2000W is almost exactly the same as the XS2000's MSRP was nearly four years ago. And the Kingston device supported hardware encryption, where the Fanxiang does not.

To be clear, the construction quality and performance of the PS2000W are absolutely fine, but what isn’t is the amount of money being asked for.

If money isn’t an issue for you, I’d buy the Crucial X10 Pro before they’re extinct, as it has hardware encryption. Or I’d consider a USB4-capable drive, like the Corsair EX400U, if you have a port that supports that specification.

Should I buy the Fanxiang PS2000W?

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• Bitdefender reported Roblox players lured by a fake “undetected” Xeno Executor mod spreading malware
• Infection chain delivers a Java‑based RAT and infostealer stealing browser data, online accounts, payment info, and crypto wallets
• Malware also enables surveillance and remote control; campaign peaked in March 2026 and remains active against Roblox’s 82M players

Cybercriminals are targeting Roblox players with an infostealer and a Remote Access Trojan (RAT) malware that grants them full control over compromised computers, experts have warned.

Roblox is an online gaming platform, virtual universe, and game creation system where users play millions of games and user-generated mods. Among the mods is Xeno Executor, a utility that allows players to run scripts that automate certain actions or run custom code. Some players use Xeno Executor to run cheats, too.

Since it’s an unofficial script, Roblox does not allow it and blocks it whenever a new version is released. Now, security researchers Bitdefender have reported finding an “undetected” version being promoted on various gaming forums, Discord communities, and similar.

CornFlake and CocoShell

This version is advertised as “invisible” to Roblox’s anti-cheat systems, but in reality, all it does is trigger an infection chain that ends in a Java-based RAT and information stealer.

The malware grabs browser data such as passwords and cookies from some of the most popular browsers (Chrome, Edge, Brave, Opera, Vivaldi), as well as online accounts and payment data (Discord, Roblox, Minecraft, Microsoft Store tokens, and more).

It also steals cryptocurrency wallet data, particularly targeting the Exodus Wallet.

As for surveillance, it can log keys, track mouse movements, grab screenshots, stream whatever is on the desktop, and access the webcam. The crooks are also granted file upload and download, PowerShell command execution, and more.

The campaign was kicked off at the start of the year, reaching its peak in March, it was said. It has now stabilized and is still going relatively strong.

We don’t know exactly how many players fell victim to this campaign, but Roblox is an incredibly popular platform, so it is possible the campaign was rather successful, too.

According to Activeplayer, Roblox currently has more than 82 million active players.

• Third-party data centers now host more enterprise workloads than corporate server facilities
• AI infrastructure is steadily pushing rack power requirements to unprecedented operational levels
• Rising electricity costs continue squeezing already stretched data center operating budgets everywhere

Corporate IT infrastructure is increasingly moving beyond company-owned facilities as organizations rely more heavily on third-party data centers for critical workloads.

The Uptime Institute Global Data Center Survey found third-party sites now account for 46% of enterprise IT workloads, compared with 44% still running inside enterprise-owned corporate server farms.

The survey gathered responses from more than 800 data center owners and operators across multiple countries, with 52% located in North America and Europe. It found that 10% of respondents still rely on smaller IT rooms and server cabinets rather than dedicated facilities.

Off-Premises becomes the default choice

Uptime Institute expects self-owned server halls to hold their current share of workloads through 2028, even as growth continues.

Analysts forecast that third-party facilities will expand further, reaching 48% of workloads and drawing capacity away from informal IT rooms.

Average rack power density has crossed 11 kW for the first time, driven by a shift toward higher-powered hardware across the industry.

Excluding a small cluster of ultra-high-density facilities, the typical rack density figure sits closer to 7.8 kW, up slightly from 7.5 kW in 2025.

Some operators are reportedly shortening hardware refresh cycles to under four years, reversing a trend among major hyperscalers toward longer lifecycles.

Firms such as Microsoft, Google and Meta have instead extended server lifecycles to six or seven years to reduce depreciation costs.

Power demands intensify industry pressures

Uptime found nearly a quarter (24%) of respondents now operate at least some racks rated 30 kW or above, compared with 19% last year.

Much of that increase came in the 50 kW-plus range, including some operators fitting AI and GPU servers into racks exceeding 100 kW.

Outages have declined for a sixth consecutive year, with the share of respondents reporting one dropping by three percentage points.

Even so, Uptime warns that unstable grids, extreme weather and supply chain constraints continue to threaten future reliability across the sector.

Roughly 71% of operators said their worst outage in the past year cost at least $100,000, up sharply from 57% previously.

Rising costs for power, staff and AI-related equipment now rank as the industry's foremost financial concern.

Capacity forecasting, power availability and ongoing supply chain disruptions add further uncertainty for operators planning large future data center expansions.

Furthermore, over half of operators (53%) now report difficulty finding qualified candidates for vacant roles, up from 46% a year earlier.

Staff shortages further compound these pressures, since electrical and junior operations roles each show skills gaps affecting 38% of surveyed organizations.

Taken together, the figures suggest an industry stretched between surging demand and finite power, staff and equipment supplies — with no easy resolution in sight.

These mounting pressures explain why third-party facilities have overtaken corporate server farms for the first time.

Outsourcing high-density, energy-intensive workloads lets enterprises sidestep infrastructure investments that increasingly strain their own budgets and technical staff.

• Workers are rebelling against RTO mandates more than ever
• Enhancv report find many employees see RTO as a path towards layoffs
• Younger workers are most at risk, many saying they feel unfairly targeted

New research has claimed that "a new era of workplace sabotage" is seeing workers fight back against unfair policies, particularly when it comes to return to office (RTO) mandates.

The report from Enhancv found nearly three-quarters (72%) of workers suspect RTO as being a way to bring in stealth layoffs via attrition of those employees unwilling to compromise on their working arrangements.

Nearly half (46%) of workers also admitted to "coffee badging" (swiping in and leaving early), a tactic which has become standard among those employees who suspect a hidden corporate agenda.

"The Great Compliance"

"Rather than fostering collaboration, strict return-to-office (RTO) mandates have given rise to The Great Compliance—a phenomenon where workers show up physically to satisfy badge-trackers while mentally and strategically checking out," Enhancv noted.

Its study, which surveyed 1,000 full-time workers across the United States who have been subject to new or stricter return-to-office (RTO) policies within the last 12 months, found a major deficit in trust among employees, especially as AI-related job cuts continue to hit the headlines.

And while RTOs may often be explained as getting a workforce back under one set of policies and rules, the simple truth is that some workers simply won't be able to comply - whether it be for family reasons, or just the fact they moved elsewhere during the past few years.

Enhancv found over a quarter (27.8%) of interviewed employees had been able to successfully negotiate a personal exception to RTO policies - creating a visible two-tier workforce, with those without any leverage may feel left out or unfairly targeted.

This could be especially true for younger workers, and the survey found Gen Z (ages 18–24) and younger Millennials reported the lowest trust in corporate motives, perhaps putting paid to the claim that RTO mandates are vital to training the next generation of workers, with average trust scores nearly 20 points lower than their Boomer counterparts.

“The idea that we need to be present is asinine," one survey respondent noted. "Many come in just to socialize and then go home. No more work is getting done here than at home.”

Enhancv noted how, "by forcing a physical presence that this generation views as performative, companies may be solving a short-term real estate problem while creating a long-term retention crisis with the very talent they claim to be developing."

To add insult to injury for employers, the survey found over one in three employees are now using their time at the office to apply for new jobs or even build side hustles as a financial safety net.

Over a third (36%) said they had applied for a new job while sitting at their current office desk, and a similar amount (36%) had started a side hustle since an RTO mandate had been announced, seeking "extra security" in an environment they no longer trust.

32% also admitted to intentionally reducing their daily output as a form of quiet protest.

"The irony for leadership is clear: the very desks they fought to fill are now being used as staging grounds for the Great Exit," the report concludes.

• Name.com has partnered with Lovable, Hercules, Railway, Sellfy, and Anything.com
• The new APIs aim to make it easy to search, purchase, and manage domains
• In the first week of integration, Lovable users registered thousands of domains

Name.com has today announced new partnerships with Lovable, Hercules, Railway, Sellfy, and Anything.com.

The expansion to Name.com’s API will allow users to search, purchase, and manage domains directly inside the tools they build with.

Name.com claims that AI has drastically lowered the barrier to building websites, apps, and online businesses, making it as simple as requesting what you want using plain-English prompts. The company notes how these new partnerships mark a shift in how software is being built and launched.

The importance of domains

Despite many traditional websites seeing fluctuations in traffic, a recent survey by Name.com reported that 77% of respondents expect domain importance to increase because of AI.

According to a press release shared with TechRadar Pro this week, Name.com believes that these latest integrations will help bring registration into the platform where creation happens, making digital identity an integral part of the building process right from the beginning.

"We’re at an inflection point where digital presence is being rebuilt from the ground up," said Sammy Ahmed, VP and General Manager at name.com. "Domains remain the foundation of online identity and presence, signaling trust and credibility that AI cannot replicate. These partnerships reflect growing demand for domain registration to happen directly within the tools people already use, and name.com is making that seamless."

The company also claims that in the first week of Lovable’s integration, thousands of domain registrations were made, showing a strong sign of demand.

“More people than ever are building digital products with tools like Lovable,” said Sebastian Schaaf, Product at Lovable. “Giving these products a home needed to be effortless for our users, and we decided that name.com was the right partner to make this possible at our scale."

The announcement builds on name.com’s list of existing partners and integrations, including Vercel, Replit, Netlify, Bolt, and Beacons.

BenQ has been one of my favorite monitor brands for quite a while now. The build quality is spectacular, the built-in features are wonderful, and their companion software makes it feel like a first-party device for Apple products, which is a huge win for me as an Apple product lover.

When it comes to creative hardware, those working in the industry need the best of the best. They crave color accuracy. A lot of them crave fun features as well, but above all they crave the ability to reduce friction in their day-to-day work.

Specifically, when it comes to monitors, color accuracy and calibration matter more than most people realize. If you're in this space, you're very familiar with how to calibrate your monitor. BenQ decided to answer that not just with its own hardware, but by building its own hardware. The company built a color calibrator into its monitors that are designed for those who need the color calibrator. BenQ didn't just do it in a way that would work, but did it in a way that's elegant and very BenQ-esque.

The monitor has a color calibrator arm that swivels down from the monitor and then pushes itself back up as well, hiding away when you're not using it but popping out when you need to calibrate further. It has a light shade or a hood, depending on the industry that you're in, that helps make sure there's no glare or reflections or anything that can manipulate the colors, producing an even more true-to-spec experience. So, for those looking for the best video editing monitor or best photo editing monitor, this one is excellent in my experience.

BenQ PD2770U: Price and availability

Right now, this monitor sells for around $1,700, and that comes with the hood and the built-in color calibrator. BenQ is also really good about sending cables that you need in the box, so don't worry about that.

If you buy this monitor, you can literally just plug it straight into your computer of choice without needing any extra accessories. It's widely available, and you can pick this up from BenQ's website, Amazon, and other retailers.

BenQ PD2770U: Unboxing and First Impressions

As expected with BenQ monitors, the packaging is simple yet well-done. It's no Apple unboxing experience, but it is one that is predictable and expected. On first unboxing, I was a little confused about how the shield could be folded up so tightly, but then I realized how intricately they've tucked it into the box to keep it safe and protected.

Other than that, I was able to set up the monitor fairly quickly. I tried it first with the built-in stand and then quickly added it to a monitor arm that I had to try out some testing. Once I finished initial rounds of testing, I moved this to somebody on my team who does a lot of video editing, and now he has been using it on his desk for the last 100 days.

I've tested this monitor in low-light scenarios and in high-light scenarios with fluorescent lights directly above the desk. Thanks to the shader, this monitor has performed brilliantly and brighter, but the shader makes it so that even this dimmer color is still spectacular and vibrant.

BenQ PD2770U: Design & Build Quality

This brand came out very much like all the others, to where it is professional and simple. It's got a basic stand if you choose to use it. It's got great cable management and simple height adjustments.

What's great is that even if you choose to not use the stand, the hood attachment magnetically connects directly to the monitor itself, meaning that you can use this with any monitor arm or monitor stand that you so choose.

Also, since it is magnetic, if you're the kind of person that works in a light-controlled room or maybe a dimmer room (like a personal home office), you can customize the lighting. Perhaps you won't need the shade. If you work in a coworking space or an area where you cannot control the lighting as much, that's where the shade comes in clutch.

Last but not least, having the USB-C hub built in makes this a monitor and a hub all in one. If you don't have a ton of ports and perhaps you're just running a more simple setup where you have a desktop or laptop plugged directly into this monitor and that's about it, then you can run all of that.

You can have plenty of room to wiggle directly from the monitor itself without needing a docking station. Even if you had a couple accessories from a laptop, you can plug those in if you wanted to.

BenQ PD2770U: In use

As I previously mentioned up above, my team and I have had this 4K monitor in our arsenal for over 100 days. It's actually even closer to 150 at this point. I wanted to make sure that we could get some good long-term testing with this panel before giving results.

In that 100 days, my team and I have put this monitor through the tests. We've edited countless videos on it, done multiple big photo projects, done administrative tasks, and used it for managing content and so much more. While it is phenomenal, all those things, it is 100% overkill for anything other than photo or video editing where you need color accuracy.

Currently, this monitor is set up in a coworking area that has some pretty aggressive fluorescent lighting right above the desk. For our scenario, having the shader has been an absolute game changer, as it helps keep any glare off of the monitor, changing and destroying the color and visual sighting.

Plus, having the color calibrator built in is not only a fun element but an absolute game changer if you need to confirm and double-check calibration throughout your projects.

BenQ PD2770U: Final verdict

I've been using this monitor mostly for creative products, but of course had to test and see how it would work for basic administrative tasks. As expected, I can reassure you that if you are using this monitor for business or productivity or administrative tasks, you are dead wrong.

This is not meant for that at all. It's overkill in price, feature set, and physical operation. However, if you are in that creative space and you want something that is more consistent than what you ever had before, then you should absolutely check out the BenQ Creative Pro PD2770U monitor.

To make a long story short, if you are in the creative space and you need color accuracy above all else, or if you find yourself reaching for or even wishing for a color calibrator regularly, then this is absolutely your monitor.

If you are just looking for a 27-inch 4K monitor, this is 100% overkill, but it will do the job in a better and fancier way than you probably need. If image quality, screen resolution, and picture clarity are a part of your workflow, consider this one. If not, check out some of our other spectacular monitors.

For more top-performers, we've tested the best business monitors and the best monitors for Mac mini.

Multiple studies have found that the average person has over 150 passwords. As a password security expert, I regularly recommend that every single password people use should be both unique and strong - but this adds the problem of being able to remember each one, or store them securely.

Password managers are designed for this exact reason. They store your passwords in encrypted vaults and autofill your credentials as and when you need them. For this very reason, Keeper is one of our most highly rated password managers.

Right now, Keeper has cut prices on its personal, family, and business starter plans by up to 50%, providing an affordable password management solution that can store and generate your passwords, while also providing convenient features for securely sharing regularly used household or business passwords.

Why we recommend Keeper

In our Keeper review, we praised the zero knowledge architecture and device level encryption that helps keep the contents of your personal vault hidden from hackers.

Keeper also scored great marks in our usability and convenience testing. Rather than having to enter your master password each time you need to fill in your credentials, Keeper uses biometric security to access your vault. Biometric security uses a facial scan or fingerprint to verify that its actually you accessing your vault. It's also phishing resistant.

Four households, the Family Plan includes five private vaults, allowing you to quickly share Wi-Fi or streaming passwords using Keeper's shared vaults.

The Business Starter plan covers 5 users, covering an unlimited devices, shared team folders, user activity reporting for IT admins, and a free Family Plan for every team member.

These discounts apply to the first year only, so the value is highest for new customers or anyone switching from a monthly plan. After that, pricing goes back to standard rates.

The Lenovo ThinkPad P14s Gen 6 is exactly what you expect to see if a ThinkPad got Botox. This computer is a professional workstation with the familiar soul of a ThinkPad business laptop, just in a sleek, facelifted chassis.

Everything about the P14s is geared toward getting work done without compromising looks or quality-of-life features that have been overlooked in the past when other workhorse laptops hit the market.

It combines serious hardware with a compact design without sacrificing the physical qualities that make a work laptop enjoyable to use. Given the size, In my experience it's one of the best laptops for engineering students looking for a more portable powerhouse, or professionals working across multiple locations.

Lenovo ThinkPad P14s Gen 6: Price and availability

The P16s Gen 3 is available in a range of configurations, with both AMD and Intel chips. You can grab the AMD version and the Intel version from Lenovo US. That's the same for Lenovo UK with AMD models and Intel models.

I'm testing the Lenovo ThinkPad P14s Gen 6, Model 21QT0029US to be exact. It has an Intel Core Ultra 7 265H processor, an Nvidia RTX Pro 500 Blackwell GPU with 6 GB of GDDR7 memory, 32 GB of DDR5 RAM, and a 1 TB NVMe SSD. It also includes Windows 11 Pro and Intel vPro support, which fits Lenovo’s stellar reputation for business security and management.

This ThinkPad P14s Gen 6 has 32 GB RAM, while leaving the second slot unused. For this laptop, Lenovo supports up to 96 GB, giving the device room to grow for larger projects. There is also only one internal M.2 storage slot, though, so if you increase storage, you must replace the existing drive.

Our exact testing model and configuration can be found for about $2,700, but with Lenovo’s frequent promotions you can also find a similar P14s configuration closer to the mid-$2,000 range. That number makes more sense when the laptop is compared with other professional mobile workstations.

Lenovo ThinkPad P14s Gen 6: Unboxing and First Impressions

Physically, the ThinkPad P14s Gen 6 is fantastic. Its aluminum top and bottom give the chassis a sturdy feel with very little flex or anything else that makes it seem fragile.

Lenovo also puts the system through MIL-STD-810H testing, which is a standard used by the DOD for drops/shocks, extreme temperatures/humidity, dust, and high altitude. So, TL;DR—this thing is solid.

Its construction feels quite fitting for a computer you’d expect to travel with, to and from work every day. The design itself is unmistakably ThinkPad as well. Which is a pro and a con depending on how unique you want to be in your laptop.

It features an all-black, professional-looking frame with subtle Lenovo branding and the familiar red TrackPoint on the keyboard. If you want to stand out and make it your own, slap a sticker on there, but if you want to look professional and crisp, then you'll fit right in with yet another ThinkPad that looks exactly like the rest.

Lenovo ThinkPad P14s Gen 6: Design & Build Quality

The keyboard is the undisputed favorite part of the laptop across our entire team. The shape and texture of the keys feel excellent, and each press is incredibly responsive, tactile, and honestly satisfying. Typing for long periods never makes my hand feel tired or makes me want to switch to a different keyboard for a break.

In fact, this whole article is typed on the P14s, that’s how much I enjoy its keyboard. I have several people who help me test laptops to ensure I am not just using my own personal bias in my reviews, and we all unanimously agree that the ThinkPad keyboards are spectacular, and the P14s Gen 6 is no exception.

The backlighting of the keyboard is also convenient, with brightness control using the Fn key and spacebar that offers a few different lighting/brightness options. The TrackPoint, along with its corresponding buttons, works as you’d expect too. With so many laptops moving to have this be software-driven, it is nice to see some business devices still have these elements built into key commands that have been around for quite some time.

The fingerprint reader is built into the power button, works quickly, and was much more dependable for us than facial recognition. The 5 MP infrared camera is acceptable for video calls, but we ran into issues when strong backlighting was present, mainly with facial recognition when trying to unlock the device.

The 14.5-inch display on this configuration has a 2560 × 1600 resolution at a 16:10 aspect ratio, a 90 Hz refresh rate, and a rated brightness of 400 nits. It covers 100% of the sRGB color space and is X-Rite factory-calibrated. In person, the screen is beautiful: the text looks sharp, colors have plenty of vibrance, and the taller 16:10 aspect ratio gives you extra vertical space for any project, document, or webpage.

The 90 Hz refresh does a good enough job to keep up with pretty much all on-screen movement and keep it looking smooth. Of course, we wouldn't suggest video editing or photo editing even on this at a professional level, but that's not what this is designed for now, is it? I'm still shocked at how many people in the classic professional white-collar jobs are happy with standard HD screens.

Movies, TV, and other media look great, but its more important job is providing a clear, color-accurate workspace for professional applications, and it does that quite well, too. The screen itself has that silky, smooth feel that many modern displays have.

There are also two 2-watt speakers, which are surprisingly good for laptop speakers. Dolby Audio helps voices and other sounds come out clearly and gives music and video a fuller sound than I would have expected from a laptop.

Of course, they will not replace quality headphones or external desktop speakers, but they work well for calls, presentations, and, as I mentioned before, your movies and TV if you plan on using this laptop for fun things too.

Lenovo ThinkPad P14s Gen 6: In use

During normal use, the P14S feels extremely fast. I was able to move between multiple programs, browser windows, and background tasks without experiencing any noticeable lag. It responds like a computer ready for real work rather than one suited for online shopping and the occasional email.

The RTX Pro 500 is a workstation-class GPU, and the P14s carries independent software vendor certifications for its intended professional applications. That matters when stability is more valuable than chasing gaming-level frame rates. It also shows that Lenovo knows its lane with this laptop.

They aren't making this for the gamers, generalists, or the budget-seekers. This has its niche in the professional category of needing to be able to handle demanding programs with speed but needing to break records in any way either. The reliability and stability are the game here, and Lenovo has it in spades.

There are reasonable limits. The GPU’s 6GB of memory may restrict extremely complicated renders, massive simulations, or demanding local AI projects - set expectations, though, and you'll get by.

The included 32GB may also become a ceiling for unusually large workloads, although the 96 GB upgrade limit provides potential to grow. For seriously demanding workloads, the ThinkPad P16 Gen 3 is still the king.

The laptop “can” run games, but gaming is simply not its purpose. Performance is good rather than great, maybe even just okay, and the RTX Pro hardware is better suited to professional engineering applications than maximizing frame rate for games. This kind of testing is helpful too, as it gives a glimpse into how this may perform for CAD renderings and other graphically-intensive scenarios.

While this could pull up some models, the more complex ones may take longer than desired, and this isn't the machine you'd want long-term if you live in that space -- or at least not as your only machine.

The cooling system for the P14s becomes noticeable during heavy work. Under sustained loads, the fan is medium-loud, with a somewhat turbine-like sound. Imagine a Dyson bladeless fan. It’s not crazy loud, but there is no mistaking when the laptop is working hard.

Our review unit has the larger 75Wh battery and a 100-watt USB-C charger. Under normal or light use, it lasted pretty much the entire day even at maximum brightness. This was a real-world observation rather than a controlled rundown, but it showed that the device can work away from an outlet without constant anxiety over battery life.

Heavy CAD rendering or other sustained CPU and GPU work will indeed drain the battery much faster, especially with higher screen brightness. That is to be expected from mobile workstation hardware. Lenovo rates Rapid Charge to restore up to 80% in about an hour, which makes it easier to recover a useful amount of battery life during a short break.

Lenovo ThinkPad P14s Gen 6: Final verdict

The ThinkPad P14s Gen 6 feels like another professional tool to choose from if you are in that arena of the professional workforce. Its solid construction, fantastic keyboard, sharp display, quality ports, upgradable memory, and consistent performance support the work it was designed to do.

When you consider the professional hardware, ISV certifications, display quality, and upgradable options, the price of this laptop honestly shifts from a negative to one of its strongest positives.

For professionals and students seeking serious power without moving to a larger or significantly more expensive workstation, this computer is a solid all-around option. They don't necessarily want the newest or the fanciest in the category, they want something they know they can rely on, because downtime isn't worth the cost of a fun feature they occasionally use.

For more powerhouse performers, we've tested the best business laptops and the best mobile workstations.

• Palo Alto Networks’ Unit 42 detailed three Google passkey exploits
• Attacks require prior malware infection; methods ranged from impersonating victims to stealing the master secret protecting synced passkeys
• Google implemented fixes after disclosure, with some services (e.g., eBay) patching vulnerabilities directly

Security researchers from Palo Alto Networks’ Unit 42 have found three ways to exploit Google’s passkey system and log into people’s PIN- or biometrics-protected accounts.

They named these ways ‘Pass-ta-key’, ‘Silver Pass-ta-key’, and ‘Golden Pass-ta-key’, each being progressively more dangerous than the previous one.

While it sounds mighty dangerous, there are major caveats to the exploit, and some of the holes have been plugged already.

Trusting the wrong device

The biggest caveat is that the victim’s device needs to be infected with malware beforehand. Malware can do all sorts of things, from stealing session cookies to exfiltrating sensitive data, so if a device is tainted with malware, it’s already in trouble.

Still, Unit 42’s findings were important enough to warrant a fix from Google.

In the first technique, the attackers pretend to be the victim. By using malware, they can “ask” Google to log into a passkey-protected account as if it was the victim themselves. Usually, the service being logged into would require a PIN or a fingerprint to confirm the authenticity of the request, but in this scenario, that wasn’t the case.

The method doesn’t work everywhere, though. Unit 42 could not replicate the attack on GitHub, but they succeeded on eBay. The latter later fixed the problem.

In the second attack, Unit 42 managed to make Google “trust” the threat actor’s device, meaning the victim’s computer was no longer necessary.

In the third attack, the researchers managed to steal the “master key”.

Google Password Manager syncs the passkeys between devices, and to do that, it uses a master secret that protects all of the synced passkeys. The researchers found that, under certain circumstances, malware can grab this master secret while Chrome is temporarily using it, unlocking all of the synced passkeys, copying them to another computer, and being able to use them at a later date.

The researchers disclosed their findings with Google before publication, and some fixes were already implemented. Google is yet to comment on the findings and confirm that all of the flaws were addressed.

Via BleepingComputer

For many enterprises, virtualization has become so foundational that its strategic importance is easy to overlook.

Hypervisors, management tools, and the surrounding infrastructure stack quietly support the systems that run finance, operations, customer engagement, supply chain and other business-critical processes.

When that layer changes, the impact is rarely confined to IT.

It can affect cost, resilience, agility and the organization's ability to set its own technology roadmap.

That is why the latest shift in the VMware ecosystem deserves close attention from technology leaders.

With vSphere 7 reaching the end of general support and newer platform models emphasizing bundled, subscription-based private cloud architectures, many organizations are being pushed into a compressed decision cycle.

The question is no longer simply which version to upgrade to. It is whether a vendor-driven platform shift aligns with the business, operating and financial outcomes the organization is trying to achieve.

That distinction matters. A version upgrade is a technical project, but a virtualization strategy is a business decision.

And business decisions should be guided by requirements, value and timing, not simply by changes in a vendor’s roadmap.

Don’t confuse urgency with strategy

Support deadlines have a way of focusing attention. They also have a way of narrowing choices. When a critical platform approaches the end of vendor support, the instinct is often to move quickly toward the recommended next step. In some cases, that may be the right decision. In others, it can lead to organizations into unnecessary cost, disruption and loss of flexibility before they have fully evaluated the alternatives.

The risk for IT leaders is treating the vendor product roadmap as if it were automatically the enterprise roadmap. The two may align, but that alignment should be proven, not presumed. A vendor’s timeline is not, in itself, a business case for broad infrastructure change.

A vendor’s priorities are shaped by product strategy, recurring revenue, portfolio simplification and platform consolidation. An enterprise’s priorities are shaped by uptime, security, cost control, application performance, operational continuity and business agility. Those priorities can overlap, but they are not identical and treating them as interchangeable can result in decisions that solve for the vendor’s direction more than the customer’s needs.

This is where CIOs and infrastructure leaders need to step back and ask a more fundamental question: what business outcome are we trying to achieve, and does this change materially advance it?

If the answer is improved resilience, better automation, simplified management, stronger security or a more cloud-like operating model, then modernization may be justified. But, if the answer is simply maintaining continuity or preserving support status, organizations should examine the full range of viable paths before committing to a major platform shift. The right response is not always the most disruptive one.

The cost question is bigger than licensing

Pricing changes often capture the most attention, but the true cost of an infrastructure change extends well beyond the license or subscription line item. In many cases, the larger costs are organizational: the time, risk and disruption that accompany a broad platform transition.

Virtualization platforms sit at the center of complex environments. A major change can trigger downstream work across storage, networking, backup, monitoring, disaster recovery, security tooling, compliance processes and application dependencies. Even when the technical migration appears manageable, the operational validation can be extensive, and the burden on teams can be significant.

Organizations should consider the full cost of change, including staff time, retraining, integration testing, potential downtime, consulting support and hardware implications. Just as important is the opportunity cost. Every major platform transition consumes budget, leadership attention and skilled talent that could otherwise be directed toward security improvements, automation, AI initiatives, customer-facing innovation or other strategic priorities. A more bundled platform may simplify some parts of the stack, but it can also introduce new commercial and architectural constraints.

This is particularly important for enterprises running stable, heavily integrated environments. Many virtualization estates have been tuned over the years to support specific workloads, service levels and operational requirements. Stability is not a weakness to be corrected by default; in many cases it is a business asset. Replacing or restructuring that environment should be justified by measurable business value, not just by pressure to conform to a new delivery model.

The three choices: migrate, modernize or maximize

Most organizations facing a virtualization decision have three broad options, and the right answer may differ by workload, business priority and time horizon.

The first is to migrate. This could mean moving to the vendor’s latest platform, shifting workloads to a hyperscale cloud provider, adopting an alternative hypervisor or pursuing a hybrid architecture. Migration may be the right choice when the current environment no longer supports business needs, when hardware refresh cycles align with broader transformation goals, or when the organization has a clear cloud operating model, budget and execution plan for the next state. But migration should be a deliberate strategic move, not a reflexive response to a platform event.

The second is to modernize in place. This approach keeps the core environment intact while improving the capabilities around it. That may include stronger automation, better observability, improved security controls, more resilient backup and recovery, tighter cost management or more intelligent workload placement.

For many enterprises, modernization does not require a wholesale migration. It requires identifying the gaps that matter most and addressing them with targeted investments. Modernization and migration are not the same thing, and organizations should be careful not to treat them as if they are.

The third is to maximize the existing environment. This option is often overlooked because it sounds less transformational, but it can be the most rational business decision when a platform is stable, secure, performant and well understood. Extending the value of an environment that continues to meet requirements is not inertia; it is intentional lifecycle management. If the platform remains fit for purpose, the better decision may be to maintain it effectively while redirecting budget and talent toward higher-value initiatives.

The right answer may include elements of all three. Some workloads may be ready for cloud migration. Some may benefit from in-place modernization. Others may be best left alone because they are stable, cost-effective and do not justify major reinvestment. The goal should not be uniformity for its own sake, but alignment between each workload and the business value it is expected to deliver.

Start with business requirements

Before committing to any virtualization path, CIOs should bring the conversation back to business requirements. What systems does the platform support? What uptime is required? Which workloads are growing, and which are stable or declining?

What regulatory or compliance obligations must be met? Where does the business need more agility, and where is predictability more important than change? Those are the questions that should shape the roadmap.

From there, organizations can conduct a practical assessment:

1. Which systems truly need to change now for security, compliance, performance or support reasons?

2. Which workloads can remain in place with the right resilience, security and operational controls?

3. Which applications are real candidates for cloud migration, and which are not?

4. Where would this decision increase lock-in or weaken negotiating leverage?

5. Which investments will deliver measurable business value over the next three to five years?

6. What strategic initiatives will be delayed if budgets and talent are redirected to this transition?

This kind of analysis helps technology leaders avoid making decisions based on urgency, assumption or vendor pressure. It also gives CFOs, boards and operating leaders a clearer view of the trade-offs, including where change is necessary, where it is optional and where it may create more disruption than value.

Control the roadmap before the roadmap controls you

The vSphere 9 era is part of a broader shift in enterprise technology: vendors are consolidating platforms, simplifying portfolios and steering customers toward subscription-based operating models.

That approach can offer advantages, including more integrated tooling, simplified procurement and a more standardized operating model. But it can also come with trade-offs, including higher costs, reduced flexibility and greater dependency on a single vendor’s pace and direction of innovation.

Virtualization remains too important to be managed as a reactive upgrade cycle. It underpins mission-critical operations and increasingly shapes hybrid cloud strategy, resilience planning and long-term infrastructure economics. Enterprises should treat it with the same strategic discipline they would apply to any other decision that affects business continuity, cost structure and future flexibility.

As infrastructure models evolve, the message for CIOs is simple: don’t let a support deadline become your strategy. Use it as the moment to define one based on business requirements, financial reality and the level of change the organization needs.

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As AI becomes a greater and more important presence in our everyday lives, the way we interact with the "traditional" internet is changing.

For so long the main way to access the internet, the web browser is now having to rethink how to appeal to an audience which often seeks AI-first interaction - raising new challenges for developers to create the best and most interactive experience.

We spoke to Ajit Varma, Head of Firefox at Mozilla, to find out more.

• The web has always been built on the principles of openness and interoperability, but today's internet is increasingly shaped by a handful of dominant platforms and AI-powered services. What does the 'open web' mean in 2026, and why does it still matter?

The open web matters more today than it has in years because AI is changing how people experience the internet. As a small number of platforms become the gateway to search, content and AI-powered services, it's becoming easier for a handful of companies to shape what people see and how they interact online.

That's why the principles of the open web still matter. Common standards mean developers can build without asking permission from a dominant platform, and users can choose how they access the web rather than being locked into a single ecosystem. Those principles have driven innovation for decades, and they're just as important as AI reshapes the internet.

The challenge now isn't simply keeping the web open as it exists today. It's making sure openness evolves alongside AI, so the next generation of the internet remains competitive, interoperable and accessible to everyone

• AI is changing the way people discover information and interact with the internet. How do you see browsers evolving over the next five years, and what role should they play in helping users navigate an increasingly AI-driven web while maintaining trust and choice?

Over the next five years, you’ll start to see more divergences in how browsers treat AI. Many companies will use the browser to try and push a specific AI agenda but at Firefox we’re focused on how AI can make the tasks a user wants to do in a browser better. This includes things like a personal agent that can help a user accomplish tasks, navigating the web through voice, making the web more enjoyable, and creating new vibe-coded extensions and mini-apps.

We’ll do all this in a way that is user centric. The browser should adapt to the user, rather than the other way round. Some people will want AI integrated into their everyday browsing, while others will prefer to use it for specific tasks. Maintaining trust means supporting those preferences and letting people decide how much they use AI.

Users should be able to choose any model they want including open and local models. The important thing is preserving that flexibility, so people can browse, search and get things done in a way that feels natural to them.

• Privacy has long been a core part of Mozilla's mission, but consumers also expect convenience and highly personalised online experiences. How do you strike the right balance between protecting users' privacy and delivering the features people now expect from modern web experiences?

For me, striking the right balance starts with recognising that every user has different expectations of their browser. While context can make online experiences more useful and personalised, people should be able to understand how their data is used and decide what they are comfortable sharing. By giving users meaningful control over the features they adopt, Firefox can deliver modern experiences while continuing to protect their privacy.

This has guided our phased approach to AI, beginning with practical features such as summarisation, translation, tab organisation and optional AI chat integrations. Before introducing more advanced AI experiences such as Smart Window, we launched AI Controls so people can manage individual features or switch them off altogether.

Firefox is also model-agnostic, so users can choose or switch providers, use local models, or opt out entirely. Even though only a small minority of users disable AI features, people regularly tell us they value knowing the controls are there. Privacy and convenience can coexist when people are able to set the terms of the experience.

• There's growing concern that the future of the web could become fragmented between closed AI ecosystems, proprietary platforms and app-based experiences. What do you see as the biggest threats to the open web, and what gives you confidence that it can continue to thrive?

The biggest risk isn't AI itself, it's concentration. If the same companies control the AI models, the interface people use to access them and the browser they interact through, users lose visibility, choice and ultimately agency. That's a much bigger shift than simply moving from one technology generation to another.

What's encouraging is that the issue is becoming harder to ignore. Developers want open standards. Regulators are paying closer attention to competition. Users are asking tougher questions about transparency and control. The web has always evolved through periods of disruption, but it has remained resilient because it's built on interoperability rather than permission. AI can strengthen that model rather than replacing it with a handful of closed ecosystems. The future doesn't have to be a choice between innovation and openness. We should expect both.

• Developers have always been central to Mozilla's community. As AI transforms software development and web creation, how do you think the relationship between developers, browsers and the open web will change over the coming years?

AI is making it dramatically easier to build software, which means we're going to see more developers – and more people who aren't traditional developers – creating for the web. That's a huge opportunity, but only if they can build on an open platform rather than for a handful of closed ecosystems.

That's why the browser becomes even more important. It's no longer just where websites are displayed – it's becoming the layer where people interact with AI to search, create, organise information and complete tasks. As that layer becomes more powerful, it has to remain open, interoperable and centred around user choice.

For developers, that means being able to build for the web, rather than for whichever company happens to control the dominant model or platform. That's always been one of the web's greatest strengths, and it's something we shouldn't lose as AI reshapes how software is built and experienced.

• If you look ahead to the end of this decade, what do you hope people will say about the state of the web? What would success look like for Mozilla's vision of an open, healthy internet?

Success, for me, is a web that's become more useful through AI without becoming less open. Technology should expand what's possible, not narrow the ways people can access information, build new ideas or participate online.

Innovation should continue to come from everywhere, not just a handful of dominant platforms. The web has always been at its best when anyone with a good idea can build something that reaches millions of people. That only happens when developers have open standards to build on and users aren't locked into a single ecosystem.

The web should still be the best place to build, discover and innovate because it remains accessible to everyone. If AI has expanded participation rather than concentrated power, we'll be heading in the right direction.

• Mozilla is competing in a browser market dominated by much larger technology companies. How do you differentiate Firefox, and why do you believe there's still a commercial and societal case for an independent browser?

We differentiate through independence, choice and accountability. Most browsers are tied to larger technology ecosystems spanning across multiple systems, search, advertising and AI.

Instead, Firefox is powered by Gecko, the only remaining independent browser engine, which gives us the freedom to make product decisions around what benefits users. It ensures that Mozilla is not only advocating for these principles but actively building the underlying infrastructure that makes them possible.

That independence is commercially relevant because there’s such a clear demand for genuine choice. Firefox has been selected through EU browser choice screens more than six million times in two years and those users are five times more likely to remain with Firefox than people who find it organically.

Societally, independent engines prevent one company’s technical and commercial priorities from being the default for the entire web. They also preserve competition, interoperability and the ability to build around different values.

• As regulators around the world seek to promote competition in digital markets, are we at a turning point for the open web, or is there still a risk that power becomes even more concentrated through AI?

There are certainly some encouraging signs that regulation can restore meaningful choice. The impact of browser choice screens in Europe shows that people choose alternatives when they are presented fairly and without being steered towards a default.

However, AI is creating a new concentration risk. If the same small group of companies control the operating system, browser, search engine and AI model, it becomes much easier for them to shape how people access information and which services they encounter.

Regulation needs to keep pace not only with how AI is developed, but with how it's distributed and embedded into everyday products. However, regulation can't do this alone. Interoperability, genuine defaults and strong technical competition will matter just as much.

• Trust has become a key differentiator in technology. How can browser makers earn and maintain users' trust as AI assistants become embedded in everyday web browsing?

Trust starts with being clear about what the technology is doing. AI should be optional, transparent and designed so users can understand what information it can access, how it’s being used and who is acting on their behalf.

That’s the principle behind Firefox’s AI controls. AI features are optional and can be managed individually, while a single setting allows users to block current and future generative AI features. We also use on-device models for features such as Link Previews and Smart Tab Groups, allowing people to receive AI assistance without any data leaving their device.

Ultimately, trust isn't earned through a single feature. It's earned by being transparent about how AI works and consistently respecting the decisions people make.

In a monoculture, a single species or type dominates a system, to the exclusion of all others.

The term comes from agriculture, when a field is planted entirely with one crop. It may be efficient and easy to manage, but a single disease, pest, or environmental change can wipe out the entire yield.

Different varieties have different vulnerabilities, so a field of diverse crops can contain the damage naturally. A monoculture has no such defense.

That same monoculture condition is now occurring in software development.

Many organizations using AI tools for coding rely on a single AI model to both generate code and review it. On paper, this is efficient. But in reality, it creates a closed loop where the reviewer can't catch things that the generator is blind to.

Security flaws go undetected in reviews and suboptimal architectural decisions go unchallenged because the reviewing system shares the same assumptions as the system that produced the code.

When issues arise, engineers look at recent commits, failed deployments, and configuration changes. The assumption that a single AI platform can objectively review its own output is rarely questioned.

The solution to an agricultural monoculture is to introduce different crop varieties with different vulnerabilities, so that no single threat can devastate the entire field. The same is true in AI coding monocultures. Organizations need to introduce AI tools that are genuinely independent of the systems they're reviewing.

Separation of duties

So, why can't the same AI just review its own work?

Asking a coding assistant to review itself is like asking a human to proofread their own writing. The human may catch typos and grammatical errors, but they often won't catch logical inconsistencies or arguments that don't hold up.

This is because the human writer often subconsciously reads what they meant to write rather than what is actually on the page. A model reviewing its own output does the same thing; it evaluates the output against the same patterns and objectives that produced it in the first place.

Similarly, an AI model trained on the same data and optimized for the same signals as the model that wrote the code will carry the same blind spots. It will catch what it's capable of catching, and miss the things that the generating model missed.

Finance figured out this separation-of-duties principle long ago. If the same person who initiates a transaction can also approve it, that opens the door to fraudulent or erroneous transactions. The approval step only works if the approver is independent of the person who initiated the transaction.

Likewise, organizations must introduce a dedicated review tool that operates independently of the system that generated the code. Choosing a different vendor won't automatically solve the problem if the underlying models share the same training data or architectural assumptions.

Genuine independence requires different training data and different signals, and the tool must specifically be built for scrutiny, not completion.

Defense-in-depth

A dedicated review tool is a strong starting point, but separation of duties alone isn't enough. The same principle that argues against a single AI doing everything also applies within the review function itself.

Code review is not a single task. Finding bugs, enforcing standards, assessing risk, and understanding how a change fits into the broader system are separate forms of reasoning, even when they appear in the same pull request.

Asking one agent to handle all of them at once forces tradeoffs between depth, speed, and coverage. In other words, some things will get less attention than they require.

Just as a mature security architecture layers independent controls so that what one misses another catches, a mature review architecture assigns distinct responsibilities to systems optimized for each one.

In practice that might mean one agent focused specifically on security vulnerabilities, another enforcing architectural standards and coding conventions, a third assessing the blast radius of a change, and a human reviewer making the final judgment call on anything flagged as high risk.

Each layer asks different questions and operates on different signals. The goal is ensuring that no single blind spot, including those shared across an AI monoculture, can let a problem through unchallenged.

The shifting role of platform engineering

As AI generates a larger share of production code, organizations will need to designate clear ownership over which AI does what, and ensure the review function doesn't get quietly collapsed into the generation function in the name of efficiency. That responsibility will increasingly fall on platform engineering teams.

The role has traditionally been about making developers more productive by simplifying tooling, maintaining infrastructure, and reducing friction. That work hasn't disappeared, but as developers move from writing code themselves to directing agents that write it for them, platform engineers will need to govern the systems that generate code, not just maintain them.

That means owning the standards those agents follow and ensuring visibility into what’s actually happening across the codebase. Most importantly, it means treating code generation and code review as distinct functions that require distinct capabilities, and resisting the organizational pressure to consolidate them into a single platform because it's simpler to manage.

Organizational memory

As AI handles more of the writing and reviewing, accumulated institutional knowledge gets lost. Why was that architectural decision made two years ago? Which parts of the codebase affect each other in ways that aren't obvious? What broke before and why? AI coding tools don't carry any of that context. They look at the task in front of them, complete it, and move on.

This kind of institutional knowledge used to live in the heads of the engineers who wrote and reviewed the code. As AI takes over more of that work, it needs to be captured in places such as documented standards and recorded review decisions or it simply vanishes. When organizations build that kind of memory into their development process, their systems get smarter about their codebase over time.

That means a review function that doesn't just flag problems in isolation, but learns the organization's standards, remembers past decisions, and understands how different parts of the codebase connect and depend on each other.

Getting ahead of monoculture risk

The AI monoculture risk is what happens when no one asks whether the system reviewing the code is genuinely independent of the system that wrote it.

Microsoft CEO Satya Nadella recently argued that the real opportunity in AI is not in picking the best model, but in building a learning loop where human knowledge and AI capability compound together, and that a company should be able to swap out a generalist model without losing the expertise built into its own systems.

That's only possible if the governance infrastructure is built at the same pace as the generation capability.

The organizations that do that early will be in a much better position than those that wait until something breaks.

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• Microsoft reports Russian APT29 (Midnight Blizzard) hijacking captive portals in hotels and conference centers
• Victims redirected to fake Microsoft 365 logins or bogus update pages, spreading CornFlake and CocoShell malware
• CornFlake steals files, credentials, and device data; CocoShell targets browser cookies, passwords, and Microsoft tokens

Threat actors are taking over Wi-Fi networks in hotels and conference centers and using the log-in portals to steal credentials and deploy information-stealing malware, experts have claimed.

Researchers from Microsoft have published a new report outlining how they spotted Russian state-sponsored actors, known as Midnight Blizzard or APT29, attacking captive portal equipment - networking hardware and software that manages the login page users see before accessing public Wi-Fi.

When connecting to a hotel network, users are often redirected to a page where they must enter their room number, accept the terms of service, and click “Connect” - that redirection is handled by the captive portal.

CornFlake and CocoShell

Microsoft did not explain exactly how this gear is attacked. However, when users try to log in on compromised networks, they may be redirected to a fake Microsoft 365 login portal that steals their credentials.

They may also be redirected to device code phishing pages abusing Microsoft Entra ID authentication flows. Finally, the researchers also saw the captive portals being used to display fake browser and OS update pages that trick victims into downloading infostealers.

So far, MIcrosoft found two malware variants being distributed: CornFlake, and CocoShell.

CornFlake acts as an infostealer capable of grabbing keystrokes and clipboard, running remote shell access, grabbing screenshots, using the microphone and the webcam, stealing browser credentials and cookies, exfiltrating files, and more. It presents itself as a “Cloud Sync Service” while using multiple persistence mechanisms.

CocoShell, on the other hand, is an in-memory PowerShell credential stealer targeting browser cookies, saved passwords, Microsoft 365 and Azure AD tokens, and Wi-Fi credentials.

APT29 is one of the most documented state-sponsored threat actors out there. It’s been active for years and is well-known for its links to Russia’s Foreign Intelligence Service and notable attacks on high-ranking western targets, such as US and German Government officials, as well as SolarWinds and Microsoft.

• Bynario disclosed CVE‑2026‑43760, a macOS RCE flaw allowing root file creation via legacy VNC password option
• Apple patched it July 27, 2026 in macOS Tahoe 26.6 and Sonoma 14.8.8; unpatched users should disable Screen Sharing/Remote Management or the legacy VNC setting
• Reporting was delayed as Apple limited submissions due to AI‑generated bug report overload, but the company reached out directly to fix this issue

Apple has fixed a high-severity vulnerability that allowed threat actors to execute malicious code remotely (RCE), as root, on certain macOS devices - and would have probably fixed the issue even sooner; had it not been flooded with AI slop vulnerability reports.

Security researchers Bynario published an in-depth report discussing finding an RCE flaw on macOS 26.5.2 devices running on Apple Silicon M4 and M5 systems, with System Integrity Protection (SIP) enabled.

According to Bynario, the vulnerability affects Mac devices with Screen Sharing or Remote Management enabled, and with the legacy "VNC viewers may control screen with password" option turned on. For those devices, should a threat actor obtain the VNC password and authenticate to the Mac (no macOS account compromise is required, only the VNC password), they would be able to perform file-transfer operations, with root permissions, due to a logic flaw.

Drowning in the AI flood

The attacker would then be able to create new files owned by root anywhere the system allows.

In the report, the researchers demonstrated creating a valid file inside /private/etc/sudoers.d, granting passwordless sudo privileges, and once that policy was in place, they were able to run commands as root.

In a separate report, the researchers said Apple was forced to limit the number of active bug reports individual researchers can keep open at one time, due to its security teams being flooded with AI slop reports.

Since they already hit that threshold by submitting more than 50 bugs in three weeks, the researchers were unable to report this RCE flaw sooner. However, they explained that Apple reached out to Bynario directly to review, and later patch, the flaw.

The bug is now tracked as CVE-2026-43760 and was given a severity score of 8.6/10 (high).

Apple released the updates on July 27, 2026, addressing the bug on macOS Tahoe 26.6 and macOS Sonoma 14.8.8.

Those who cannot patch should disable the legacy "VNC viewers may control screen with password" option or disable Screen Sharing and Remote Management entirely.

As telemetry becomes central to how modern platforms are built, trained, and automated, its perceived future utility has driven a dramatic increase in collection and retention. A recent study shows telemetry volumes tripling in many enterprises over the past year, with agentic AI expected to drive nearly 10x more growth expected within the next two years.

Clearly, telemetry is starting to look less like infrastructure and more like capital. The mistake is treating it as a pure asset and overlooking the other side of the ledger. Exploding log volumes and rising costs are the most tangible inconveniences, and often the first triggers for concern, but they are merely symptoms.

The bigger problem is that telemetry gradually becomes a system of knowledge that exceeds an organization’s ability to understand, govern, or explain it and, therefore, to reliably bound its cost, risk, and downstream use.

When AI systems both produce telemetry and consume it

Traditional telemetry was largely retrospective. It described what happened. In AI, it starts feeding back into the system. A session log captured to troubleshoot a crash today may become training data tomorrow, evolve into a model feature later, and eventually drive automated decisions without human supervision.

In essence, the same data serves multiple functions throughout its lifecycle, many of which have little to do with why it was collected in the first place.

This sets a loop in motion. The system produces telemetry, the AI consumes it, which produces new signals and predictions, and those create an appetite for still more telemetry. As a result, the value organizations place on data keeps growing, often ahead of any clear understanding of how it will be used.

Once telemetry becomes a form of organizational memory over time, you are past just discussing observability. You are now dealing with governance, cost, and control.

Telemetry retention is fundamentally biased by asymmetry

It’s easy to justify the seemingly “small cost” of storing another terabyte when weighed against the hypothetical cost of discarding it, which can seem enormous, because someone can always argue that the data you threw away might have been a golden ticket.

The prospect of regretting its deletion feels potentially irreversible. Could it have solved a problem? Could it have trained a model or shown you an opportunity you missed? Caving to uncertainty, most organizations just keep everything.

The flaw in this reasoning is a myopic focus on the asset that ignores its liabilities. Every retained dataset carries ongoing storage and governance costs, security and compliance obligations, and discovery risk if you ever land in litigation. Each new dataset can also be combined with existing ones, amplifying its informational value in ways that were never foreseen.

Is there a guarantee that organizations will never regret a deletion? No, but there can be a clear rationale. You knowingly forgo some option value because the expected benefits of keeping the data do not outweigh the costs and risks of holding it. That is a decision you can stand behind later, even if it turns out the data might have been useful.

Asking whether something could be useful someday is not helpful, because almost anything clears that bar. It’s better to ask what specific capability you are keeping it for. If you have a clear answer, retain the data and govern it accordingly.

For CTOs, the blind spot is treating telemetry growth as a scaling problem

As important as ingestion pipelines, storage, query speed, and tooling are, what often catches people off guard is that telemetry turns into a body of knowledge that no single person in the company actually understands. While most teams can tell you where their data lives, far fewer can explain what it reveals once you start putting it together.

The math here is quite unforgiving because exposure does not grow one stream at a time. Every new source can be matched against every source you already have, so the number of possible combinations spirals into something no longer tractable.

Consider clicks, session length, support tickets, device IDs, login records, or approximate location – on their own, none of it is sensitive, and nobody thinks much of collecting any of it. But put them together, and you can reconstruct someone’s daily routine, flag changes in their financial behavior, or predict upcoming life events.

The sensitivity is not attributed to any single stream but is born out of correlation across streams. In reality, most organizations have never fully explored what those correlations could actually enable.

For a CTO, the thing to worry about is not the size of the data, but whether you can still explain what your organization knows and where that knowledge came from. Give every stream an owner, a stated purpose, and a date it expires. If a stream cannot answer what it improves, then it has no business being retained indefinitely. A rule like that will do more to reduce risk than any amount of clever storage engineering.

For CFOs, the blind spot is filing telemetry under infrastructure cost

What makes telemetry different from other infrastructure is that it compounds. More telemetry produces more analysis, which creates new use cases, which extend retention and increase demand for more storage, processing, tooling, and oversight. Before long, what started as a small storage expense has become an ongoing commitment.

The next year’s cloud bill may seem like the thing you should brace for, but at least you can put a number on it. What’s more concerning is that telemetry can become an ever-growing liability with unpredictable cost, risk, and duration. Unglamorous as it may be, the fix is down to the same thing – focus on the bounded business outcomes it produces.

Can you take any category of telemetry you hold and say plainly why it exists, what value it earns, how long it should live, and what happens to it when it stops being useful?

What would a bounded telemetry architecture look like in an AI-driven world?

It starts by overturning the most basic assumption that you have to collect and keep everything until it turns out to be absolutely impractical. Instead, assume most things should not be collected and that nothing stays forever unless there is a reason for it to.

The effect is a change in default behavior. When every stream is set up with a clear purpose, owner, retention policy, and expected outcome, data starts following a lifecycle instead of piling up. Raw events might exist briefly at full detail, collapse into aggregates after that, and, once they are no longer useful for decisions, eventually disappear.

Thinking in lifecycle terms reframes data as something that interacts across systems rather than existing as isolated stores. That’s why the boundary you draw is not around a single database, but around what the combined system is allowed to infer and act on. What you collect should be based on the decisions you want to improve.

The complication in an AI setting is that these systems do need substantial telemetry to work, so it is not as simple as collecting less. The challenge is to collect enough while still keeping control over what the system learns from and what it ultimately produces.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit