• New Spectre variant exploits processor prediction behavior in just-in-time compilers
• Researchers demonstrated practical attacks against Intel-based Linux systems
• Vendors released mitigations as security teams assess performance tradeoffs

The dreaded Spectre vulnerability which had chip manufacturers scrambling for a fix a few years ago, has returned, experts have claimed.

Security researchers from the Vrije Universiteit in the Netherlands and Scuola Superiore Sant’Anna in Italy published a new report, detailing a type of attack they call Branch Target Reuse (BTR).

They labeled it the first practical in-place Spectre v2 attack that targets just-in-time (JIT) compilers - and it's a lot to decompile (pun intended), so let’s break it all down.

Spectre and Meltdown

Modern microprocessors come with a feature called “speculative execution” - they speculate what a program’s next moves might be, and load them in advance, so that when one scenario actually happens, it can be executed rather quickly.

They also monitor recurring patterns and try to remember them, but this also opens the doors to so-called “side-channel attacks”, which allow threat actors to steal information by observing indirect clues from a system (timing, power consumption, etc.), rather than accessing them directly.

Back in 2017, security researchers discovered that speculative execution can be abused via side-channel attacks, giving birth to two vulnerabilities: Spectre and Meltdown. Given how widespread the flaws were (practically all chips were affected), and their potential severity, the entire industry rushed to fix the flaws with software patches. Some succeeded, but the overall effort was more of a fiasco than a success. Many chips were throttled significantly, and some computers were entirely bricked.

Spectre and Meltdown were eventually fixed, but since then, there have been countless copycats and variants. We now have a new variant, called Branch Target Reuse (BTR).

BTR

When a processor wants to “remember” a recurring theme, it stores it in something called the Branch Target Buffer (BTB). At the same time, there is an element called “JIT”. Short for just-in-time, it is a compiler that translates code into native machine instructions during program execution, rather than before it runs. In practice, JIT creates code at memory address X, later running it numerous times. The CPU learns the pattern and tries to repeat it.

So when JIT deletes the code and puts something else at the same address X, that’s where the problems begin, since the CPU still tries to jump to X first. That is called Branch Target Reuse. The CPU will eventually realize that the old habits no longer work, but it’s the moment in between that the researchers managed to exploit.

What makes BTR particularly dangerous is the fact that there is no need for a new malware to exploit it. Threat actors can simply use the fact that machine-code bytes can mean different things when execution begins at a different byte offset.

In the paper, the researchers detail two proof-of-concept (PoC) exploits that can target Intel-based Linux kernels, revealing the root password hash even with the constant binding defense provided by cBPF. The expected leakage rate is 5.7 KB/sec for Intel Raptor Cove chips and 5.4 KB/sec for Lion Cove which, according to The Register, is “slow but enough for an unprivileged user to coax a sensitive password hash out of a vulnerable system.”

Linux kernel developers and Oracle responded with mitigations. The bugs have now gotten two CVEs: CVE-2026-64507 and CVE-2026-64508. Mozilla decided to focus more on site isolation, and while IBPB is most likely effective, it will slow the machine down. “Update your OS and software as soon as vendor patches are available,” the researchers said. “Both the Linux kernel and Oracle have released patches.”

The Branch Target Reuse paper, which can be read on this link, was peer-reviewed and accepted by ACM CCS 2026, a major academic cybersecurity conference, due to take place in mid-November this year in The Hague, Netherlands.

Via The Register

• Dots will decide how to work autonomously toward a goal you set
• They have their own cloud computer and browser for safety
• Only Pro and Business Premium users are getting access for now

OpenAI has launched a new type of always-on AI agent within ChatGPT designed to take ongoing actions for work rather than waiting for individual prompts.

The new GPT-6-powered feature, Dots, means ChatGPT can keep working on tasks between conversations, only bringing things back to the user when they require specific attention or permission.

Designed to be more of an autonomous agent, the company explains that users only need to set goals and boundaries, and the Dots should be able to go about working out what happens next.

ChatGPT's Dots ramp up the 'autonomous' in agentic AI

The fundamental difference for ChatGPT's Dots is that they each have their own cloud computer and browser, giving them an environment within which they can interact with the broader web, use apps, run code and work with other plugins, keeping users' computers totally separate and free of any potential damage an autonomous agent could otherwise take.

A Dot's activity is shown as either being in progress, scheduled or completed, with some example use cases spanning checking a user's calendar to provide them with a morning report, or reviewing information from connected services and apps.

And to make Dots even more like proactive colleagues, the company has already added them into Slack and Teams, with text support also coming soon.

Built-in safeguards also promise to pause or stop a Dot's work if their system detects malicious instructions or behavior.

They're coming first to Pro and Business Premium users at no additional cost, and only work towards a set goal will consume usage limits – conversations with dots are totally free. Dots need to be set up on the desktop app or a browser first, but from there, they'll also be available to interact with on the mobile app.

OpenAI didn't share plans for a broader rollout, but given its previous deployment phases, it's possible that Dots could extend to other subscription types in the future once early users get to grips with real-world use cases and costs.

Artificial intelligence is becoming part of almost every workflow. Developers are using it to write and test code, customer support is turning to it to handle routine queries, and security teams are using it to analyze alerts and support investigations. Digital forensics and incident response (DFIR) is no exception.

But as digital forensics work has evolved over the last few decades, one lesson remains consistent. Tools can support investigations, but investigators remain responsible for the quality of the conclusions they reach. As AI becomes increasingly integrated into workflows across industries, the challenge is understanding where AI tools can assist and where human expertise remains essential – and importantly, where organizations should draw the line.

Where AI adds value

For digital forensics and incident response teams, some of AI's most promising applications are also its most practical.

With 90 percent of criminal investigations and prosecutions now involving a digital element, DFIR teams are being asked to process enormous volumes of data. As of February of this year, more than 20,000 devices remain in the digital forensics backlog in England and Wales alone.

Data is spread across endpoints, cloud environments, communications platforms and user accounts, and AI can help investigators triage. It can process that information more efficiently and help to identify potential leads, spot connections between datasets, and highlight other sources of evidence that may require further examination.

For example, it can help investigators correlate relevant activity across multiple devices, accounts and data sources, supporting the reconstruction of events timelines that would otherwise take days to piece together manually. It can also help practitioners organize information and structure reports, allowing them to spend less time on administrative work and more time on analysis.

Used appropriately, AI can reduce the time spent on manual, repetitive work and allow skilled practitioners to spend more time on the analysis and decision-making that moves their work forward.

The danger of treating AI as an answer

The challenge is that AI often appears so authoritative that people become overly reliant on its outputs.

One of the most important lessons from digital forensics is that every tool has limitations. Experienced investigators understand that forensic tools can be wrong. AI is no different. The greatest risk of AI is using it without having the foundational knowledge needed to recognize when it is wrong.

AI is like your junior analyst on their best day. It can identify patterns, highlight areas of interest and help move work forward. But no organization would allow a junior analyst to make the final decision in a sensitive investigation without oversight and accountability. AI should be approached in much the same way.

Problems arise when organizations treat AI-generated outputs as answers rather than something that needs to be validated by someone with the expertise to challenge them. AI can help you reach a conclusion, but it shouldn’t be the conclusion in and of itself.

Knowing where to draw the line

There are many areas where AI can accelerate and enhance work, but there are some areas where AI should never be used. Organizations need to pay attention to where those boundaries lie.

In digital forensics, AI can help identify information that warrants further examination. It can support analysis and help investigators work more efficiently. But it shouldn’t be blindly trusted to reach investigative conclusions or determine innocence or guilt.

Those decisions carry legal, professional and life-changing consequences, making human judgement and accountability non-negotiable.

The same principle extends beyond digital forensics. Organizations in every sector are now deciding how much responsibility should be handed to AI. The answer will vary depending on the use case, but the underlying question remains the same - what safeguards are in place to prevent people becoming overly reliant on AI outputs, and how will they know when those outputs are wrong?

Building frameworks for responsible adoption

These are exactly the questions currently being addressed by the new practical frameworks for the use of AI in digital forensics and incident response. They encourage organizations to consider how much risk a task carries, how outputs will be reviewed, and where human oversight should remain mandatory.

Using AI in DFIR requires strict governance because the stakes are so high. Mistakes are taken seriously when someone’s life is on the line, or a wrongful conviction could take place. However, it shouldn’t take consequences this severe for organizations to work towards protecting the integrity of their decision-making and ensuring humans remain in the driving seat.

Rather than racing to embed AI into every process, organizations should establish governance frameworks first – including what AI should never be in charge of – then start with low-risk use cases where outputs can be easily reviewed or reversed. From there, they can expand capability and explore how AI can help their teams do their jobs better, acting as a force-multiplier rather than the final arbiter.

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Last October, a massive DDoS attack suspected to be linked to the Aisuru botnet disrupted some of the biggest names in gaming. Steam, Riot Games, PlayStation Network and Xbox experienced simultaneous outages, affecting games including Counter-Strike, Dota 2, Valorant, and League of Legends.

Researchers investigating the event estimated that the attack may have peaked at 29.69 Tbps, which would make it one of the largest DDoS attacks ever recorded.

These are top-tier, sophisticated technology companies with significant resources devoted to staying online. Yet even at this level, major disruptions still happen. That raises a more useful question than simply how large the attack was: Why can the DDoS defenses that organizations have invested in heavily, still leave them exposed when it matters most?

Why Availability Outages are a Revenue Problem in Gaming Gaming accounted for 19% of DDoS attacks observed by Gcore in H2 2025, making it the third-most-targeted sector, behind technology and financial services. Overall DDoS activity in Gcore's dataset increased 150% year over year.

For a gaming company, availability is arguably the biggest part of the product. If players cannot authenticate, stay connected, or play at all, users, and therefore revenue, go down. The consequence of extended disruption is a lack of trust, affecting the bottom line.

For example, an outage during a major launch or tournament for a gaming company is catastrophic because it’s occurring exactly when they’ve put time and resources into ensuring maximum player demand. A failed launch window means marketing dollars, player acquisition, and revenue all take a hit.

How configuration drift creates DDoS protection gaps

Configuration drift is the growing mismatch between deployed DDoS controls and the changing environment those controls were configured to protect. Gaming is especially vulnerable to this because the product changes constantly.

Studios are always shipping updates because players expect it. To stay relevant and exciting (and profitable), they need to always be launching a major update or new title, or expanding to a new region or user base.

Constant changes mean constant risk. Every new change is a chance for a system or safety net that was in place during deployment to fail. For example, let’s say a studio launches a new matchmaking service. Traffic to that service may be routed differently, but the mitigation policy protecting the original service doesn't automatically follow it. Now you’ve got a gap. The DDoS protection may be the same, but the environment around it isn't.

In addition to product, feature, or service releases, any change to the IT ecosystem may have an impact on the environment the security controls were configured to protect. Those changes accumulate. This is called configuration drift. And it becomes the default, not the exception.

Why point-in-time DDoS testing cannot keep pace with continuous change

This problem is especially concerning in gaming, but any company operating a fast-moving digital service faces some version of it. Point-in-time red team testing alone can't provide current evidence about DDoS protection across a continuously changing environment. If point-in-time testing happens in January, and then the environment materially changes in February, what exactly does the January result tell the team in August?

Despite this, point-in-time testing still has real value: it tells a studio how well its people and procedures respond on the day the test is run. The issue is that the change is continuous. And the release and testing cycles are running on different clocks.

A game studio might make meaningful production changes every week while its formal testing schedule runs, say, quarterly or semiannually. Every change between those two points potentially creates a gap between the environment that passed the test and the environment players are actually using.

Point-in-time testing was never built to validate configuration coverage across an entire, constantly changing attack surface. For gaming companies, that leaves too much time between an environmental change and knowing whether existing DDoS protections still cover it.

What gaming security teams should change

Because gaming infrastructure is constantly changing, DDoS validation needs to be ongoing rather than relying on scheduled point-in-time testing or waiting for major events like a new launch or a tournament. This gives security teams current evidence that their protections continue to work even as infrastructure and services evolve.

That ongoing process should account for changes to public-facing services and how existing protections respond to them. Did this change alter what is exposed, how traffic reaches it, or how existing DDoS controls respond?

When ongoing validation identifies a DDoS gap, teams should investigate how it emerged rather than treating it as an isolated vulnerability to patch. If a configuration change exposed one service, the same change or pattern may exist elsewhere in the environment. Finding the vulnerability should prompt teams to look for similar exposure across other services.

What this means for any always-on digital business

Players don’t care about protection deployments, whether their preferred game passed its last point-in-time test, or if an outage is caused by the technology itself or an outside attack. The game works, or it doesn't.

Gaming makes the consequences of these vulnerabilities unusually visible. When availability is part of the product, even a small weakness can become a business problem the moment attackers find it.

For any always-on digital business, leaders need to pay attention to how their defenses can keep pace with the environment they are responsible for protecting, especially as it changes. The same lesson applies to any company whose customers expect its digital services to be always available.

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Historically, the cybersecurity conversation in banking and fintech has centered on detection: can we spot the intrusion, flag the anomaly, catch the fraud pattern before it does damage? We developed incredibly sophisticated tools that can sift through thousands of ‘signals’ every time a customer makes a payment, ranging from how long it took them to type their card number to, sometimes, their social media activity.

That framing assumed a certain tempo: human attackers probing and exploiting at human speed, or automated scripts operating faster but without the intelligence to adapt dynamically or discover novel vulnerabilities. AI changes that assumption.

PYMNTS reported this month that OpenAI could not rule out its highest cybersecurity warning level for its upcoming Astra model, a threshold that signals a model may be able to independently discover and develop working zero-day exploits, or carry out attacks with minimal human direction.

Around the same time, the International Monetary Fund reached a similar conclusion in a note on artificial intelligence and cybersecurity in the financial sector.

The IMF's argument is the one banks should be listening to: AI does not need to invent a whole new type of attack to change the risk equation. AI-enabled attacks may not look fundamentally different from attacks carried out by humans.

What changes is their speed, scale and capacity to adapt. It changes the equation by accelerating vulnerability discovery and exploitation across shared technologies, turning what used to be isolated incidents into correlated disruptions that hit several institutions at once.

Why AI cyberattacks are so dangerous

Banking has never run on bespoke, siloed technology. It runs on shared foundations: core banking platforms, cloud infrastructure, payment rails, identity systems and a long chain of third-party software. Few of these systems are built entirely from scratch. Modern financial infrastructure relies on layers of commercial software, cloud services and open-source libraries.

That makes development faster and can strengthen security through shared scrutiny, but it also creates common dependencies. In the wrong circumstances, that can become analogous to thousands of doors relying on the same key.

An attacker who needs weeks to find a working exploit gives defenders time to patch, monitor, and respond. An AI system can potentially find that same exploit in seconds, and in the most autonomous cases execute a multistep attack with limited ongoing human involvement – 27 seconds is the current record.

The IMF frames this as a dual-use problem: the same capabilities that let AI find vulnerabilities faster for defensive purposes are the ones that let it find them faster for offensive purposes.

This is not a hypothetical for payments specifically. Payment infrastructure has so many overlapping systems at the intersection of common software, cloud dependency, and dense third-party integration that a single exploit becomes an ecosystem-wide issue.

A vulnerability in a widely deployed component can therefore propagate risk far beyond a single institution, particularly where common cloud services, software libraries or third-party platforms are involved.

Why prevention alone isn’t enough

For a long time, resilience strategy has leaned heavily on prevention: patch faster, test harder and review more code before release. All of that still matters. But when discovery and exploitation can happen faster than a conventional patch cycle, prevention alone is no longer sufficient.

The answer is a shift from periodic defense towards continuous resilience: architectures designed to detect, isolate, contain and recover while an attack is unfolding.

The IMF's recommendation points the same way the PYMNTS piece does: institutions need architectures built to limit the blast radius of a successful breach through segmentation, disciplined access controls, zero-trust design, and closer oversight of third parties. Detection, containment, and recovery need to operate at a speed that is comparable to the speed of the threat, not the speed of the last board-approved incident response plan.

That has equally important implications for how banks govern AI within their own security operations. AI tools will increasingly play a role in detecting vulnerabilities and responding to threats, but the degree of autonomy granted to those systems must be an explicit governance decision. Institutions need clear boundaries around what AI can access, what actions it can take independently and where human intervention remains mandatory.

Why boards need to make AI cybersecurity a priority

This is where the conversation has to move from the security team to the boardroom. Segmentation and access control are technical decisions, but the appetite for AI autonomy in critical infrastructure is a governance decision. Who decides what an AI system is permitted to touch? Who is accountable when it acts on a high-level objective in ways nobody fully anticipated? How quickly can exposure be contained once it is identified?

There is no vendor, platform, or piece of infrastructure that resolves this on its own. The institutions that manage this transition well will be the ones that treat architectural resilience, not just threat detection, as a standing governance priority, and that know precisely what their systems, and the AI operating within them, can reach.

The capability curve is not waiting for banks to finish their next audit cycle. Resilience now has to be designed for the speed of the attack it is meant to withstand.

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For years, enterprises have treated cloud adoption as a milestone of digital transformation. Today, however, cloud adoption alone is no longer enough. As AI moves from experimentation to execution, cloud maturity is increasingly the difference between AI that scales and AI that stalls.

According to the research we recently carried out with more than 2,300 senior decision-makers globally, that gap is becoming increasingly visible. Many organizations have invested heavily in cloud, yet far fewer have embedded it deeply enough into their operating models, governance and business strategy to fully support the next wave of AI-driven transformation.

That distinction matters because AI has fundamentally changed the role of cloud. It is no longer simply infrastructure. Cloud has become the execution layer for AI, where data, applications, automation and security come together to enable faster decisions and better business outcomes.

The cloud maturity paradox

Cloud is now nearly ubiquitous across the enterprise. Yet widespread adoption has not consistently translated into the innovation, modernization and business value organizations expected. Our research also shows fewer than half of organizations are satisfied with the cloud’s impact on innovation or their progress in IT modernization.

That disconnect reveals a deeper truth: adoption has outpaced maturity. Many organizations initially approached their cloud journey by focusing on cost-efficiency, migration speed or infrastructure modernization. Those priorities delivered short-term gains, but not always transformation – and the needs of the AI era have brought that to the forefront.

The organizations pulling ahead approach cloud differently. They invest in modernization, architectures and governance models that enable business reinvention, positioning them to scale AI and generate measurable business outcomes.

Perhaps the biggest misconception surrounding AI is that better models alone create better outcomes. AI amplifies the strengths or weaknesses of the cloud foundation beneath it. Organizations with mature cloud environments can scale AI faster, govern it more effectively and extract greater business value. Those without that foundation often struggle to move beyond isolated pilots.

Almost all organizations report that AI is increasing demand for cloud capabilities, yet a large majority also acknowledge that current levels of investment are insufficient to support their ambitions.

Legacy systems holding enterprises back

One of the greatest barriers to AI success is continued reliance on legacy systems. Around half of the organizations in our research say outdated applications and data platforms are holding back innovation.

For years, legacy technology was primarily viewed as an efficiency challenge. Today, it has become an AI challenge. Organizations can no longer separate modernization from AI strategy because the limitations of legacy systems now directly define how far AI initiatives can scale and how much business value they ultimately deliver.

Legacy architecture is often poorly suited to AI's demands for cloud-native, data-rich and real-time environments. Meaningful modernization requires redesigning applications, data and workflows—not simply migrating them—so organizations do not allow yesterday's architecture to define tomorrow's AI capabilities.

Architecture choices now carry strategic weight

Another shift is the growing importance of cloud architecture decisions. Organizations are no longer selecting cloud infrastructure based solely on technology requirements—they are deciding where AI workloads should run and how they can be governed and scaled across the enterprise.

The data we collected shows strong momentum toward hybrid, multicloud and sovereign cloud strategies. In fact, sovereign cloud adoption is expected to rise by 50% over the next two years as organizations place greater emphasis on data sovereignty, resilience, regulatory compliance and control.

AI workloads are compute-intensive and unpredictable, making decisions about where AI workloads run more important than ever. Cloud architecture decisions increasingly determine whether AI initiatives can scale efficiently, remain cost-effective and deliver business value.

Cloud architecture is no longer simply an infrastructure decision; it has become a strategic business decision.

The rise of platform-led operations

As infrastructure complexity increases, so does the need for a different operating model.

AI-enabled enterprises are dynamic, distributed and constantly evolving, making manual cloud management increasingly challenging.

Agentic AI raises the stakes further. As AI agents begin to act more autonomously across applications, data and workflows, platform-led operations can provide the visibility and control needed to manage them at scale.

Platform-led cloud management enables organizations to embed governance into systems, automate routine decisions and maintain consistency across an organization’s entire environment. It also helps address one of the most common challenges: cloud cost management.

More than half of organizations report difficulties in controlling cloud costs, particularly as AI and agentic workloads introduce new and often unpredictable usage patterns.

Platform-led operating models provide the visibility, governance and automation organizations need to manage cloud in the era of AI while continuously optimizing cost and performance.

They connect modern applications, industry cloud capabilities, AI agents and data into a coherent operating environment, creating greater visibility across business processes and enabling organizations to manage increasingly autonomous AI systems with greater consistency and control. More importantly, they help organizations realize operational savings they can reinvest in modernization and innovation initiatives.

Security as a foundation, not a constraint

The data we collected shows that security is now the top cloud investment priority, reflecting a broader shift in how organizations think about modernization and AI readiness. It also reveals that governance remains uneven.

Organizations with higher levels of cloud maturity are far more likely to define clear security roles and responsibilities backed by formal governance processes, giving them greater confidence in their cloud security posture.

The research also found that risk, governance and compliance are expected to be the biggest challenges as organizations deploy agentic AI over the next 12 to 18 months. As AI systems become more autonomous, security and governance can no longer be treated as an afterthought. Instead, governance must be built into cloud architecture and operating models from the outset.

As cloud, AI and security ecosystems become more distributed and increasingly reliant on third parties, security can no longer be managed in isolation. Organizations need an enterprise-wide approach to risk that brings together controls, tools and responsibilities across providers, platforms and partners, with clear governance and accountability built in from the start.

Far from slowing innovation, strong security foundations enable organizations to innovate with confidence. When identity management, clear data protection policies, continuous monitoring and governance are embedded from the outset, organizations become more resilient—and better positioned to innovate.

From adoption to value creation

Taken together, these trends point to a broader shift in how organizations should think about cloud.

Organizations that will lead in the AI era are those that align their cloud and AI strategies, continue modernizing applications and data, make deliberate architecture decisions, embed governance and security from the outset, and treat cloud as a business operating model for continuous innovation rather than simply a technology platform.

AI is accelerating faster than enterprise cloud maturity, creating a widening gap between organizations prepared to scale AI tools and those that are not.

Ultimately, the question is no longer who has adopted cloud. It is who has matured it into the execution layer for AI — a platform for innovation, resilience and business value.

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• The RAF has jammed hostile satellites from the ground for a year
• Losing GPS would cost the British economy about £1.4 billion every day
• Jamming could stop hostile satellites from tracking Britain's nuclear-armed submarines

Britain's Royal Air Force (RAF) is using ground-based electronic warfare equipment capable of interfering with satellites during sensitive military operations.

The technology can use powerful electronic signals to disrupt satellite communications and navigation, while lasers can interfere with satellite sensors.

British defence officials describe the country's terrestrial electronic warfare capability as world-leading, with the equipment already used for about a year against potential threats.

Ground-based systems can disrupt hostile satellites

The equipment could be used when hostile satellites attempt to monitor British military activities or track strategically important assets.

Defence officials said the system could prevent adversaries from observing Britain's nuclear-armed submarines or sensitive special forces operations.

Such capabilities are becoming increasingly important as military forces depend heavily on satellites for communications, surveillance, navigation and other operations.

RAF head Air Chief Marshal Sir Harv Smyth said adversaries were carrying out increasingly irresponsible and provocative activities involving space-based systems.

In May 2026, British forces reportedly tracked five Russian satellites moving close to two Finnish commercial satellites.

Smyth also linked Russian satellite activity with GPS disruptions recorded across Europe and Canada.

Britain operates a small number of military satellites supporting surveillance and communications, while working closely with the United States Space Force.

The ground-based electronic warfare system therefore provides Britain with another means of responding when hostile spacecraft threaten military activities.

New RAF unit and economic risks

RAF has also established a dedicated Space Effects Squadron, which is responsible for defending British space assets against hostile activity and related threats.

The Ministry of Defence says the squadron will focus on disrupting, degrading and denying hostile threats affecting British space capabilities, protecting satellites that support several critical military functions, including missile warning, communications, and navigation.

The government's concern extends beyond military operations because disruption to satellite services could affect major parts of Britain's civilian economy.

Defence Secretary Wes Streeting said losing GPS services could cost the British economy about £1.4 billion each day, amounting to roughly £10 billion every week if disruption continued at that level.

Streeting said everyday services depend on satellites, including weather forecasting, energy systems, financial transactions, satellite navigation and mobile communications.

A prolonged loss of satellite services could therefore affect businesses and public services far beyond defence operations.

The United States has also confirmed the deployment of an unspecified weapon in space to protect its satellites.

Russia and China have criticised the U.S. move, while Washington has accused both countries of developing space-based weapons.

Those capabilities reportedly include electronic jammers, lasers and projectiles designed to interfere with or damage satellites.

Via BBC

• Report claims production of budget smartphones could drop by 40% by 2030
• The increasing costs of RAM and chipsets thanks to the AI data center boom are key factors in the forecast
• In addition, basic product lines aimed at the lower end of the market are becoming less profitable as minimum spec phones are overlooked in favour of portable media production devices

It could be time to start saving for your next phone, thanks to the increasing demand for RAM, processors, and SoCs, new research has claimed. The AI data center boom has driven various price rises across the electronics industry over the past few years, and now it seems that the days of the budget smartphone could be numbered – and at best, they may be hard to come by for a while.

Forecasting by Counterpoint Research estimates smartphones below the $200 figure could shrink by 40% by 2030 - which equates to the market shrinking by 230 million devices.

While mid-range, premium, and ultra-premium devices are less affected by this, those on lower incomes – particularly in poorer countries – could find internet access is off-limits.

A sharp contraction

Counterpoint Research’s study suggests the smartphone market will change considerably as higher device specifications justify higher price points in a landscape where component costs are climbing.

This “structural shock” can be traced to the current downturn, and will likely lead many people to keep their phones for longer (although those who can afford it will upgrade). It’s a scenario that could see the extension of replacement cycles by consumers and vendors alike (perhaps persuaded by market demand) and the refurbished handset market could also benefit. Handset insurance plans may also see longer subscriptions, too, further supporting the refurbished market and legacy stock supplies.

Over recent years, premium smartphones have driven the market, and the data apparently suggests this will continue. The prediction doesn’t suggest budget phones will go away entirely, but there could be new opportunities from the used phone market, dumbphones (like the Commodore Callback), and even niche/DIY solutions.

Connectivity risk

More premium phones and higher specced mid-range devices sounds good at face value. However, it does mean that regions where budget phones are vital to communications and internet access are likely to find it more difficult to achieve mobile internet connectivity.

“The smartphone market will recover in unit terms, but the affordable segment will not return to its previous standing,” said Yang Wang, a principal analyst at Counterpoint Research, noting the market size will be similar overall, but different in value.

“The opportunity now shifts toward better-specced mainstream devices and premium products, although higher tiers cannot absorb all the lost volume,” Wang adds, “there is also a wider connectivity risk, as the loss of affordable new smartphones could make it harder for first-time users in lower-income markets to come online.”

Counterpoint Research underlines that it expects the smartphone market to regain its 2025 volume by 2030, but with a much smaller budget segment.

• Japan plans to hire millions of AI robots as its working population shrinks
• One domestic startup plans to build 100 workplace robots by 2027
• Japanese insurers are preparing for accidents involving increasingly capable workplace robots

The Japanese government has set a goal of placing 10 million robots driven by artificial intelligence into workplaces before 2040, hoping to offset a rapidly thinning workforce in its aging population.

By 2040, United Nations medium-variant estimates place Japan's population at 112,158,304, which is 6.2% lower than the 119,584,122 projected for 2030.

The median age would reach 54.0 years, with 35.3% of people at least 65 years old and only 10.4% of them children below 15.

Machines move into offices and wards

Hospitals and factories in Japan already struggle to fill positions, which is why a growing number of employers are turning to machines for daily tasks.

Startup Zeals recently introduced the D1 in Tokyo, a wheeled machine with a humanoid form that costs from 5 million yen (about $32,000).

The indoor unit stands 129 cm tall, recognizes faces through a camera, and each of its two arms can lift a maximum of 5 kg.

Zeals intends to build 100 units by March 2027 and accumulate 10,000 hours of running time, selling through dealers and leasing partners.

The company writes its own software and buys components from American and Chinese suppliers, but it eventually hopes to use only Japanese parts.

In late August 2026, a hospital in Nagoya, Aichi Prefecture, trialed the robot for leading visitors and clearing tables in its dining space.

“It is important to gather training data and ensure that it is actually useful in real-world settings,” said Masahiro Shimizu, CEO of Zeals.

Kakuichi, a Nagano firm that sells steel and systems for managing energy, will onboard a humanoid machine among its new graduates in April 2027.

Play Robotics, a firm that studies humanoids, is helping Kakuichi install and run the Chinese-built robot called Miyazawa Noah.

Kakuichi intends to assign the robot to manufacturing, sales and other departments so that it builds skills by doing real work.

“Developing a humanoid robot is also about fostering employees who will make full use of it,” said Riu Tanaka, CEO of Kakuichi.

Insurers prepare for the dangers that the machines create

Mitsui Sumitomo Insurance has joined forces with Zeals to sell policies built for D1 users, counting on demand from hotels, hospitals, factories and logistics firms.

It acknowledges that the use of these robots will be associated with some mishaps, whether physical or cyber-related.

"There are risks of causing harm to people or property, as well as concerns about data breaches in cyber-attacks," said Masaki Oshiro, manager of the 1st Branch Office at the insurer's Tokyo East Branch.

In July, Mitsui Sumitomo Insurance acquired shares in a Japanese firm developing physical AI, which lets robots learn new tasks on their own.

The purchase was meant to give the insurer a closer look at how such systems behave and what could go wrong with them.

Buying into a developer while already selling policies suggests the insurer is still learning what these robots can do and how often they fail.

So, while Japan makes moves to boost its workforce with robots, the performance and impact of these robots on its economy is uncertain.

Via Japan Today

• Eric Schmidt's Perennial Autonomy wins Gauntlet II deep strike with 80.1 points, with a massive 9.2-point margin over the competition
• All four entrants tied directly to Ukrainian drone makers, including a Gauntlet I order winner, missed both top-five lists
• Ukraine's SkyFall still placed fourth indirectly in deep strike through British partner Skycutter, and Defender Media says another hidden Ukrainian firm also performed well

Perennial Autonomy, the drone company founded and funded by former Google CEO Eric Schmidt has topped the deep strike category of Gauntlet II, the second round of the Department of War's Drone Dominance Program.

The contests, which center around a straightforward goal of finding the most effective but cheap one-way attack drones in bulk, attract competition from across the globe as small and large players alike vie for the top five positions in each category that would allow them to secure production orders.

One of these tests is called "Deep Strike," and Perennial Autonomy not only delivered an impressive result, but it also kept some of its fiercest competition in check by a considerable margin.

The bigger surprise: who is missing?

While Perennial Autonomy's win and 9.2-point lead over the next-best competitor are impressive, they aren't a record by any measure: 4th-place holder Skycutter won Gauntlet I in March 2026 by about 12 points, showing that results can change quickly in an industry where innovation happens at a breakneck pace.

The more surprising part isn't Perennial Autonomy's impressive showing, but who failed to make it through.

Four entrants tied directly to Ukrainian drone makers reached the trials at Fort Carson, Colorado: Stellarion and Grim Tech under their own names, F-Drones through its US subsidiary UDD Tech Corp, and General Cherry through Wilcox Cherry Defense, a joint venture with US firm Wilcox Industries, according to a field breakdown by Challenging Defence Markets. Stellarion and UDD flew deep strike, while Grim Tech and Wilcox Cherry flew close quarters.

None of these are small-time players in Ukraine's burgeoning homegrown defense industry, however; Grim Tech, for example, claims to be delivering as many as 350,000 drones to the war effort monthly.

It does, however, raise the obvious question many would ask: why aren't any Ukrainian firms in the top 5 of the Deep Strike tests?

The scores are a sharp reversal from earlier in the year. At Gauntlet I, held at Fort Benning, Georgia, UDD was among the 11 companies that secured orders, according to reports. By mid-August, UDD was one of only three Gauntlet I vendors to complete deliveries, alongside Neros and Griffon Aerospace, according to Inside Unmanned Systems. Delivering on time evidently did not translate into a place on the new boards, where the merits of one's newest tech evidently trump past reliability.

On closer inspection, Ukraine's presence is quieter but more persistent than one might initially assume. This is because, while Skycutter is headquartered in London, the drone that won Gauntlet I was the Shrike 10, developed by Ukraine's SkyFall.

DroneXL summed up the SkyFall connection this way: "Ukrainian engineering did place, under a British flag." Defender Media's sources also say at least one more Ukrainian company performed well through a Western partner, but the firm has declined to comment, and the claim has not been independently verified.

Ukraine also had a hand in the current winner, albeit indirectly; Perennial began in 2023 as White Stork following Schmidt's meetings with Ukrainian officials, according to Inside Unmanned Systems, and has since traded as Project Eagle and Swift Beat. Neros won a 2025 contract to supply 6,000 Archer FPV drones to Ukraine and later opened a Kyiv office that year. The jamming deployed for the contests was also partly Ukrainian.

This does beg the question: why are companies from what is arguably one of the largest users and producers of attack drones falling short of the competition despite having the most battlefield experience in play?

The answer might be a little more unglamorous than one would like: Stellarion, the only one of the four to comment publicly, blamed logistics rather than its drones. Export permits and shipping dragged on so long that, as its representatives told Defender Media, "This complicated the integration of products with American components, including warheads."

This adds to friction already built into the program: The Pentagon cannot buy directly from Ukrainian suppliers; contractors must be US legal entities registered with the Defense Counterintelligence and Security Agency, and significant foreign ownership brings extra conditions such as US-citizen leadership. Motors and batteries from certain countries were barred from August, and finalists had about five weeks to deliver 120 drones fitted with live payloads, underscoring that companies linked to the US industrial base would have a de facto edge.

Companies whose supply chains were built around Ukraine's front line therefore had the most to rebuild in the least time. That is a plausible explanation, not a confirmed one, and it may have cost them time, resources, and, down the line, a top-5 score in the Department of War's Gauntlet II.

As a result, one could argue that for Ukraine's drone makers, the next round (Gauntlet III) may be decided as much by permits, paperwork, and parts lists as by what actually happens during the tests themselves.

• Russian underwater robots could reach offshore accidents within hours, not days
• USC says robotic systems could handle most routine underwater inspections
• The robots inspect hulls, propellers, and rudders while ships remain stationary

Russia's United Shipbuilding Corporation (USC) has introduced a new line of underwater robotic systems built for oil, gas, and maritime repair operations.

The robots were shown at a recent St. Petersburg conference dedicated to technologies for the energy, logistics, and shipbuilding sectors.

Corporate representatives described the systems as part of a broader push toward domestic marine robotics, according to the company's press service.

A faster alternative to traditional vessel response

Officials from USC's engineering division noted that mobilizing a standard vessel after an accident can take between 4 and 14 days under normal conditions.

A resident marine robotic complex, by comparison, is said to respond within just a few hours of an incident occurring nearby.

That speed advantage is central to the pitch — the corporation claims the approach can cut financial losses tied to drilling platform downtime by as much as 85%.

The same systems are also expected to take over roughly 80% of routine monitoring, inspection, and minor repair work currently handled by human crews or slower vessels traveling from distant ports.

Engineers involved in the project argue that keeping a robotic unit permanently stationed near offshore infrastructure removes the delay caused by weather, distance, and vessel scheduling conflicts.

These robots can also be used to check a ship's condition while it sits at anchor or alongside a pier waiting for its next assignment.

It examines the hull, propeller, and rudder assembly, flags any visible damage, and streams footage back to a human operator in real time.

Vessel owners can then reach repair decisions faster, while repair yards get an early estimate of what work will be required before the ship even arrives at the dock.

USC representatives said this cuts down on guesswork once a vessel finally reaches a repair facility.

Software platform ties navigation, sensing, and AI together

Beyond the underwater vehicles, USC unveiled a unified software platform meant to serve as the backbone for these operations.

The platform folds navigation, hydrographic data collection, communication links, and AI tools into one continuous system rather than separate tools.

The company also displayed uncrewed surface vessels intended for logistics runs and hydrographic survey missions along Russia's coastal waters.

USC additionally outlined concepts for defending port waters using barriers built from booms and netting, plus modular systems designed for seismic exploration work.

Those exploration complexes are described as capable of coordinating groups of up to 1,000 individual devices operating together but the company did not reveal details on how such coordination would function in practice.

USC’s long-term goal is to move these robotic complexes into serial production, with cost control serving as the guiding design principle behind every future model built.

Via 1.ru

• A macOS flaw could give hostile apps the highest system privileges
• An NFC tag could trigger an Android app without owner approval
• Android flaw lets apps change Wi-Fi settings without requesting extra permissions

Russian cybersecurity firm Positive Technologies has claimed it discovered 11 security flaws affecting Android and Apple devices.

The company, which is currently under American sanctions, gave the findings to Russian news agency TASS.

Nine of the flaws affected Apple devices and software, while two affected Android, including Pixel phones, and were reportedly rated high severity.

How a tag and an app exposed Android phones

The first Android flaw let attackers use a crafted NFC tag to fetch, set up, and run an app while the owner approved nothing.

The second flaw allowed an app already on the phone to alter network settings, including joining a chosen Wi-Fi network, without any extra permissions, and also let the app add a certificate or adjust proxy parameters, and neither action required the phone owner to confirm anything.

Google resolved both Android flaws in its September 2026 patches, so devices which have installed those patches should no longer face either problem.

The company describes the tag flaw as especially hazardous since holding a phone near the tag suffices to trigger it.

There was no mention of specific Android versions or Pixel models that were vulnerable, so the number of exposed devices remains unknown, but to be safe from malware attacks, get the latest security patch.

What some Apple flaws allowed

According to TASS, the nine Apple flaws covered higher access rights, privacy exposure, and weakened data safeguards.

One macOS flaw allowed a hostile app to obtain the highest level of control over the computer, and another exposed information the system normally protects. The keys used for access could be deleted without the user approving the action.

Another flaw was found inside the kernel of the operating system and could cause a device to fail or corrupt data held in memory.

Apple has released patches for the flaws, although the company did not say which operating system versions carry the fix.

Devices that never received an update stay exposed to every flaw the firm described, whatever patches the vendors have issued.

Owners of older phones and computers that no longer receive vendor updates face the most uncertainty, because a fix never reaches them.

Android owners should check their software version in system settings to confirm the September 2026 patches arrived on their devices.

Neither Apple nor Google acknowledged the Positive Technologies report as expected, but they both released patches fixing these flaws, which implies that the report is legitimate.

Via 1.ru

• Russia fears Google’s developer checks could eventually restrict access to domestic applications
• Google will begin Android developer verification across four countries in September 2026
• Users may face a 24-hour waiting period before completing some installations

The Russian Ministry of Digital Development has warned that Google's forthcoming Android developer checks could keep Russian applications off Android phones over time.

Minister Maksut Shadaev said the rules may stop Russians from freely installing mobile programs, and developers already dropped by online marketplaces face the greatest exposure.

Google has not announced any ban on Russian developers, but the ministry is assessing the dangers and preparing proposals with industry groups to limit possible damage.

What Moscow fears from the new checks

The ministry objects to the technical framework since it could let Google influence which software from Russian programmers reaches users.

The scheme requires authors to prove who they are and register each application, including programs obtained through independent shops or installed from standalone package files.

Google bought the startup that created Android in 2005, and the system rests on freely available code that any mobile phone maker is allowed to adopt.

Yet many devices still depend upon Google's cloud services and must gain approval from the firm, giving it substantial sway over huge numbers of phones.

Alternative app marketplaces in Russia cover only part of what users want, so any extra installation hurdle would fall on people with few real substitutes.

Steps for users and the rollout timetable

Under the scheme, any user installing software from developers lacking verification must change phone settings and then confirm with a fingerprint or lock passcode.

The user must then restart the phone, wait 24 hours, and reopen the configuration menu to continue with the installation.

Finally, the user chooses whether the exemption lasts seven days or has no end date, so one unofficial program will take over one day to install.

Google’s rollout of the checks will commence on September 30, 2026 in Thailand, Singapore, Brazil and Indonesia.

Although the company has not listed Russia among countries facing any restriction aimed at its programmers, it plans to expand the checks to more countries next year.

Therefore, the Russian government is already anticipating that it will be on the list sooner or later.

Google explains that the checks are a safeguard against harmful programs, though that claim still awaits proof from concrete results from the first round of checks.

Nearly 56 months after the war in Ukraine began, Moscow is taking a proactive step in preparing developers for things to come (they may never come).

While Google’s security rationale remains plausible, a system verifying developers and controlling external installations could provide a mechanism that other governments may scrutinize.

For Russian users the practical risk is delay rather than disappearance, since a determined user could still install unverified software once the waiting period ends.

Whether the 2027 expansion includes Russia remains unknown, and until Google says so the ministry is answering a hypothetical restriction with real preparation.

Developers already removed from marketplaces have the most to lose if Google’s verification system prevents users from installing their applications.

Via 1.ru

• Amazon pledges $20 million to a new Colorado River Basin Collaborative targeting $100 million in two years, with $32 million in commitments so far
• Amazon only reported one figure: 2.5 billion gallons of global data center withdrawals, but no basin, warehouse, historical, or power-generation water figures were made public
• AWS interestingly gives paying customers region-level water data, though it says its basin sites are air-cooled, shifting the onus of water use to power plants it does not currently account for

Amazon has committed $20 million to water conservation in the Colorado River Basin, seeding a new corporate effort it calls the Colorado River Basin Collaborative.

The goal is to raise $100 million within two years for leak repairs, canal lining, and precision irrigation along a river that serves roughly 40 million people and is about to absorb steep new federal delivery cuts.

Amazon's chief sustainability officer Kara Hurst tied it to the company's Climate Pledge, arguing that "water and climate are deeply connected."

No published figures when it comes to Amazon's own use of the same water

When asked about its water use, Amazon's response, interestingly, offered no new information beyond what was already in the public domain from earlier disclosures.

This means the ecommerce and data center giant is unwilling to specify how much water it draws from the same basin where it now funds conservation projects.

Amazon has named two projects so far, both in the Lower Basin. It will spend $2.2 million over three years with the Municipal Water District of Orange County on leak detection and repair, which it expects to save more than 189 million gallons a year once fully in place.

Another $2.4 million goes to agtech company Arable for soil-moisture sensors on tribal farmland that draws from Lake Mead, projected to save about 238 million gallons a year.

Interestingly, Amazon told Arizona Public Media it cannot yet name the tribal recipient because water rights negotiations are ongoing, and the Arable sensors themselves run on AWS.

Together, these two projects total $4.6 million, about 23% of Amazon's pledge, which means there may be more to come, even though none of it has been earmarked for any publicly named project yet.

The river's own arithmetic is far larger than what Amazon is looking to resolve: its announcement states that the basin's supply-demand gap is about 1 trillion gallons a year, making its current contributions a relatively small play at best.

Amazon does provide one figure to account for its use: in June 2026, it disclosed for the first time that its data centers withdrew about 2.5 billion gallons worldwide in 2025, and said withdrawals at the sites it owns and operates fell 2% from 2024.

It claims a water usage effectiveness of 0.12 liters per kilowatt-hour, more than seven times better than its calculated industry average, and says a third party assured its 2025 withdrawal and replenishment data.

Bloomberg reported Amazon's figure counts both evaporated water and water discharged to wastewater, a more conservative method than some rivals use. AWS' VP Kerry Person then told Bloomberg that, to read the press, "the data center industry is apparently consuming all of the water in the world," reflecting its frustration with how it is being portrayed in a post-AI world even as scrutiny grows globally.

With Amazon insisting that it has no water-cooled data centers in the Colorado basin, one also has to account for the fact that it does not disclose the water used by power plants and energy generation projects that support its increasingly power-hungry data center ambitions.

Amazon says it is 75% of the way to being water positive, returning three gallons for every four its data center operations used in 2025. The simplest way to substantiate that in the Colorado River Basin would be to publish what its data centers and warehouses withdraw there. Until it does, the $20 million is a welcome, abeit inadequate contribution to a river in crisis, and an incomplete answer to the question the announcement invites.

• Swedish celebrities are fronting a campaign to be rude to phone scammers
• The majority of Swedish phone scam victims are over 60
• Swedish police state that fraudsters collect around 5.7 billion SEK a year, over £431 million

Swedish celebrities have been hired to help the national government promote the use of profanity when dealing with telephone scammers.

The famously well-mannered Swedes are being encouraged to employ potty-mouthed responses when bothered by vishing scams and other attempts to steal money over the phone.

Swedish law enforcement states that 5.7 billion Swedish krona – £431 million or $575.3 million is stolen every year by fraudsters, many of which operate over the phone. The campaign is targeting over 60s as they are the main target for scammers.

Targeting over 60s

Christina Schollin, Claes Elfsberg, Suzanne Reuter, and Lennart Jähkel are participating in the campaign, which aims to provide elderly Swedes with the confidence to tell phone scammers to “go away” with whatever expletives come to mind.

By aiming for the over 60 demographic, the criminals perpetuating the crimes aim to leverage feelings of trust in institutions, often posing as government agencies or banks, often initiating contact via text message.

When the target calls the number in the message, the scammers employ tried-and-tested scripts to persuade the victim of the importance of their call, applying subtle stress and panic to push them into parting with their cash.

But the campaign is more than about turning Sweden into a land of sewer-minded insults -- over-60s are being educated not just in delivering a timely profanity, but also in recognizing when they are being scammed. With the new campaign, swearing is merely one option. Being confident to simply hang up, or tell a fake cop scammer to go away, are both good alternatives.

Don't be fooled

The notion of being rude to a stranger on the phone is not one that comes naturally to the Swedish people, which is why the campaign has been launched with well-known celebrities. Actress Christina Schollin, journalist Claes Elfsberg, actor Lennart Jähkel, are three of the famous faces, and all have one thing in common; like the key demographic of scam victims, they are over 60.

Swedish comedian and actress Suzanne Reuter is the first to feature, and says "I'm in the police campaign because it's a terrible crime that affects so many people. If I can use my voice to help someone not be fooled, then I want to do it. Many of us are raised to always be polite and accommodating, and there are many of my generation. But today you have to dare to be a little more questioning.”

She adds: “It's not nasty - it's self-protection.”

• Meta unveils new platform to provide businesses access to a range of AI tools
• Offering includes access to Muse agent, Meta Business Agent, Muse API, Muse Code
• Zuckerberg announces new Chief Enterprise Platform Officer, poached from MongoDB

Moving beyond simply automating and assisting your entire personal life, Meta now wants to give businesses access to its leading AI models to help supercharge growth and productivity.

The company's new Meta Enterprise Platform will offer access to Muse agent, Meta Business Agent, Muse API, Muse Code for businesses and developers.

“Meta already serves billions of people at scale and helps hundreds of millions of businesses reach customers. Today we are starting the next major pillar of our business, Meta Enterprise Platform, to help businesses use AI to grow and transform in new ways as well,” company CEO Mark Zuckerberg said in the announcement.

Meta offers AI power to businesses

The announcement also included Zuckerberg offering “strengths that few other companies have,” which includes, “advanced models, leading agents, large-scale infrastructure, and years of working closely with many businesses.”

Meta Muse and Meta Business Agent are recent additions to Meta’s lineup of AI agents. Business Agent was unveiled on June 3 2026, pitched as an AI agent “that lets every business show up for every customer as if they had an infinite team behind them.”

September 8 saw Muse unveiled as a personal AI agent, but in this case the tool is being rebranded as a small business assistant, offered alongside Meta's coding agent and the Muse API.

As part of the move to offer more AI access to businesses, Zuckerberg also announced former MongoDB CEO Chirantan Desai would be joining Meta as its new Chief Enterprise Platform Officer.

“Over the coming years, AI will fundamentally redefine how organizations of all sizes innovate, grow, serve customers, and run business operations,” Desai said in a statement. “Meta has a unique role to play because it is bringing together advanced models and leading agents with a proven track record of helping millions of advertisers and hundreds of millions of businesses scale.

“Meta Enterprise Platform will focus on turning its AI stack into products and services that companies can deploy for their own businesses. As with Muse, security and privacy are built into Meta’s enterprise products from the outset. Our goal is to make Meta the place enterprises come to scale their businesses. I’m incredibly grateful for the opportunity and looking forward to working with our customers along this journey,” Desai concluded.

• OpenAI has been forced to pause its GPT-6.1 rollout amid safety concerns
• Safety head reveals it failed on two separate fronts
• AI developers are calling for more government involvement

OpenAI has reportedly cancelled its planned public release of the upcoming GPT-6.1 Astra model after internal testing revealed potential safety issues.

TheWall Street Journal claims the model was due to be released within Codex and ChatGPT as soon as October 2026, but the company's safety head, Saachi Jain, explained that 6.1 "didn't quite meet the bar in terms of staying within scope and authorization, and how it communicates back to the user about the type of work it's done."

A modified version of GPT-6.1, or at least a development on GPT-6, will likely still release pretty soon, but the temporary pause highlights ongoing issues that all AI developers seem to be facing with increasingly sophisticated models.

OpenAI halts GPT-6.1 Astra rollout

The WSJ reporting reveals that the model actually failed on two separate instances – firstly, it continued working on a task beyond what a user had initially requested, sometimes taking actions without user permission.

Secondly, 6.1 Astra also failed on the transparency front, apparently showing more deceptive behavior than GPT-6 by obscuring from the user what it had actually done and implying that it had or had not completed certain tasks.

"We want to make sure our model development is safe no matter whether that's in the company, or when we ship it to users," Jain added, citing the company's "extremely high bar in terms of safety and alignment."

The news comes just days after ChatGPT maker OpenAI said in an X post that it would be "conducting a much broader review of actions taken by [its] models during training and evaluation," following a Hugging Face incident.

CEO Sam Altman also asserted that the company is conducting an "extensive and ongoing review."

More broadly, OpenAI and fellow AI developers have also been calling on governments to have more oversight over model releases in order to ensure maximum safety. For now, global ongoings are leading companies to battle it out in a bid to release best-in-class models, but with greater oversight and limitations, they would be freer to slow down and focus more on safety.

"For anything regarding safety and alignment, there’s a trade off. You really do need to find what’s the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction," Jain added.

• Apple patches high-severity CoreGraphics zero-day reportedly exploited against specific targeted individuals
• The vulnerability enables arbitrary code execution through maliciously crafted files on affected Apple devices
• Apple urged users to install the latest security updates, particularly high-value targets facing sophisticated attacks

Apple has released a fix for a zero-day vulnerability it says was allegedly used “in an extremely sophisticated attack against specific targeted individuals”.

The vulnerability was found in iOS 26.7.1 and iPadOS 26.7.1 and users are advised to apply the fix as soon as possible. This is particularly important for high-value targets such as diplomats, dissidents, whistleblowers, political opposition, and journalists.

Zero-day

The vulnerability was found in CoreGraphics, the company’s low-level 2D graphics framework used across different platforms (iOS, iPadOS, macOS, and more). CoreGraphics provides developers with tools they need to draw and render visual elements (lines, shapes, images, and even text), and can handle operations such as colors, transparency, gradients, clipping, etc. It is usually used when developers need more precise control over how something is drawn.

The vulnerability is tracked as CVE-2026-86950, with a severity score of 8.8/10 (high). The National Vulnerability Database (NVD) describes it as an out-of-bounds issue that allows threat actors to execute arbitrary code via a maliciously crafted file. The bug can also be exploited to crash programs and corrupt data. Besides iOS and iPadOS, it was also fixed in macOS Sequoia 15.8.1, and macOS Tahoe 26.7.1.

Here is the full list of affected devices:

iPhone 11 and later

iPad Pro 12.9-inch 3rd generation and later

iPad Pro 11-inch 1st generation and later

iPad Air 3rd generation and later

iPad 8th generation and later

iPad mini 5th generation and later

Mac devices running macOS Sequoia 15.8.1 and Tahoe 26.7.1

Apple fixed it with improved bounds checking, it was said in the security advisory.

Abused in “extremely sophisticated” attacks

What makes this vulnerability stand out in a sea of zero-days is how Apple described observed exploitation attempts.

“Apple is aware of a report that this issue may have been exploited in an extremely sophisticated attack against specific targeted individuals on versions of iOS before iOS 27.” We don’t know which report Apple is referring to here - we could not find anyone discussing it. The company only said the bug was flagged by Meta Product Security.

This could be deliberate, though. High-profile attacks tend to draw a crowd, as well as increased interest from other threat actors looking to exploit zero-day flaws. Apple is known for hiding details of vulnerabilities until it is confident that a significant majority of affected devices have been patched.

“Extremely sophisticated attacks against specific targeted individuals” is also wording Apple usually uses for state-sponsored espionage attacks against diplomats and politicians, high-value targets such as tech CEOs, journalists, political opponents and dissidents, and similar. Although it is not mentioned in the report, tech companies like Apple and Google tend to notify the victims when they’re being targeted in such attacks.

The last time Apple used similar wording was in February 2026, when it patched CVE-2026-20700. In that incident it also did not discuss the attackers, or the victims, but we do know that it was discovered by Google’s Threat Analysis Group (TAG), a department assigned with investigating primarily state-sponsored hacking campaigns.

CVE-2026-86950 is now the second zero-day vulnerability the company patched this year. Last year, Apple addressed seven zero-day vulnerabilities exploited in the wild: CVE-2025-24085, CVE-2025-24200, CVE-2025-24201, CVE-2025-31200, CVE-2025-31201, CVE-2025-43529, and CVE-2025-14174.

Given the severity of the flaw and what it can be used for, users are advised to apply the patches without delay. Apple users with automatic updates enabled should receive the security fix automatically, but those who haven't yet updated should manually check Settings → General → Software Update and install iOS/iPadOS 26.7.1.

Via BleepingComputer

• Nvidia response to "recent security incidents" by tightening restrictions on autonomous agents
• New software and hardware safeguards impose barriers if agents go rogue
• More than 100 customers have already agreed to work with the principles

Nvidia hasunveiled the new Open Agent Safety Platform designed to prevent autonomous AI agents from 'escaping' and acting beyond their established boundaries.

Following several recent alarming reports of AI-led attacks, the company admitted that safety should not just rely on safeguards built into AI models and applications, and that additional compute and hardware-level controls should also be used as more solid barriers.

"Recent security incidents have underscored the need to equip organizations with open, customizable tools," the company wrote in its announcement.

Nvidia adds even more layers of control for autonomous AI agents

The Open Agent Safety Platform consists of two key controls – OpenShell, which Nvidia describes as a software-based "secure runtime boundary," and Sentry, a hardware-based "watchdog" running on its BlueField-4 DPUs.

"Safety and security require full-stack engineering. NVIDIA Open Agent Safety Platform brings together industry, researchers and public-sector organizations to share best practices, align on evaluation methods and foster international cooperation," company CEO Jensen Huang summarized.

While existing model-level safety mechanisms are designed to tell AI not to behave in a certain way, the software-level OpenShell implements technical boundaries to actually prevent an agent from ignoring those instructions. Nvidia also argues that Sentry can quarantine and stop an AI agent within milliseconds.

Over 100 customers such as SpaceXAI, Anthropic and Microsoft are already using Nvidia's software and hardware-based safeguards.

The news, of course, comes after several high-profile instances where AI agents have operated beyond their intended controls, however while these new Nvidia-backed safeguards may be new, the overall principles aren't. The company is just stressing the importance of principles we're already familiar with, like least privilege, isolation/quarantining and detailed monitoring.

"As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety," Huang added.

RugOne Xsnap 7 Pro: 30-second review

Launched at the Berlin IFA this year, the RugOne Xsnap 7 Pro fuses two well-known products into something genuinely new.

In this case, those elements are the rugged Android smartphone and the small action camera. In this design, the two can be separated, giving you a rugged phone that can spawn an action camera, the Xsnap Go, whenever you need one.

But ignoring that aspect for a moment, what does the Xsnap 7 Pro offer for those who just need a rugged Android phone? As rugged designs go, this one doesn’t have a massive battery, but it does have a decent processor in the Dimensity 9400, and with 12GB of RAM and 512GB of storage, the specifications are at the higher end of this scale.

Also, even if you don’t use the detachable camera, the built-in cameras are decent and capable of good results. But the party trick is the tiny square camera that can be removed from its slot in the back of the phone, which doubles as a charger, and then remotely controlled to capture images and video.

It connects via the platform's WiFi hotspot, and all captures are stored in the 64GB of internal camera storage before later transfer to the phone. The maximum video resolution is 2.7 K, but it supports anti-shake and the option to flatten the barrel distortion the fisheye lens creates.

This camera isn’t a direct competitor to any camera GoPro recently made, the DJI Osmo Nano, or Insta360 Go Ultra. However, it’s included in the price, and it could be useful for anyone who needs to be in a location where using the camera built into the phone isn’t convenient.

This design probably isn’t the best rugged phone, since the action camera slightly compromises it, but if you need a solid, powerful phone with a detachable camera, this is it.

RugOne Xsnap 7 Pro: price and availability

• How much does it cost? $999
• When is it out? Available now
• Where can you get it? Launched on Kickstarter on the 7^th of September 2026

I don’t follow why so many Chinese device makers do this, but RugOne initially launched this phone on Kickstarter. The supposed purpose of that platform is to get funding for a concept to turn it into a finished product, but since I had the review hardware almost before the Kickstarter launched, it appears to be entirely a marketing exercise.

When the Kickstarter opened for pledges on 7 September 2026 with an early bird price of $899 (€799), Wider retail availability is planned for October 2026, at $999 in the United States and €1,099.99 across Europe.

As with any Kickstarter campaign, backers are funding a pledge rather than making a conventional purchase. Delivery estimates, warranty terms and final specifications can all shift between a campaign page and a shipped product, and this is worth stating plainly in the published review rather than leaving it as an implication.

That said, the phone and camera do exist, but I can’t give you more definitive timescales or pricing, which isn’t helpful.

If the phone alone was $999 at launch, I’d be inclined to think it was overpriced. But with the camera included, that’s a whole different calculation based on what you think the camera is worth.

A DJI Osmo Nano Standard Combo (64GB) is roughly $390 with 64GB onboard, but that’s a camera that’s capable of 4K at 60fps and a 200-minute battery life.

If I optimistically value the Xsnap Go camera at half that value, then $800 still seems a little steep for the phone. But there is some convenience in having the camera housed in the phone and charged from it.

Sadly, I don’t recall seeing any other products with this arrangement, so the choice is either this, a phone, or a phone-and-action-camera combo.

• Value score: 3.5/5

RugOne Xsnap 7 Pro: Specs

RugOne Xsnap 7 Pro: Design

• Curious structure
• Detachable Xsnap Go camera
• Lots of accessories

There is something about the Xsnap 7 Pro that smacks of the kind of exercises students do when they're aiming to become industrial designers. In that, taking two disparate devices and fusing them into one can be an interesting challenge.

The problem the people behind this phone didn’t solve was how to house a camera that’s 25mm deep without making it implausibly thick at one end.

We’re all used to the camera cluster being raised to provide extra depth for the optics and sensors, but this design takes that a whole extra level.

Curiously, RugOne quotes the thickness as 19.1mm, but I measured it, and 28mm seems more accurate. The upshot is that it goes in a pocket more easily upside down, but getting past the camera bulge might be difficult unless you have huge pockets.

Removing the camera doesn’t make it any thinner.

The mechanism for detaching the Xsnap Go camera is straightforward yet quite annoying. To release the camera, press the button below it and then, gripping the edge of the lens cover, pull it out. I’m making this sound simpler than it is, because the camera is held in place not only by the latch the button operates but also by magnets. If you loosen your grip at all when removing it, the camera will shoot back into its home in an instant.

The magnets are there because the rear of the camera has contact pads that connect it to the phone for charging, and for them to work, they need to be flush with the opposing pins on the bottom of the recess.

Putting it back in is much easier, as once the magnets grab, it automatically locates.

The space the removable camera occupies forces the rear integrated camera sensors to the left, where they are vertically stacked.

Away from that area, the back is entirely flat and has a backlit RugOne logo running across it that is called the Aura Pulse, which can be set to animate for an incoming call or a notification. Unfortunately, the LEDs behind it are too dim to be useful, as I had a hard time telling whether it was working even in a dimly lit room.

Potentially more useful, the flat part of the underside does support wireless charging. However, because a significant amount of weight is in the raised area, I couldn’t balance it on my Ugreen charger without it toppling over.

Most of the rest of this design uses the standard playbook for rugged phones, with the power and volume rockers on the right, along with one custom button and another customisable button on the left intended for Walkie-talkie use. The SIM card slot is next to the USB-C port, and the phone is IP68 rated for water resistance even if that port doesn’t have a rubber plug.

The only notable feature at the top end is an IR blaster, which lets the Xsnap 7 Pro act as a remote control if you need it.

The 6.67in AMOLED panel runs at 1220 x 2712, which RugOne markets as 1.5K, with a 120Hz refresh rate and a claimed 1700 nits of peak brightness. Corning Gorilla Glass Victus sits on top, a sensible choice for a phone that will spend time in pockets, tool bags and glove boxes.

On paper, this puts the Xsnap 7 Pro's screen ahead of many rugged phones, which often settle for lower resolution or dimmer panels to save cost. Brightness claims from any manufacturer are difficult to counter, but this one is one of the brighter ones I’ve tested when measured directly under daylight conditions against other devices.

One thing I liked about this exceptionally bright and colourful display is that it has an under-screen fingerprint sensor, which makes it much easier to use for left-handers.

Overall, from the outside, this looks like a dozen ruggedised designs Ulefone (RugOne) has made, except for the swelling needed to accommodate the removable camera. I’d be more critical of those design choices if I could imagine a way of including it without it creating the ergonomic compromises it plainly created. But I can’t think of how this might have been better, so it is what it is.

Design score: 3.5/5

RugOne Xsnap 7 Pro: Hardware

• MediaTek Dimensity 8400
• UFS 4.0 storage
• USB 3.2 Gen 1 port
• 9300mAh battery

The underlying platform used here is the MediaTek Dimensity 8400, an octa-core chip built on Arm Cortex-A725 cores and paired with an Arm Mali-G720 MC7 GPU. It is a genuinely modern mid-to-upper-tier platform, backed here by 12GB of RAM and 512GB of UFS 4.0 storage, with the option to borrow further RAM from storage through Android's virtual memory feature.

If you see claims of 24GB of RAM, that’s marketing people getting overly excited about that feature and trying to claim virtual memory is real RAM.

For me, the important takeaway is the UFS 4.0 storage. One of the things I noticed almost immediately about this device is how quickly it downloads and installs applications. Read and write speeds are a major factor in that speed, along with the processing power of the Dimensity 8400.

Another unexpected feature RugOne included is that the USB-C port isn’t the same as what most phone makers include. While it has the physical housing of a modern USB-C port, most phones only have it wired to the USB 2.0 standard, capping the file transfer speed at around 40Mb/s.

On this device, the port is actually wired as USB 3.2 Gen 1, which enables much faster file delivery. This is only possible because the UFS 4.0 storage can keep pace, something that a phone with UFS 2.2 or 3.0 storage wouldn’t be able to offer.

A 9,300mAh cell is a reasonable capacity for any phone, rugged or otherwise, and RugOne claims up to seven days of typical use. That’s probably a significant oversell, since my testing suggests that a couple of days can eat through that capacity if the battery use isn’t managed.

Charging is handled over 45W wired PD or 50W wireless, with 10W reverse wireless charging thrown in for topping up earbuds or accessories. However, one additional draw on the battery power is the action camera. If you use the camera, once returned to the phone, it immediately starts to charge from the battery until its internal battery is fully charged.

Therefore, if you run low on power, I’d recommend not re-docking the camera.

Docked back onto the phone, RugOne claims up to 20 full recharges of the module from the phone's battery, extending total shooting time to a claimed 800 minutes across a full charge cycle of the handset.

The kit includes an independent charger for the camera, so you can keep it fully charged with a power pack.

• Hardware score: 4/5

RugOne Xsnap 7 Pro: Cameras

• 50MP and 64MP on the rear
• 32MP on the front
• Detachable Xsnap Go action cam
• Four cameras in total

The RugOne Xsnap 7 Pro has four cameras:

Rear cameras: 50MP Samsung GN9, 64MP OmniVision OV64B Night vision
Front camera: 32MP GalaxyCore GC32E1
Detachable camera: OmniVision 5MP OV-OS05A10

The primary camera on the Xsnap 7 Pro is the classic Samsung GN9 sensor, a 50MP OIS design with an f/1.88 aperture and 84° FOV optics. If this were the only sensor on this phone, it would be reasonable, since it’s a solid design that can take great pictures, especially in good lighting conditions.

In my examples, I accidentally left HDR mode on, so the colours are excessively punchy, but it will take better, more colour-balanced images if you leave it off. The GN9’s weakness is that it has no zoom optics, so you're limited to basic digital zoom.

The other weakness of most Samsung primary sensors is that they tend to get grainy when the light level drops. Thankfully, if you want sharp images in complete darkness, then the 64MP OmniVision OV64B is a great option. And, as long as the subject is within the IR illumination, it will be captured precisely.

The 32MP Samsung GC32E1, f/2.45 sensor is probably overkill for a FaceTime camera, but it's in so many phones these days that it's almost becoming a de facto standard.

But none of these cameras is why you would consider this phone, so let’s get to the detachable Xsnap Go action module, which uses the OV-OS05A10 sensor, a 133° FOV, f/2.4, 14mm equivalent.

Before we get into the details of this device, I want to talk about some of the mechanics of using this device, and how it doesn’t always work the way you might want it to.

For starters, you don’t use this camera through the camera app; instead, it uses its own dedicated Xsnap Go app, which will only talk to the detachable camera if certain phone settings are configured.

One requirement is a WiFi hotspot, so you can’t use it while the camera is active and also be logged into a local WiFi access point. You also need to remember to disable this when you are done, as the app won’t disable it when it exits.

With all settings configured, a link to the camera is established, whether the module is mounted on the phone or detached from it.

Which begs the question: how far can the phone and camera still talk? Not very far. I estimated the limit at about 30 ft if any structure is involved, and maybe 50 ft outdoors. One issue I ran into is that if the phone and camera disconnect while recording, the app can't easily re-establish the connection, and at least once I had to reboot the phone to get them to talk again.

You can use the camera's single button to take photos or record video without the app, but you won't know if it's run out of internal storage without the phone connected.

Via the app, you can select basic video recording, slow motion, time-lapse, interval photo and single still image capture. The 5MP sensor's best resolution is 2.7K, while the Samsung sensor on the phone offers 4K. The camera has 64GB of storage that isn't expandable, and you must instruct the device via the app to copy the captures to the phone's storage.

It offers various motion and distortion compensation options, and you can manually tweak exposure settings or use HDR. It’s clearly not up to the primary sensor's capabilities, but you can detach it and place it away from the phone for another perspective.

That could be incredibly useful for someone working, for example, on a vehicle engine who needs to be behind the steering wheel but still see what’s happening in the engine bay.

In my testing, I did some motion-compensated video and time-lapse, and the results were decent. The only issue I noticed, and some dedicated action cameras suffer with this too, is that if the exposure changes during a time-lapse, the camera can’t adjust for the new lighting conditions.

The run time of my time-lapse was 40 minutes and 30 seconds, half a minute more than RugOne claims for the battery life on the detachable camera. This runtime might have been longer if RugOne made better-considered accessories. The phone includes a small charging dock and a tripod mount accessory for the detachable camera.

Had these been one item, and not two, a power bank might have been able to keep the camera running for longer recordings. Unfortunately, they are separate and not designed to work together, as the camera can only be attached to one of them.

That said, the detachable camera is something unique about this design, and even with some notable limitations, it can be extremely useful.

• Camera score: 4/5

RugOne Xsnap 7 Pro: Performance

The Xsnap 7 Pro has a few good things, and one that isn’t so encouraging.

For comparison, I’ve used the Oukitel WP500 Ultra, a premium design that uses the slightly less impressive Dimensity 9300, and not the 9400 that the Xsnap 7 Pro sports.

Overall, in most tests, the Dimensity 9400 provides about another 10% of performance, and the ARM Mali-G720 MC7 is also a significant uplift.

It’s annoying that 3DMark is mostly overpowered by both these platforms, but the Nomad Lite test shows how the ARM Mali-G720 MC7 delivers when combined with the UFS 4.0 storage.

And that would be the whole story, if it wasn’t for the battery test.

Is the downside of the UFS 4.0 storage that it consumes more power? It certainly doesn’t seem that way, because with slightly less battery, the Xsnap 7 Pro lasted even longer than the WP500 Ultra.

I believe a contributing factor is the OLED display on this phone. My methodology for the battery benchmark is to reduce screen brightness to about 120 nits. And the OLED is so much brighter than a typical VA panel that its brightness setting is around 20%, not 40% or more.

What’s harder to tell is whether having the detachable camera connected and charging from the phone also has a significant impact. It’s nice that you can recharge the camera without needing the charging base, but does that come with a catch? Maybe, but you can always remove the camera from the phone and charge it separately.

• Performance score: 4/5

RugOne Xsnap 7 Pro: Final verdict

I have mixed feelings about this design for a few reasons. But before we get to the caveats, I need to call out the lovely build quality that RugOne-branded hardware is rocking, which is generally excellent. This phone feels exceptionally well made and able to take more than a few light knocks. That makes it suitable for anyone who works outdoors or in harsh environments.

The base platform is also elegantly powerful, making the experience responsive and useful in a wide range of situations. If you occasionally game or use VR, this phone has enough power to do it smoothly.

The standard camera installation is workmanlike and can deliver good results. However, the detachable Xsnap Go camera isn’t quite at the same level, and you need to configure it optimally to get the best results. If 2.7K resolution video and a 40-minute battery life are enough for your needs, this could be the phone for you, but it doesn’t embarrass any dedicated action cameras currently on the market.

If you work alone but need to be in two places at once to observe events, this solution might work for you, as long as you can work with the limited camera runtime.

The camera's caveat is that it makes the phone harder to pocket, and it could drain the internal battery when you don’t want it moving to the camera.

But minor points aside, this is still a unique offering that some professions might find invaluable.

Should I buy a RugOne Xsnap 7 Pro?

Buy it if...

Don't buy it if...

For more ruggedized devices, we've reviewed the best rugged tablets, the best rugged laptops, and the best rugged hard drives

• AI-powered deepfakes convinced executives to authorize massive overseas fund transfers
• Fraudsters impersonated senior figures through messaging apps and cloned voices
• Incident highlights evolving business email compromise tactics in the AI era

Cybercriminals have tricked a major Italian bank into wiring more than $100 million abroad by targeting executives with AI-powered deepfakes. Some of the money has since been recovered, but a significant portion remains unaccounted for.

The target was Fideuram – Intesa Sanpaolo Private Banking, a very large Italian private-banking and wealth-management group owned by Intesa Sanpaolo. It cannot be considered a “normal” retail bank - its core business is managing investments and wealth for affluent and high-net-worth clients.

According to the latest figures, the bank has around $513.25 billion in assets under management (AUM), more than 7,100 private bankers, and more than $9.85 billion in net profit.

Deepfakes and phone calls

Citing two sources familiar with the matter, Reuters reported that the attack took place in February 2026, while the chairman was ​Paolo Molesini.

Molesini reportedly received a WhatsApp message that seems to have come from Carlo Messina, Intesa Sanpaolo Chief Executive Officer (CEO), who asked him to help with an urgent overseas transaction, to which the victim agreed. He was later contacted by someone claiming to be senior partner at a prominent law firm, to confirm the instruction. This person used AI tools to create a deepfake of the lawyer’s voice, tricking Molesini into thinking the request was authentic.

The chairman then reached out to the bank’s finance department, arranging a series of transactions to foreign account, the majority of which were in mainland China and Hong Kong.

The scam was spotted soon afterwards, and Fideuram moved to block the transaction. With the help of law enforcement in China, Portugal, and Italy, around $60.14 million was recovered. Around $40.8 million remains missing, apparently converted into cryptocurrencies.

Ruining a person's career

​Paolo Molesini announced he was stepping down from roles of president for both Fideuram itself and Intesa Sanpaolo Private Banking a month later, in March 2026, citing “personal reasons”.

According to Reuters, neither Molesini, or any other Fideuram executives are under investigation, but local authorities are apparently investigating a “foreign national living outside Europe” for potential fraud.

In the pre-AI era, this type of fraud was known as Business Email Compromise (BEC). Crooks would break in (or otherwise spoof) the email address of a company’s CEO (or other executive), map out employees working in the finance department, and reach out with instructions for wire transfers. These instructions would explain that the transfer needs to be completed in secrecy, to avoid unnecessary publicity or competitors.

Nowadays, with the advent of artificial intelligence and the ease with which crooks can create deepfake images, voice clones, and even videos, BEC attacks have spread out to instant messaging platforms, voice calls, and even video calls.

Last year, scammers used artificial intelligence to create a deepfake of the voice of an Italian minister, tricking local businessman Massimo Moratti to transfer around $1.13 million to an overseas account. The money was later successfully recovered, it was said.

• ShinyHunters bypass PeopleSoft mitigation rules, reviving exploitation of a critical zero-day vulnerability
• The campaign has expanded globally, targeting organizations across technology, healthcare, government, and other sectors
• Oracle’s original patch remains effective, while organizations should investigate systems and rotate potentially exposed credentials

ShinyHunters have found a way to bypass a mitigation for a zero-day they previously exploited - so now, not only are they back to abusing the same bug, they’ve even expanded their scope to target a much larger pool of organizations.

In June 2026, it was reported that ShinyHunters, the infamous data extortionists, found a Java deserialization vulnerability in Oracle’s PeopleSoft Environment Management Hub (PSEMHUB) servlet that allowed them to achieve web shell deployment or fileless command execution on vulnerable servers.

Oracle PeopleSoft is a suite of enterprise business software used mainly by large organizations, universities, governments, and corporations to manage things like human resources, finance, supply chain, and student administration.

This remote code execution (RCE) bug caused by unsafe deserialization of Java objects in PSEMHUB was exploited for days, targeting higher education institutions, stealing their sensitive files, and enabling extortion campaigns.

Oracle fixed the issue on June 10 2026, bringing the tool to versions 8.61 and 8.62. The bug was assigned an identifier CVE-2026-35273 and was given a severity score of 9.8/10 (critical). The US Cybersecurity and Infrastructure Security Agency (CISA) added the bug to its Known Exploited Vulnerabilities (KEV) catalog on June 12, giving federal agencies a three-day deadline to patch up.

ShinyHunters Return

The company also provided mitigation measures for organizations that were unable to install the patch at the time. However, Mandiant and Google’s Threat Intelligence Group (GTIG) are now reporting that ShinyHunters found a way to bypass these mitigations, and are back to exploiting the flaw against organizations running unpatched versions of the software.

“This new wave of activity stems from UNC6240 modifying its exploit to bypass web application firewall (WAF) rules blocking the vulnerable Environment Management Hub (PSEMHUB) endpoint,” the two organizations said in a new report, stressing that the campaign has now expanded globally.

“The threat actor bypassed these string-based WAF rules by URL-encoding a single character in the request path, requesting /%50SEMHUB/ in place of /PSEMHUB/,” the researchers said. “Many WAF and reverse proxy rules match the literal path before URL decoding, while the PeopleSoft application server decodes the request and routes it to the vulnerable servlet. This allows the threat actor to reach the endpoint on systems whose operators may have believed their WAF rules had mitigated the exposure.”

At first, ShinyHunters were focused mostly on higher education institutions. This time around, though, they’re casting a much wider net, deploying web shells on “dozens of systems globally” including technology, IT services, healthcare, agriculture, transportation, and government.

“Mandiant recommends that organizations running Oracle PeopleSoft take the following immediate actions,” the researchers added.

ShinyHunters are known extortionists. They are using the PeopleSoft vulnerability to gain persistent access and steal credentials, allowing them to move laterally through target environments and exfiltrate sensitive data such as HR or payroll.

Patching works

The good news is that the patch still works. If an organization applied Oracle’s fix for CVE-2026-35273, this WAF bypass should not matter, because it only works around the mitigation, not the underlying vulnerability fix. Therefore, both Google and Mandiant advise, first and foremost, that organizations apply the Oracle Security Alert issued when the zero-day was first disclosed.

Businesses should also disable the Environment Management Hub (EMHub) service in multi-server configurations, or remove the PSEMHUB application entirely, in single-server configurations.

To check whether they were targeted or not, they should search PIA WebLogic access logs for requests to /PSEMHUB) and any percent-encoded variant, and should inspect /webserv//applications/peoplesoft/PSEMHUB.war/ for files that are not part of the shipped product.

Finally, they should rotate all credentials the PeopleSoft application service account can read, and monitor outbound traffic from PeopleSoft hosts to the network indicators listed here.

• One in four UK workers say they'd be less likely to apply for a job due to AI screening
• 78% of global applicants are still using AI at some point in their recruitment process
• Report calls for more training opportunities to find meaningful use cases

New Morgan McKinley research has claimed more than half (55%) of UK employers still don't use AI in recruitment – putting Britain far behind the global average of 36% non-users.

And on the other side of the process, UK jobseekers are also apparently wary of AI being used to assess them, with more than a quarter (28%) saying AI-based assessments would make them less likely to apply for a job (again, more than the 23% global average).

However, concerns over AI's usage are clearly more pronounced in certain areas, because 78% of global candidates use AI at some point in their job searching journey.

Recruiters and applicants are still wary about AI's use in recruitment

Ultimately, it seems that AI could be obscuring human assessment processes, for example two in five UK employers say the use of AI has made it harder to verify applicants' skills. Head hunters also note that AI has led to a higher volume of applications they're having to process, likely increasing quantity but not quality.

"As candidates increasingly use AI in their job searches and employers grapple with verifying skills and experience, organisations will need to balance the benefits of AI with transparency and human oversight," Morgan McKinley UK Senior MD Chris Lawton explained.

More broadly, it's clear that job-related concerns continue to plague AI adoption, with nearly half (46%) of global employees worried that AI could replace their job, even though 64% of employers say it hasn't actually impacted headcount.

Moving forward, the report calls for a more meaningful approach to AI adoption with supported training opportunities. With just one in four workers believing their employer is investing adequately in their AI development and Morgan McKinley data revealing that two-thirds (68%) have had either inadequate or zero AI training, this area is clearly lacking.

"Organisations need to give their people the skills, guidance and governance to use AI effectively and responsibly, rather than leaving individuals to navigate this transition alone," Lawton concluded.

The adoption of AI tools in the workplace shows no sign of slowing down. With 88% of businesses using AI in some capacity, companies are now highly focused on trying to measure the ROI from their investments in the technology.

Companies deploying AI tool stacks across their teams aim to boost productivity by automating routine tasks, synthesizing data and improving efficiency, reducing both costs and time-intensive labor.

But there is friction. Half of London businesses say their workforce does not currently have the skills needed to meet their organizations AI requirements. Companies are now facing a costly problem, they need a return on the investment in AI and it’s going to take a revamp of skills and job roles to do so.

Businesses need to take a head-on approach to solve this challenge by balancing technical knowledge and creativity as well as business understanding.

It is not just technical IT skills required for back-end ops, but also digital literacy, critical evaluation of AI outputs and workflow integration. These are becoming essential at every level, from entry level to C-Suite level.

Job Descriptions need updating

As the digital skills gap widens, it is unsurprising that, in the UK, one in four workers worry AI could replace them. In reality, the labor market is splitting into two distinct tiers.

The professional tier is centered on blended roles that rely on human judgement and recognizes that AI tools are only as effective as the people guiding them. By contrast, the democratized tier sees repetitive tasks handed over to AI tools entirely.

With most roles sitting in the professionalized tier, maximizing AI efficiency requires critical thinking and judgement that the technology itself cannot supply. However, if those expectations are not written into the role, employers end up overestimating the capabilities of their employees.

Nowadays, many roles have become blended with technology, and, except for technical roles, many necessary AI capabilities are often summarized into a single generic bullet point on technical proficiency. What we are seeing is a reshaping of what employers value. This reshaping is driving the emergence of creative technologists.

A creative technologist is someone who understands technology, creativity and strategy, and organizations that actively empower this role are more likely to pull off digital transformation successfully.

Today, many teams are full of creative technologists who don't realize that's what they've become. The forces driving this are generative AI becoming a daily tool, rising content demands, and increased cross-discipline collaboration.

But employers need to ensure job descriptions match this reality. A blended job description needs to name the AI tools the person will actually use, then spell out the skills needed to use them, moving things like critical thinking from generic bullets into specific expectations tied to those tools.

If employers get that right, they will be hiring people who can capture the productivity gains and not just operate the tools. AI success requires people who combine technical expertise, industry know-how and creativity.

Without more blended roles, businesses are going to struggle to see the return on investment and ensure they are using the right tools for the right job.

Encouraging trialing, testing and tryouts

Effective learning can only happen in an encouraging environment. Getting comfortable with prompting frameworks makes AI tools feel less daunting, and that comfort is often what surfaces where AI can actually help within the organization. Experimentation matters even when a given approach doesn't get pursued further. Sometimes the most useful lesson is learning what not to do.

One of the biggest barriers to that experimentation paying off is dark data. Most organizations sit on enormous volumes of information, but a large share of it lives in documents, emails, PDFs, and other unstructured formats that are hard for AI systems to interpret.

That means even well-designed models often work from an incomplete, inconsistent view of the business which leads to unreliable outputs and initiatives that stall before they scale.

Moving past that requires turning dark data into something usable that can build upon the structure, context, and governance AI systems need to act on the data with confidence. Once that foundation is in place, AI becomes far more reliable, and far easier to move from isolated pilots into real production environments.

The human edge

AI can streamline planning, but people still drive execution. As tools take on more routine workflow, organization and documentation tasks, the skills that remain most valuable are the ones AI cannot replicate such as judgement, storytelling, creative thinking and the ability to lead people through change.

Demand for AI-fluent specialists has created a new kind of empowered generalist who can read AI trends as fluently as they manage stakeholder relationships and understand customer pain points.

AI may automate workflows and documentation, but it cannot lead a team, interpret context with human nuance or fully understand a company’s mission.

Where wearing many hats within one role was once seen as a drawback, AI is making it one of the most efficient ways to work. It gives employees access to skills that would previously have taken years, and several role changes, to acquire.

Rather than simply eliminating roles, AI is also creating new ones centered on managing the human-AI relationship, including AI evaluators and human-in-the-loop reviewers.

In this sense, AI is not replacing jobs so much as reshaping them, with the potential to reduce workforce inequality by democratizing access to new skills.

The skills gap is a hurdle many organizations are now facing and both employees and employers must experiment to adapt.

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• Netherlands announces the Digital Autonomous Work Environment for Government Scheme
• Windows operating system could end up being replaced by NixOS
• Other software stacks are also being considered

The Dutch government has become the latest European ruler to announce plans to invest more heavily in sovereign software in a bid to reduce its reliance on foreign tech vendors, including Microsoft, following an earlier April 2026 pilot.

The DAWO (Digitaal Autonome Werkomgeving Overheid), which translates loosely as the Digital Autonomous Work Environment for Government scheme, covers a broad spectrum of software changes.

Besides just ditching Windows in favor of an open-source alternative, the Netherlands wants to explore cloud infrastructure, productivity software and more.

Dutch government announces plans to ditch Microsoft

Though the migration is still in its early stages, small-scale trials are reportedly taking place across the country's public bodies, with eight municipalities allegedly preparing trials.

Naturally, the long-term migration is likely to take years and include multiple bids, but the country expects the first migrations to have happened by the end of next year, per Tom's Hardware.

The biggest migration will likely involve Microsoft's Windows operating system. Windows currently accounts for around 63% of global desktops across all users, per Statcounter data – a proportion that's dropping as macOS and other operating systems rise in popularity.

NixOS is set to be the replacement, developed by Dutch develop Eelco Dolstra with the NixOS Foundation based in the Netherlands itself. openSUSE and Fedora were also reportedly trialled before reaching the decision to go for NixOS.

The Netherlands is also said to be working with other European tech vendors, like France's La Suite and Germany's openDesk, in a broader pro-European push.

Ultimately, what the Netherlands is doing is a move we're seeing reflected across the bloc, with government agencies and other public bodies seeking to support sovereign and homegrown tech amid complex geopolitical happenings, set against a backdrop of concerns, namely potential US government access to American software users' data.

And the Netherlands is just one of a growing list of countries making similar moves – Germany's Schleswig-Holstein state already revealed plans to migrate a long time ago, together with Estonia, the French city of Lyon and more.

As the EU AI Act’s transparency requirements come into force, organizations need to be on the front foot when it comes to regulatory readiness and how AI is being used. Facing fines of up to €35 million or 7% of global turnover mean it’s no longer enough to just monitor communication risks. Firms must definitively prove how those risks were identified, reviewed, and escalated.

When regulators ask questions, most compliance teams don’t face the issue of a lack of data. In most cases, the information does exist somewhere within the organization. The harder challenge is bringing that evidence together quickly, clearly, and defensibly across different systems, workflows, and teams.

That challenge is becoming more important as regulators place greater emphasis on demonstrable outcomes. Firms are increasingly expected to show not only that controls exist, but how those controls operate when a potential risk emerges. This is where comms surveillance has become central to effective compliance.

From surveillance activity to supervisory outcomes

Comms surveillance has typically been measured through activity. Compliance teams have tracked how many alerts were generated, how many reviews were completed, how many cases were escalated, and how many investigations were closed.

Those metrics still matter, but they only tell part of the story.

A stronger measure of readiness is whether a firm can connect surveillance activity to supervisory outcomes. Can it explain why a particular risk was identified? Can it show how an alert was reviewed? Can it document the evidence that supported a decision? Can it demonstrate whether escalation was appropriate? And, crucially, can it reconstruct that process months later when a regulator asks for evidence?

These are the questions that now sit at the heart of regulatory readiness.

The evidence chain is often too fragmented

Most firms have already solved the basic issue of capturing communications. The next stage is more complex: connecting communications, alerts, reviews, investigations, and outcomes into a clear and defensible chain of evidence.

Too often, that chain is fragmented and therefore, obsolete. Comms often sit in one platform, while surveillance workflows sit in another, and investigation records somewhere else entirely. Each system may function effectively in isolation, but the gaps are evident when compliance teams need to explain what happened from start to finish.

This is especially pertinent as AI-driven communications become a bigger compliance risk. Our research found while more than half (55%) of organizations globally have the core AI policies, training and review steps in place, fewer than one in five (19%) have the logging, retention, detection and scoring controls needed to prove what happened.

Without that evidence layer in place, organizations lack being able to confidently demonstrate what AI tools produced, who reviewed it, where it went and whether the record was kept.

The result is unnecessary friction: investigations take longer than they should, evidence is harder to assemble, and supervisory decisions still require additional manual validation.

Regulatory requests become more disruptive when teams have to spend valuable time piecing together information that should already be connected. This risks turning into an operational readiness nightmare.

Transparency is becoming a regulatory expectation

As regulatory expectations continue to evolve, firms need to be able to demonstrate how oversight works in practice. In other words, showing how potential risks are identified, how decisions are made, how evidence is reviewed, and how outcomes are recorded.

A surveillance program is only truly effective if it can support that level of transparency. Regulators may not prescribe a specific architecture, but they do expect firms to show that oversight is effective, repeatable, and defensible.

That's why organizations are now looking beyond individual tools and towards a more unified approach to communications governance, surveillance, investigations, and retention.

Operational visibility matters as much as information storage

Operational visibility is becoming just as important as data capture. Compliance teams need to understand where information is stored, as well as how it moves through the organization once a potential issue is identified.

They need to be able to follow the full chain of activity from initial alert through to investigation, escalation, decision, and retention. Comms surveillance has become a critical part of evidence framework.

Done well, it helps teams identify risk earlier, investigate issues more efficiently and demonstrate how supervision operated over time.

Readiness is about proving what happened

The priority is now creating the context needed to make better decisions, and to evidence those decisions effectively.

Regulatory readiness increasingly depends on the ability to demonstrate judgement: why a case was reviewed, what information was considered, whether escalation was required and how the final outcome was reached. Without that connected view, even well-run processes can become difficult to defend.

Comms surveillance is therefore moving from a narrow monitoring function to a core component of regulatory preparedness. It provides the first line of evidence for how potential risk was identified, assessed, and resolved. It also gives firms the foundation they need to demonstrate that supervision is not only in place, but working effectively.

In today’s regulatory environment, readiness is all about being able to prove what happened, not simply locating the information.

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UK small and medium-sized businesses (SMBs) have reached a defining moment. Rapid advances in technology and AI have created the potential to transform how SMBs operate – improving competitiveness, enhancing efficiency and unlocking new growth opportunities.

Yet, over a third of UK SMBs risk being permanently left behind their competitors because they lack the confidence and processes required to identify, adopt and scale new technologies effectively.

The result is an increasingly pronounced divide between businesses that feel equipped to embrace technology and those struggling to keep pace – Tech Vanguards vs Tech Bystanders. Crucially, that confidence can be self-reinforcing.

Businesses that successfully introduce and scale new technologies are better placed to make their next technology decision with confidence. Those that hesitate risk falling further behind, as the capabilities and experience needed to keep pace become harder to build.

This issue can have widespread consequences. SMBs account for over 99% of UK businesses and are the backbone of the UK economy. Therefore, if the confidence gap isn’t closed the whole country will face the consequences.

The disconnect between access and action

As technology has advanced, innovation has become widely accessible to businesses of all sizes, and many SMB leaders already understand the importance of innovation and technology adoption to their business performance. However, according to Babble’s research, fewer than half feel they’re very confident in their ability to identify, adopt and deploy new technologies successful.

There is a clear gap between knowledge and action, and this is where confidence is becoming a key competitive differentiator. The SMBs pulling ahead don’t necessarily have larger budgets or more resources. But they have built the confidence to make better technology decisions through having clear leadership, strong governance and repeatable processes in place.

These foundations matter because they decrease the uncertainty surrounding technology investments. Rather than each investment being treated as a high-stake investment, SMBs with strong leadership and repeatable processes will have frameworks for assessing where technology can add value and if it fits with their business. Successful adoption builds institutional knowledge and experience, making each decision easier.

For less confident businesses, the opposite can happen. Without established processes, technology decisions can feel riskier and more difficult to navigate, encouraging businesses to delay investment or pursue isolated initiatives that struggle to deliver meaningful returns.

More than half (53%) already feel at a competitive disadvantage compared with businesses better equipped to adopt technology, while 28% believe they'll lose new business to competitors embracing technology and 24% fear losing existing customers.

In an environment where innovation is accessible, confidence is the defining competitive advantage.

The cost of standing still in the AI era

While delaying technology adoption instead of making the wrong investment may seem like the lower-risk option, those decisions quietly accumulate over time, especially in the current AI environment.

Previous technology shifts, from the internet and mobile connectivity to cloud computing, gave SMBs time to observe early adopters before deciding when and how to follow. AI is different because it is not simply introducing another tool into the technology stack.

It has the potential to reshape how work itself gets done, taking on repetitive and manual tasks while allowing employees to spend more time on the judgement, creativity and customer relationships where people add the greatest value. For resource-constrained SMBs, those productivity gains are especially significant.

But productivity tools are only the beginning. SMB technology adoption today remains concentrated around chatbots and assistant technologies such as Microsoft Copilot, which can help individuals work faster and reduce administrative burden. But to unlock significant value, SMBs need to move beyond assistants and embed AI directly into workflows and processes.

The risk of waiting isn't simply missing out on today's productivity gains. Businesses experimenting with AI now are building the experience to understand where it works, where it doesn't and how to deploy it at scale. By the time today's hesitant SMBs decide to act, their competitors may already be several decisions ahead.

The answer isn’t more expertise

Despite the growing confidence gap, which is alarming and needs addressing urgently, there is reason for optimism. SMBs are uniquely positioned to thrive compared to larger organizations. They aren’t weighed down by decades of legacy infrastructure and complex organizational structures that can slow down decision making.

The issue for SMBs isn’t lack of ambition; it’s lack of time. SMB leaders are often balancing multiple priorities from marketing to finance and therefore don’t have the capacity to evaluate technology solutions, especially considering the speed at which new advancements are entering the market. It’s difficult for SMBs to distinguish genuine business value from marketing hype. This challenge is compounded by years of technology vendors overpromising and underdelivering.

But SMB leaders don’t need to have every answer or be a technology expert. Adoption should begin with the problem, rather than the technology itself. After all, Henry Ford’s customers didn’t ask for the car, they asked for a faster horse – knowing the problem doesn’t always mean knowing the solution.

SMBs don't need to know which platform, model or vendor will solve every challenge. They simply need to understand where friction exists inside their business. Once those questions are clear, the right partner will help identify the correct solution. Ultimately, assisting in closing the confidence gap.

Confidence starts with the leader

Closing the confidence gap doesn’t require SMB leaders to become technology experts overnight. Technology confidence is built one successful decision at a time, the aim over the next 12 months shouldn't be to transform everything.

The aim should be to create the conditions, one step at a time, for better decisions to be made consistently. That means making technology a defined leadership responsibility with clear ownership at board level and treating it as a business priority rather than something delegated to IT teams.

Competitive advantage will come from knowing where and when to invest. SMBs that establish clear ownership and repeatable processes today will be better equipped to spot emerging opportunities, make informed technology choices and scale what works. In doing so, they can build the confidence to keep adapting as technology evolves.

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For the past several years, the enterprise AI conversation has been dominated by models. Every week has seemed to bring a new benchmark, a new leader board, or a new debate about which model is smartest. Organizations often made technology decisions based on incremental improvements in model performance, assuming that the model itself was the primary source of competitive advantage, but that assumption is rapidly becoming outdated.

As foundation models continue to improve, they are also converging in capability. At the same time, the cost of deploying AI at scale is becoming a more pressing concern for enterprises than squeezing out marginal gains in benchmark performance.

As a result, the focus is shifting from the model itself to everything surrounding it: the systems that make AI useful, governable, and economically viable in real-world business environments.

The question enterprises are increasingly asking is not, "Which model should we use?" Instead, they are asking, "How do we turn AI into reliable work?"

The rise of the AI harness

The answer begins with what can be described as the harness. An AI model on its own is powerful, but incomplete. To deliver business value, it needs context, access to enterprise tools and data, memory, controls, and guardrails. Together, these elements form the scaffolding that transforms a general-purpose model into a system capable of performing useful work, determining how effective an AI system becomes.

Two organizations might deploy the same underlying model yet achieve dramatically different results. One may struggle with inconsistency, high costs, and poor outcomes. The other may produce reliable, measurable business value. The difference often lies in the quality of the harness surrounding the model rather than the model itself.

That reality becomes more significant as models continue to commoditize. In many cases, organizations will have access to the same leading AI capabilities. What will differentiate them will be their ability to operationalize those capabilities efficiently and safely, and move from intelligence creation to intelligence application.

Why loops matter more than prompts

A related concept is what might be called the loop. Many organizations still approach AI interactions as isolated prompts and responses. But enterprise work rarely operates that way. Real business processes involve objectives, verification, correction, and completion criteria. A loop defines a governed unit of work.

Rather than simply generating an answer, the system performs a task, validates progress against the objective, corrects mistakes when necessary, and stops when the outcome is achieved. This seemingly subtle distinction has enormous implications.

When AI systems operate through loops instead of one-off prompts, they generate operational traces and performance records. Those traces can be used to evaluate outcomes, improve workflows, and refine future execution. Over time, this creates a learning system that grows more effective through use rather than requiring constant manual intervention.

In practice, this means enterprises are no longer deploying isolated AI tools. They are building repeatable learning systems — ones that improve the more they do the work.

The orchestration challenge

As AI adoption expands, organizations encounter another reality: there is rarely a single harness. Most enterprises operate across multiple functions, business units, and regulatory environments. Each domain may require different tools, data sources, workflows, and governance requirements.

A harness optimized for software development is unlikely to be the same one used for finance, healthcare, customer service, or compliance. This creates a new challenge: orchestration.

The orchestration layer acts as the traffic controller for enterprise AI. It routes tasks to the right harness, determines when human oversight is required, and coordinates work across multiple systems. Rather than treating AI as a standalone capability, orchestration allows organizations to manage AI as an enterprise-wide operational platform.

This capability becomes particularly important in regulated industries, where workflows often cross organizational boundaries and require strong accountability. As AI moves deeper into critical operations, orchestration will become a defining architectural requirement rather than a technical afterthought.

Governance and assurance as strategic differentiator

If orchestration determines where work goes, governance determines whether that work can happen safely and assurance determines whether we can trust it was done as intended. Many organizations initially viewed AI governance and assurance as a compliance exercise rather than a core operational requirement.

Effective governance and assurance encompasses policy enforcement, authorization controls, asset management, cost monitoring, evaluation and audit services, observability, guardrails, and risk management. These capabilities must function consistently whether work occurs within a single AI system or across multiple interconnected workflows.

The need is especially acute as enterprises deploy autonomous and semi-autonomous systems. A model generating an inaccurate answer may be inconvenient. An AI agent taking action in a regulated business process without adequate oversight can be more problematic. Organizations that treat governance and assurance as an integral component of their AI architecture rather than a bolt-on feature will be better positioned to scale adoption while maintaining trust.

Thinking in ecosystems, not products

Collectively, the harness, orchestration capabilities, and governance and assurance layers form what can be viewed as a governed operating environment for AI. Combined with models, data, and compute infrastructure, they become an ecosystem that functions as a single accountable system.

This shift in perspective is important because no single vendor currently delivers every component of that ecosystem. The future of enterprise AI is therefore unlikely to be dominated by one model provider or one platform vendor. Instead, success will depend on how effectively organizations integrate and operate a diverse collection of technologies.

For enterprises, particularly those operating under regulatory, security, or sovereignty requirements, this integration challenge is not optional. AI must operate within boundaries defined by business policy and organizational control. The ability to combine frontier models with protected enterprise data while maintaining accountability will become a critical capability.

The next AI battleground

The model layer will continue to improve. It will also become increasingly interchangeable, but that does not diminish the importance of AI innovation. Instead, it changes where value is created.

The next battleground is the layer surrounding the model: the harnesses that make work reliable, the loops that make systems learn, the orchestration platforms that coordinate activity, and the governance and assurance frameworks that make scale enterprise ready. Together, they will determine whether AI remains an impressive technology demonstration or becomes a sustainable enterprise capability.

In the years ahead, the organizations that gain lasting advantage are likely to be those that stop asking which model is best and start focusing on how to integrate, govern, and operate AI as an accountable business system. The future of enterprise AI belongs not just to the model alone, but to everything around it.

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• £90 million is being allocated toward using excess heat from UK data centers
• In London, around 650,000 homes will receive cut-price heating via a “mega heat project”
• The project is described as delivering “clean heat” to London

The UK government plans to heat around 750,000 homes as part of a new £90 million project that will include supplying hot water heated by data centers. The Department for Energy Security and Net Zero (DESNZ) is funding the building of new heat networks and improving existing infrastructure to pipe hot water from data centers across parts of London, including under the Thames.

The initiative is part of a national project to reduce heating bills, with London, Manchester, Newport and Abergavenny in Wales, Solihull in the Midlands, and Bradford in Yorkshire all benefitting. Similar waste-heat projects have been running in Copenhagen, Amsterdam, Vienna, and Berlin for several decades.

In London alone, 650,000 homes will benefit, although the project has drawn ridicule for the cited use of “electric barges” to transport hot water 25 kilometers along the River Thames, while the subterranean pipe system is installed.

£5 billion unlocked

Diverting heat from data centers into water is the core of the £90 million in government funding, but the mega heat project in London relies on unlocking £5 billion in investment, which will also generate jobs.

Alongside roles in river transport, the money will support construction and engineering projects. The mega heat project under the Thames is expected to distribute heated water from a data center, where enough excess heat is generated to provide enough for homes across London.

While £41 million of the funding goes to the capital, £9.7 million will be invested in extending the city’s original heat network. Heat networks were common in the industrial north in the mid-20th century, although Bradford’s network is a more recent project, commencing in 2024. The projects in Greater Manchester (Gorton and Levenshulme) will replace legacy heating systems in social housing blocks.

The “electric barges”

While initial reaction to the news of London's mega heat project has been positive, the use of “electric barges” to transport water along the River Thames has inspired some derision. However, the £41 million heat project is planned to start as soon as possible.

The barges (which will cover 25 kilometers a day) are heated, with the water temperature sustained until it is deposited into insulated riverside tanks mounted. Expected to launch in 2028, the barges will remain in operation until the new hot water distribution system is completed.

“Clean power and clean heat benefits communities across our country," noted the UK’s Minister for Local Energy and Jobs Martin McCluskey.

"Building the infrastructure to deliver it is reindustrialising the UK and rolling out cutting-edge energy technology so families can benefit from lower bills, good jobs and energy security. And for those already using heat networks, we are upgrading inefficient systems to make sure people get the high-quality, low-cost, reliable heating they deserve.”

• Home puts chats, delegated work, and Office documents inside one interface
• Code lets non-programmers describe software and have Copilot build it
• Autopilot can continue recurring work without waiting for another instruction

Microsoft has introduced a redesigned version of its Copilot AI platform which claims to combine chat, delegated work, and coding tools into a unified application experience for users.

The company says the update is meant to let individuals and organizations scale artificial intelligence across everyday tasks and long-term projects.

Three new capabilities anchor this release, including Home, Code and Autopilot, each aimed at a different kind of work.

Home brings Chat and Cowork together

The Home tool brings together two existing modes, Chat for quick questions and Cowork for tasks users delegate entirely, under one shared starting point.

Word, Excel, and PowerPoint now operate inside this same interface, letting users draft documents, budgets, and presentations without switching applications.

In these documents, Copilot is now grounded in Fabric IQ, pulling context from more than 20 million semantic models built in Power BI.

Edits made by colleagues or by the assistant itself appear in real time, so progress stays synchronized across a shared file.

A new plugin registry will let organizations manage Microsoft, partner and custom-built plugins from one central catalog starting this month.

Independent developers and partners can also publish plugins once for use across multiple Copilot surfaces under the new registry arrangement.

The Code tool allows non-programmers to describe an app, tracker or dashboard in plain language and have it built automatically.

This feature runs on the same underlying technology used in GitHub Copilot and can be hosted within a company's own systems.

Autopilot operates without constant prompting

Autopilot, the third addition, is a persistent agent capable of completing recurring work without needing a new instruction each time.

It can run supplier reviews or similar multi-step processes, build schedules, contact stakeholders, and follow up on outstanding items independently.

Because it operates continuously in the cloud, work can continue late at night or whenever a person's attention shifts elsewhere.

A related feature called Today, entering private preview in October, will summarize missed messages and pending tasks across mail and chat.

Microsoft is also tying spending controls to these tools through a system it calls FinOps for AI, letting administrators track usage.

Administrators can set spending limits, approve credit requests and restrict which AI models different teams are permitted to use each month.

Everyday tasks like quick answers or first drafts run on a fixed-price subscription, while agentic features use usage-based billing.

Code, Cowork and Autopilot all fall under this usage-based pricing model, alongside frontier models Microsoft refers to as Astra and Fable.

Home and Code are set to roll out through Microsoft's Frontier program within weeks, and Autopilot enters private preview by month's end.

Microsoft has not released independent data showing how widely the three features are being adopted, how accurate they are, or how much time they actually save.

• Threat actors spoofed major US HR/payroll platforms with fake desktop clients built via Lovable landing pages
• Victims downloaded a modified ScreenConnect build from GitHub, giving attackers hidden, unattended remote access
• Campaign shows ~291 downloads; likely targets payroll staff, enabling potential wire fraud through diverted payments

Cybercriminals are impersonating large American HR and payroll platforms in attacks that are very difficult to spot, new research from Allure has claimed.

Its report revealed how the as-yet unidentified threat actors were found spoofing three major US HR and payroll platforms, likely picked primarily because they offered a cloud-based service accessible through a browser, rather than a standalone desktop app.

The crooks used Lovable (a legitimate AI-powered service for building websites and landing pages with nothing more than prompts, requiring no technical knowledge whatsoever) to create landing pages imitating the legitimate brands, but with a small (yet important) distinction - they offered a desktop client.

No reference points

Since a legitimate desktop client does not exist, there is nothing to compare the malware to. This, Allure says, makes it very difficult for victims to determine they were being targeted. After all, it would make sense for a major HR and payroll platform to have a desktop app at one point, right?

Those that clicked the download button were served an executable from GitHub Releases, a feature of GitHub that developers use to publish specific, packaged versions of their software. You can think of it as the software equivalent of a product download page. Being a legitimate service (and one frequently used to host software like this), it yet raises no suspicions or red flags.

The executable itself is not malicious either, which is probably the cheekiest part of the attack. As such, it flies under the radar of most antivirus or endpoint protection services and can easily be installed on the device.

"Legitimate" tools

Well if it’s not malicious, what is it? And what is the risk?

The program victims end up installing is a variant of ConnectWise’s ScreenConnect, a remote desktop and remote IT support platform used primarily by IT departments and managed service providers (MSP). It is a legitimate tool that allows IT techs to remotely connect to computers and servers, troubleshoot problems, install software and patches, and more.

But because ScreenConnect provides remote, often privileged access to computers, it is an attractive tool for attackers, and is often used in cyberattacks of different nature.

“This build was configured to surreptitiously do the same thing without the user being aware. We extracted the client configuration and the launch parameters. The access mode is set to unattended. The victim-facing indicators are turned off: no “your machine is being controlled” banner, no system-tray icon, no connection balloon,” the researchers said.

In other words, the variant was configured to allow crooks access without notifying the victim in any way.

Allure did not identify the attackers, nor did it discuss the success of the campaign. We don’t know exactly who it targeted (apart that it aims for finance and HR departments), or how many organizations ended up installing ScreenConnect. The researchers said the GitHub downloads page shows 291 downloads, but that doesn’t necessarily have to mean 291 victims, or successful attacks. It is likely that numerous security researchers downloaded the tools, as well as sandboxes, and that many of the victims realized they were attacked before suffering any meaningful damage. Therefore, the actual number of victims is likely significantly smaller.

We also don’t know what the endgame is, although Allure suggests it might be wire fraud:

“Whoever installs it is the person who runs payroll, and unattended access to that machine is a path to diverting or draining an entire company’s payroll,” they said.

“If your company runs a cloud payroll or HR platform, the most useful thing you can do this week is check whether yours actually ships one [desktop client], and tell employees that a download the vendor does not offer is not an upgrade.”

• DARPA will now test Microsoft's controversial quantum hardware inside a dedicated Maryland facility
• Majorana 2 uses a different approach to tackle quantum computing's stubborn error problem
• Microsoft is putting its topological quantum claims under independent government scrutiny

Microsoft has opened a 15,000 square foot quantum research center inside the University of Maryland's Discovery District, a year after the project was first announced.

The facility gives the Defense Advanced Research Projects Agency (DARPA) physical access to Microsoft's latest topological quantum system for independent testing.

Previously, DARPA and other outside researchers could only test Microsoft's hardware through cloud-based remote access, making this the first time an external agency will evaluate its topological hardware on-site.

DARPA gets hands-on access to Majorana 2

The system installed for testing includes qubits built on Majorana 2, Microsoft's second-generation topological quantum computing chip.

Topological qubits are designed to reduce the burden of quantum error correction compared with other existing qubit approaches currently in use.

Quantum computers remain highly sensitive to environmental noise, meaning useful systems must detect and correct errors repeatedly while calculations are underway.

Majorana 2's material stack replaces aluminum with lead, a substitution Microsoft says improves overall performance and stability.

The chip has drawn criticism from some scientists, who remained skeptical of Microsoft's earlier performance claims, but Microsoft says the delivery to DARPA may help address that skepticism by opening its hardware to an outside testing team which brings expertise across hardware, control systems, software, and applications, according to the company.

Microsoft describes independent access as a necessary step toward assessing whether its approach can produce an economically relevant quantum computer.

The quantum computing industry does not have a broadly useful, fault-tolerant machine that consistently outperforms classical computers on commercially important work.

DARPA's evaluation falls under the Underexplored Systems for Utility-Scale Quantum Computing program, part of its broader Quantum Benchmarking Initiative.

That wider initiative aims to determine whether competing quantum computing approaches can produce commercially useful machines by 2033.

"DARPA is an ideal partner for our work because its test and evaluation team brings expertise across the computing stack," Microsoft said in a blog post.

A shared space for training and industry partners

Beyond the DARPA testing area, the center includes a hands-on quantum hardware makerspace built for students, researchers, and career changers entering the field.

Seven companies have joined this effort so far, including AMD, Intel, Bluefors, IQM, Riverlane, Quantum Motion, and Fermi National Accelerator Laboratory.

Fermilab's involvement also extends into a collaboration exploring how its Quantum Instrumentation Control Kit could support broader quantum education efforts.

In the makerspace, visitors will assemble small but functional quantum systems using parts sourced from several leading hardware and computing companies.

Microsoft credits Maryland's Capital of Quantum Initiative, led by Governor Wes Moore, with funding the renovation of the original Discovery Center building.

The Maryland facility will also let prospective partners test how their own technologies work with Microsoft's broader quantum platform.

Hardware engineers can use the space to integrate machines or components, while developers explore possible applications.

Microsoft's strategy centers on supporting multiple quantum hardware types through one shared software and infrastructure layer.

• An F-35B and Ferrari SF90 will race at Grottaglie Air Base
• Barone's tuned Ferrari hits 330 km/h across the 500-meter sprint
• The jet completes its takeoff exactly as the race course ends

The Italian Navy has organized an unusual matchup between one of its F-35B fighter jets and a Ferrari SF90 sports car.

Set for October 11 2026 at the Grottaglie air base in southern Italy, the event pits vertical takeoff aircraft against Formula-tuned horsepower.

Fabio Barone, who holds seven speed records in Ferrari vehicles, will drive the car during the contest.

A runway built for landing, not racing

The first race covers 500 meters along an aluminum runway typically reserved for F-35B landing approaches at the base.

At that distance, the jet will complete its takeoff just as the course ends. Barone said his Ferrari normally accelerates from zero to 100 km/h in 2.8 seconds under standard settings.

With specialized engine adjustments applied for the event, that time drops slightly to 2.6 seconds flat. Over the full 500-meter stretch, the car is expected to hit 330 km/h before finishing.

"With that acceleration, I will face 2.2g, meaning 140kg of pressure" said Barone, adding the jet will likely start more slowly but carries far greater raw power overall.

A longer second race stretches across 2.8 km, combining both asphalt and aluminum surfaces along the runway.

After the first 500 meters, the aircraft will take flight and finish the course airborne. It will fly roughly 20 meters above the ground for the remainder of that longer race.

The F-35B and FS90 will share a single runway only 35 meters wide, and driving at high speeds over the aluminum plates could destabilize the car's handling

Echoes of a Formula 1 stunt from 2003

Races between military jets and race cars are not without precedent in Italy, dating back over two decades.

In 2003, Formula 1 driver Michael Schumacher raced a Ferrari against an Italian Air Force Eurofighter jet at Grosseto air base (see main picture) in a contest which drew a crowd of roughly 50,000 spectators to watch the two machines go head-to-head.

The Eurofighter won two of those races, covering 900 and 1,200 meters, without ever leaving the ground.

Schumacher still won a shorter 600-meter sprint, reaching 294 km/h and finishing the course in 9.6 seconds, beating the jet by two tenths of a second, despite the aircraft's clear power advantage.

Barone has his own history staging similar stunts involving military hardware and Ferrari engineering, having previously driven a Ferrari along the flight deck of the Navy's Trieste carrier vessel, reaching 164 km/h across roughly 200 meters before braking hard to avoid going overboard.

Barone said the appeal of racing a fighter jet comes from doing something genuinely unusual in public view.

"Everyone can race against another car, and this is a chance to put on a show," he said, citing Schumacher's 2003 run as inspiration.

Public stunts pairing military aircraft against sports cars tend to favor spectacle over strict competitive fairness.

Whichever machine crosses the finish line first, the event mainly serves as a marketing moment for both the Navy and Ferrari.

Via DefenseNews

• Formula 1 expertise is being redirected toward Ukraine's next generation of naval drones
• UFORCE wants racing engineers to solve problems of weight, speed, and endurance
• UFORCE is building one software layer across its different unmanned platforms

Ukrainian defense company UFORCE is looking to recruit engineers from Formula 1 racing teams to help build lighter, faster naval drones for combat use.

The company wants specialists skilled in carbon fiber, composites, sensors, and engine tuning to redesign its MAGURA naval drone family.

UFORCE pairs these new hires with existing Ukrainian engineers to jointly develop faster, lighter combat systems.

Racing know-how meets naval engineering

"Formula 1 specialists have started coming to us en masse," said Oleg Rogynskyy, chief executive officer of UFORCE, noted engineers accustomed to shedding excess weight while preserving reliability bring skills that transfer directly into naval drone design.

Racing engineers routinely redesign components quickly without sacrificing dependability, a balance matching requirements for combat drones operating in harsh environments.

Development teams focus jointly on engine efficiency and composite materials used across multiple drone types.

These teams also balance weight against energy consumption, available computing power, and the operational range required for each mission.

The firm operates research centers in the United Kingdom and United States, partnering with ReconCraft on engineering and manufacturing.

Working outside Ukraine lets engineers design for harsher ocean conditions, including larger waves common across the Atlantic and Pacific waters.

Those conditions require sturdier vessels reinforced with Kevlar material, along with distinct navigation, control, and communication systems for each environment.

Some engineering solutions built for these tougher conditions are later adapted for systems developed specifically for use inside Ukraine.

A wider hiring search beyond motorsport

UFORCE has also approached Ukrainian engineers working at major firms in computing and defense, including OpenAI, Anthropic, Google, Anduril, and Palantir.

This foreign structure allows specialists to support Ukrainian defense projects without relocating their families.

UFORCE currently employs more than 1,000 people worldwide, with most staff still based inside Ukraine despite expanding international operations.

Software remains central to its strategy, since different unmanned platforms share one underlying layer across all equipment types and systems.

Engineers integrating hardware, autonomy, sensors, and computing systems must work across multiple platform types built for very different combat environments.

Its product lineup spans MAGURA naval drones, the Nemesis and R18 air platforms, and several active ground robotic systems.

These systems have together completed more than 150,000 combat missions, while MAGURA drones have reportedly struck more than a dozen Russian vessels.

In May 2025, MAGURA V7 drones fitted with US-made AIM-9 Sidewinder missiles reportedly downed two Russian Su-30SM fighter jets over open water.

That marked the first documented case of a sea drone destroying a manned combat aircraft using guided missiles fired from water.

Rogynskyy said the company ultimately wants to unify land, sea, air, and counter-drone systems under one shared software framework entirely.

Via United24media

• Raspberry Pi firmware has refused RAM capacity changes on Pi 5-family boards since at least September 2024, something that was formally confirmed by a company engineer in June 2026
• The company states that this is intentional: a lock kills the profit in reselling 2GB boards fitted with untested larger chips as 8GB models, preventing bootlegging with substandard components that causes QC issues
• A second Raspberry Pi engineer has stated that same-part repairs still work, and an older bootloader can be flashed onto devices to bypass the check altogether

Raspberry Pi has spent two years locking its newest boards to the memory capacity they left the factory with to prevent scalpers and bootleggers from hoarding its cheapest, low-memory options for third-party upgrades.

At a time when shortages are dictating supply and pricing for most components with on-board memory, one of Raspberry Pi's engineers has an unusually blunt message for anyone hoping to solder in a bigger chip: "don't waste your time trying - it won't work."

The line comes from Phil Elwell, who posts on Raspberry Pi's official forums as PhilE and is identified as the company's senior principal software engineer, making it essentially an official take on the matter.

A policy implemented before the RAM apocalypse began

The confirmation given by Elwell builds on the company implementing hardware locks in two different ways: not only are Raspberry Pi models locked to the memory size they ship with, but in certain cases, a same-part repair- essentially a memory swap to replace a faulty module may not work thanks to other protections being enabled on-device.

Elwell replied to a repair technician who tried to upgrade a Compute Module 5 from 2GB to 4GB, first with a spare chip and then with one lifted from a working 4GB donor module. Each time, the system saw only 2GB, even after the technician adjusted the resistor the board uses to signal memory size and tried flash chips carrying older firmware.

The reason given here is one that centers on both the fact that Raspberry Pi paints itself as an entity that buys memory competitively in the market, passing on savings to consumers, while admitting shortfalls during certain windows.

Also, the fact that Raspberry Pi Holdings does not want to become bogged down in both a shortage of lower-memory-configuration options and acting as a support channel for complaints regarding unofficial memory mods, many of which are doomed to fail or act erratically thanks to untested memory being used.

The rationale is hard to argue with from a business perspective, but they could have handled it better. The check shipped in the 2024-09-23 bootloader, published on GitHub on September 25, 2024, and its release notes read: "Minor updates to align with manufacturer test." It makes no mention of memory checks, and the first official confirmation we get comes from a forum nearly 21 months later.

While the lock was in play before the current memory crunch, it complicates matters when replacement units are prohibitively expensive, especially for higher-end (read: high memory) Raspberry Pi SKUs.

YouTuber Jeff Geerling, who covered this, pointed out a workaround: you could flash an older bootloader (v2024.09.10-2712) that doesn't have this memory check. Doing this, however, essentially cuts one's Raspberry Pi off from future firmware upgrades that include a slew of fixes and security patches, something that is a heavy trade-off to make.

The makers could have handled this better, as he puts it: the Raspberry Pi could just void a one-time memory bit, not online how e-fuses work in modern electronics and consoles that would be written to the moment the Pi detected a 3rd party module.

Ironically, the memory shortage has exacerbated the situation, making it likely that even more people would attempt to find a cheaper way to get a memory upgrade, even as the Raspberry Pi Foundation passes on memory costs to end-users, making for an extremely lucrative business had the lock not existed, which would potentially render thousands of complaints on their forums by users attempting such a mod or having been duped into buying a unit with modified memory from the get-go.

Whether a silent firmware change, confirmed 21 months later in a forum reply, was the right approach for a company whose brand was built on tinkering is a story for another day, however.

• Marlan Space and Loft Orbital announce $1bn program to grow the Altair AI Earth observation constellation from ten satellites to fifty
• The fleet, which is funded by money from a UAE-based investor, Marlan Space will leverage French AI lab Mistral AI's models onboard
• None of the first ten satellites (which are in production) have launched yet, with a stated timeline of five years for deployment

French President Emmanuel Macron used the stage at recent the International Space Summit in Paris to announce a $1 billion program led by Abu Dhabi's Marlan Space and Loft Orbital.

The cash infusion takes their Altair Earth observation constellation from 10 satellites to 50, with French AI company Mistral supplying the models that run onboard.

The satellites will carry radar, optical and other sensors and are designed to interpret what they see before anything reaches the ground, sending an alert within seconds rather than raw imagery that would otherwise hours to download and process.

A sensor network that thinks on the fly and more

The architecture is serious and addresses a real bottleneck: the proposed design will essentially act as an orbital data center with 50 connected "compute units" and a centralized app store that lets users choose which LLMs to run on satellite-collected data.

Customers not only get access through what the partners call an AI application store, but can also choose the models they want, run them on shared spacecraft, and export the data they need to external models if required.

"Working together, Loft Orbital and Marlan Space have assembled a consortium required to build out a 50-satellite, AI-enabled, multi-modal constellation," Alex Greenberg, co-founder and COO at Loft told TechRadar Pro.

"The satellites are produced by Orbitworks at its facility in Abu Dhabi, the region’s largest high-volume site for commercial constellations. Mistral joins the consortium to provide the onboard models giving the constellation its reasoning and natural-language tasking capabilities in orbit, and will power services in the AI application store. More consortium partners will be announced over time."

This deal, therefore, falls into the same category as the space data center plans filed by US-based SpaceX and Blue Origin, with the proposals centering on hosting general-purpose compute and renting it out to potential buyers.

Altair-Next Gen, as the program is called in the Marlan-issued version of the release, processes data collected by its own sensors, even as it stresses that the 50 satellites would allow it to essentially offer something akin to a "permanent capability" versus traditional solutions that beam back data hours later and often require it to be processed on the ground.

The more interesting story, however, is the application store that the program pushes, which is a more novel idea. It lets customers choose which model to use. It also leaves it unclear whether the store would restrict open-source or open-weight models.

At a time when Mistral remains one of the most powerful EU AI brokers, trailing behind only US and China-based frontrunners on frontier AI models, this is an important step for both the European Union and its UAE-based investment backer to invest in an alternative, even as US labs are looking to slow down or 'pace the frontier' over AI concerns, something that has provoked rebukes from other industry players such as Palantir as well as the White House, with the US president himself addressing and downplaying the issue.

• AI adoption has increased dramatically in the UK, new survey claims
• Most workers know or believe that their employer is using AI monitoring tools to keep tabs on them,
• A majority now demand disclosure of monitoring to become a legal requirement

Workers across the globe are increasingly being observed by their managers using AI-based monitoring tools, and want legally enforceable disclosure of the fact, according to a new survey which highlights changing workplace trends in light of the march towards ever-more AI use.

Furthermore, employees feel that AI has raised expectations on how much work they can complete each day, and contributes to their overall stress levels.

Owl Labs' State of Hybrid Work survey spoke to 8,000 workers worldwide, and provides a picture of how UK-based employees are adapting to modern workplace demands.

AI oversight

It is fair to say that employees do not like being watched. The infantilization of the workplace, where managers watch every move, illustrates a breakdown in trust, and the use of AI monitoring tools to observe activity seems to be the latest step in this phenomenon.

The State of Hybrid Work survey shows that 58% of employees believe their employer has deployed AI-based monitoring tools.

Meanwhile, 82% want disclosure of this practice to become a legal requirement. Also of interest is the growth of “work to rule,” the practice whereby employees stick closely to what is in their job description. This is at 38% overall, with 47% of Gen Z and 42% of Millennial workers participation, suggesting the lack of trust runs in both directions.

Elsewhere, the survey has highlighted some other trends. On average, 30% engage in so-called “billboard days” (where employees choose to be in the office the same day as senior colleagues and clients), split between 36% of men and 25% of women. The practice of “job hugging” – holding onto a job that doesn’t offer fulfillment – is also increasing, with 39% of Millennials and 43% of Gen Z admitting to it (35% overall).

Pace of change

The overall findings highlight a changing workplace in which employees can choose to attend on their favoured days, can produce more work with AI assistance, but find AI monitoring intrusive and stressful.

“In recent years, many organisations have moved fast to adopt AI and encourage their employees to embrace the office," said Frank Weishaupt, CEO of Owl Labs.

"Employers have seen productivity gains as a result, but employers need to ensure that the technology, platforms, and policies available to their teams are supporting this increasing pace of change.”

Weishaupt notes that the data they’ve collated illustrates rising pressure and expectations, adding: “Doubling down on employee experience, including by offering better technology, is going to be a key part of employers keeping their best people over the next few years.”

Recent reports suggest that although .COM remains the undisputed domain top dog, several other TLDs are starting to threaten that position – and .WEB may just be a strong contender to do the same.

Although the .WEB TLD has been tied up in legal and admin since it was auctioned off in 2016, it is now about to hit the market, offering a new generic domain which could rival rising stars like .AI and .IO, as well as established giants like .COM.

I caught up with Mike Chattan, Senior Director of Domain Operations at Network Solutions to get his thoughts on how .WEB may change the domain landscape and how businesses can take advantage of this new opportunity.

Can you tell us a little bit about the history of .WEB and why, despite its highprofile, it is only now becoming available for use?

During the last round of new generic top-level domain (gTLD) launches, .WEB was seen as an extension that challenges .COM. It’s short and closely tied to the history of the internet itself through the term 'World Wide Web.’

.WEB generated significant interest because it can give customers more options when their preferred .COM domain isn’t available. Seven applicants competed for .WEB, and the contention was resolved through an ICANN auction in 2016. The subsequent legal and regulatory process delayed the delegation of .WEB for years.

With ICANN now officially delegating .WEB to Verisign in July 2026, the registrar has said it plans to begin offering .WEB later this year. Now, we’re at the point where businesses and individuals can start considering what this extension could mean for them.

How do you see .WEB fitting into the domain landscape alongside powerhouses like .COM and .NET? 

.COM and .NET are what many people associate with a website or the internet, but a domain extension alone doesn’t determine a business’s success. You don’t need a .COM or .NET to build a successful business. It can help with familiarity, but at the end of the day, you drive your own success through your brand, your customer experience, and how effectively you market yourself.

For businesses using .WEB, the key is consistency and education. Use the domain across your website, marketing materials, social media, and other customer touchpoints so people become familiar with it. Don’t assume customers will automatically recognize a new extension or understand that it’s legitimate.

That education is important because people are more cautious about unfamiliar links and domains due to phishing and other forms of online abuse. So, build that trust around the domain over time.

Domains like .AI and .IO have exploded in growth in recent years. How could .WEB change the narrative for modern businesses? 

TLDs such as .IO,.SH, and .AI are technically country code top-level domains (ccTLDs). As we’ve seen, when ccTLDs have zero locality or other restrictions, they can act just like any other gTLD. .AI is a good example here because it’s popular among technology and artificial intelligence companies even though it’s the official ccTLD of Anguilla.

However, while we treat these extensions like gTLDs, some of them still apply ccTLD application rules, which makes it hard and more expensive for users to register.

Carrying and renewal costs are also problems for both corporations and single domain owners when it comes to these ccTLDs that have found other use cases. I will say that once the next round of TLDs comes out and customers have more options, these ccTLDs will become less relevant due to their pricing, unless the registries behind them make changes to accommodate non-country-specific customers. Customers might be able to find a better fit with new gTLDs for a quarter of the price.

How should SMBs evaluate if .WEB is the right TLD for them? 

.WEB is a brand-new TLD that gives SMBs the opportunity to register a name that may be unavailable in other TLDs.

But first, SMBs should consider if .WEB fits their brand, their audience, and the way they want customers to remember them. If they choose .WEB, they should use it consistently across their website, marketing, customer communications, and social channels. Like any new TLD, .WEB will require some education and repetition before it becomes familiar to customers.

Should brands be looking to secure .WEB domains as a defensive move? What are the risks associated with competitors buying up your brand associated .WEB domain? 

One concern that comes with new TLDs is brand protection. There is a lot to protect, and that comes with costs. That said, I don’t think .WEB requires a different brand-protection strategy from other TLDs. The same brand protection practices that apply to other TLDs should apply here as well.

For most brands, the important thing is protecting the second-level domain (SLD), regardless of the extension attached to it, and it needs to be done across the board.

How can brands measure the success of .WEB domains?

It will take time to understand how successful .WEB becomes. Registrations are an obvious starting point, but they don’t tell the whole story. I’d also look at renewal rates, the number of domains actively being used by businesses, adoption across different industries, and whether customers become familiar with .WEB to recognize and trust it.

The registry also has an important role to play. Historically, new TLDs have benefited when their registries invested in educating the market. Radix, for example, stood out during the last gTLD expansion by investing heavily in awareness and education around its TLDs. Verisign has a big opportunity with .WEB because they have an established .COM distribution network and experience operating domain registries.

• New report claims "little direct evidence" that AI is causing graduate unemployment to spike
• Unemployment sees the same seasonal changes every year regardless
• Remote working could be impacting jobs, but they're usually similar jobs

A new report from CESifo has challenged the broadly accepted notion that AI is making it harder for graduates to get jobs, claiming there's no solid evidence that AI has caused a rise in unemployment among recent graduates.

While earlier research may have anticipated stronger labor market impacts, the paper confirms there's still no meaningful impact despite median monthly AI spending per employee doubling since the end of 2025, now that firms have actually got to grips with AI and aren't just experimenting so much anymore.

Crucially, the authors argue that there's so much more to any existing graduate unemployment than just AI, making attribution harder.

AI might not be taking so many younger workers' jobs after all

The paper admits that unemployment typically rises during the summer months, but that coincides with a large amount of graduates entering the market, thus it falls again come fall time. For reference, previous summer unemployment averages among graduates have been between 6.3% and 7.8% since 2022, just before ChatGPT's public preview launch, so not much has changed.

CESifo's research also admits that the most AI-exposed roles have changed as we'd have expected if AI were indeed harming employment, but not statistically significant enough to be able to say that AI is replacing younger human workers.

Interestingly, the paper also correlates graduate unemployment with remote working, suggesting that WFH jobs could actually be more at-risk than AI-exposed roles.

However, because AI-exposed roles are typically the most desk-based jobs, and these are also the ones that are most often suitable for remote setups, it's possible that earlier work could have confused or blended both impacts.

"There has been little direct evidence" that AI will replace jobs, they concluded while admitting that we're still in the very early years of AI and that this is merely "a useful first test."

"These findings do not rule out larger effects in the future," they stressed.

• Citrix patched two critical zero‑days (CVE‑2026‑88771, CVE‑2026‑88772) in NetScaler ADC/Gateway, both enabling remote code execution
• Exploits already observed; CISA added flaws to KEV catalog with a three‑day patch deadline (Sept 30)
• NetScaler appliances are prime targets due to internet exposure, privileged access, and limited monitoring visibility

Citrix has released a patch for two critical zero-day vulnerabilities apparently being actively exploited in real-life attacks.

In its weekend security advisory, Citrix said it fixed two bugs: CVE-2026-88771 and CVE-2026-88772.

It is now urging organizations to apply the fix as soon as possible and defend their premises from potentially disruptive attacks.

What Citrix fixed

The first issue is an improper input validation vulnerability that allows unauthenticated attackers to execute arbitrary commands remotely. It has a severity score of 9.5/10 (critical). The latter is a buffer overflow/memory-corruption vulnerability in Citrix NetScaler ADC and NetScaler Gateway which could allow attackers to execute malicious code remotely, or trigger Denial of Service (DoS). This one, too, carries a severity score of 9.5/10 (critical).

Both flaws affect Citrix NetScaler ADC and Citrix NetScaler Gateway: ADC before 14.1-73.37, before 13.1-64.23, before 14.1-73.37 FIPS, and before 13.1.37.279 FIPS and NDcPP; Gateway before 14.1-73.37 and before 13.1-64.23.

Citrix released patches for the flaws in NetScaler ADC and Gateway 14.1-73.37 and 13.1-64.23, as well as the corresponding FIPS builds.

"Exploits of CVE-2026-88771 and CVE-2026-88772 on unmitigated NetScaler deployments have been observed," Citrix said in the security bulletin.

Abused in the wild

Citrix has thus confirmed what was being reported on, earlier. According to BleepingComputer, Citrix admins took to Reddit to report IT suppliers and security teams contacting their organizations and recommending they shut down NetScaler appliances.

"We got a call from our IT supplier's security team, they couldn't give any details but they advised to shut our Netscalers down immediately," one administrator wrote. Other admins said similar warnings were coming from law enforcement, national cybersecurity agencies, and CERTs. "We are currently rapidly reacting to rumors that multiple unpatched Citrix NetScaler RCE vulnerabilities are circulating in the wild. While details are scarce, the information is credible," security experts watchTowr said in response to the news.

The US Cybersecurity and Infrastructure Security Agency (CISA) also reacted. Both vulnerabilities were added to its catalog of known exploited flaws (KEV) on Sunday, giving Federal Civilian Executive Branch (FCEB) agencies a three-day deadline (until Wednesday, September 30) to patch up.

The Dutch National Cyber Security Center (NCSC-NL) also reacted fast. Even before Citrix publicly disclosed the flaws, the organization allegedly notified organizations in the country about the zero-days. BleepingComputer said “multiple people shared copies of the notification online, which said the agency had received information from a European partner CERT regarding two vulnerabilities that could independently lead to remote code execution.”

Why target NetScaler

This is not the first time cybercriminals are targeting NetScaler appliances. They are often in the crosshairs because they are tasked with providing remote access to internal applications and desktops, and as such, they are exposed to the internet and can be targeted directly.

That makes any zero-day vulnerabilities in the appliances particularly valuable. Bugs that leak authenticated session tokens, those that allow for authentication bypass, or remote code execution, can provide a foothold inside an organization, without requiring any interaction on the victim side. From there, threat actors could steal credentials, access internal resources, or move laterally to deploy ransomware.

Edge appliances like the Citrix NetScaler are also attractive because they’re somewhat harder to monitor, compared to other endpoints. Organizations usually deploy extensive security tools for computers and servers, leaving specialized networking appliances somewhere in the shadows.

This combination of internet exposure, privileged access, and somewhat limited visibility, makes a serious NetScaler vulnerability particularly attractive to both state-sponsored actors, and profit-oriented groups.

Via BleepingComputer

AI tools are now a normal part of the working day. Employees lean on AI for tasks ranging from drafting emails and summarizing content, to preparing legal documents and creating financial reports but, while there’s no doubt that AI and automation are making our workdays more efficient, the more we rely on these tools, the greater the risk of them being exploited, misused or simply trusted too much.

In 2023, US lawyers were sanctioned after filing legal documents built on false citations that were invented by ChatGPT. If experienced professionals are trusting AI outputs without verifying primary sources, even in high-stakes legal proceedings, it raises concerns about our growing dependence on AI and the risks of unchecked and hallucinated data.

But hallucinations are only part of the problem. As employees become more comfortable using AI at work, organizations also need to understand what information employees are feeding into AI engines and whether they have the right controls in place to manage the security, privacy and compliance risks that come with unsanctioned AI use.

Ungoverned AI is no longer a theoretical compliance risk. It’s a real issue that CISOs are having to deal with, from protecting consumer and employee data to defending against AI-enabled attacks. As AI regulation moves from guidance to enforceable requirements, organizations are facing more pressure to understand where AI is being used and who’s responsible for its outputs.

The cost of AI without governance

Ungoverned AI use poses significant risks, particularly when employees share sensitive data with AI tools without clear guidance on how it should be used. That information could then influence business decisions based on unverified AI outputs or fall into the hands of a bad actor looking to exploit the business.

Once sensitive information leaves an organization's control, there’s no telling where it could end up. If an AI platform is breached, data entered into the tool could be exposed, potentially giving attackers exactly what they need to target a company’s systems. What starts as a quick shortcut to save time can just as easily become an entry point for credential theft or serious financial loss.

The EU AI Act’s transparency requirements, applicable from 2 August 2026, expects organizations within scope to be transparent about their business’ AI interactions and AI-generated or manipulated content. It also requires staff to be equipped with an appropriate level of AI literacy, alongside existing regulations such as GDPR and relevant FCA requirements that involve the effective governance of AI processing of personal data. The cost of breaching these transparency requirements can carry fines of up to €15 million or 3% of an organization's total annual turnover.

This means that now more than ever, organizations need to understand exactly how AI is being used, what information is being entered into AI tools and what controls are needed around higher-risk use cases, especially where personal data and financial decisions are involved. This is where AI verification protocols become critical.

Why verification isn’t a one-size-fits-all

A generic AI policy that isn't tailored to specific use cases will tend to get ignored. Employers telling staff to simply "check everything" they receive from these tools isn't realistic or clear enough guidance, since not every AI-assisted task carries the same risk.

Scrutiny needs to scale with what's at stake if the output turns out to be wrong. By treating every task the same, you either take up valuable employee time on unnecessary verification processes or risk a potential security incident because an AI-generated output is moved forward unchecked.

For low-risk tasks like internal drafting, brainstorming and research support, light spot-checking can be enough. However, anything feeding into decisions about employment, finance or customer-facing content needs strict approval processes, ongoing monitoring, and a genuine human in the loop.

Practical steps to reduce the AI hallucination risk

Reducing AI cyber risk starts with proper governance. Organizations need clear rules on which AI tools are approved, what information employees can share within the platforms, and who’s accountable when something goes wrong. Waiting until a hallucinated output causes damage is the most expensive way to learn this lesson.

It’s becoming increasingly common for employees to bring their own AI tools into work. Our latest research found that 40% of CISOs fear their staff are sharing sensitive information with generative AI platforms, while 68% identify employees as their organizations biggest cyber risk. Ungoverned AI is becoming a growing governance and security blind spot for organizations .

But the risk goes far beyond what employees put into AI tools. When organizations don't know which tools are being used or to what extent employees are relying on their outputs, they have limited visibility into whether AI-generated information is being factchecked before it’s acted upon.

Organizations can't manage risks they don't know exist, which means mapping where ungoverned AI is genuinely being used and then offering secure, approved alternatives, rather than banning it completely. This gives organizations greater visibility over how AI is being used and where hallucinations or inaccurate outputs could enter business processes, before they become a liability.

Generic training won’t fix the AI problem

Employees need to be equipped with the right processes, tools and guardrails to use AI effectively, without putting businesses at risk. This includes training to recognize automation bias (our tendency to trust outputs blindly), paired with explicit verification routines that turn "double-check the AI output" from a vague instruction into an actionable process for employees to feel in control of how they can use AI responsibly.

Role-based security awareness targeting and behavior-triggered interventions are also critical for putting the right guidance in front of the right people at the moment they need it, so that sensitive information is protected, and the right human judgement is used.

Ultimately, addressing AI hallucination and the risk of ungoverned AI comes down to training employees properly. Most CISOs (81%) say that security awareness training fails because it’s too generic to feel personally relevant. Without context that reflects the decisions employees face day to day, training gets completed but rarely absorbed and actioned on.

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• Kiteworks warned customers of a “credible” planned cyberattack, urging a nine‑hour precautionary shutdown
• No breaches reported; advisory lifted Sept 27, systems restored, version 9.5.1 deemed secure
• Context recalls Cl0p’s past file‑sharing platform hits, raising speculation about possible resurgence

Secure file sharing company Kiteworks recently told its customers to shut down their servers for nine hours, in anticipation of an incoming cyberattack.

There are no reports of actual attacks - at least not yet, but in a blog post, the company revealed it had received “credible” threat intelligence from federal authorities warning them of a planned attack against the company and its customers.

In response, the company told its customers to shut down their servers for nine hours, depending on their timezone.

What happened?

“Kiteworks is advising customers to facilitate a nine-hour precautionary shutdown window this weekend, in their local time zone,” the announcement reads.

“Customers who self-manage their Kiteworks systems—on-premises or on AWS or Azure—should shut down those systems themselves during this window. Kiteworks will shut down the customer systems it hosts, on behalf of customers, during the same window, so Kiteworks-hosted customers are not required to take any action.”

In the announcement, the company’s Chief Information Security Officer, Frank Balonis, said the measure is proactive rather than reactive, and that there are no ongoing attacks just yet:

“Out of an abundance of caution, we notified customers directly and recommended a precautionary shutdown window while we continue to work through the matter with federal intelligence authorities,” Balonis said.

“We have no indication that Kiteworks or our customers’ systems have been compromised, so this advisory is preventative rather than a response to a confirmed breach.”

Citing German media, BleepingComputer said the shutdown window “applies to customer worldwide”, with affected time zones ranging from Australian Eastern Standard Time, to Pacific Daylight Time. In Central Europe, customers were told to shut down between 4 and 10 AM on Saturday. On the east coast of the US, the shutdown window is between 10PM on a Friday, until 4AM on Saturday.

Who could be gearing up for an assault?

Kiteworks is a US-based enterprise security and secure data-sharing platform, which organizations can use to manage sensitive information, especially when ordinary email attachments or consumer file-sharing services aren’t considered secure enough.

Kiteworks says it does not have any fixes in the pipeline and that all known vulnerabilities are accounted for in the latest version 9.5.1. Obviously, customers are advised to run this version instead of older, potentially vulnerable ones, and were told that other subsidiaries such as Zivver, DRACOON, totemo, ownCloud, WAMNET, Maytech, Bonfy.ai, and 123FormBuilder, were not affected.

Secure data-sharing platforms are an attractive target for cybercriminals, given the high-value organizations that use them, as well as the sensitive information they handle. We don’t know who the attackers might be, but there are some strange coincidences occurring at the moment.

Three years ago, a hacking group known as Cl0p struck two major file-sharing platforms: GoAnywhere MFT and MOVEit. The breaches ended up becoming some of the biggest hits in recent history, affecting thousands of customers. Victims ranged from government agencies, schools and healthcare, to major firms like Sony and PricewaterhouseCoopers (PwC).

Cl0p tried to extort many of these companies, even succeeding with some. The cybersecurity community tried to estimate how much money the group stole this way, and the general consensus seems to be anywhere between $40 and $100 million.

In the months and years following these two incidents, Cl0p vanished. The group was no longer making high-level plays, and we’ve not reported on any major breaches since. Until last week, that is, when it was reported that ShinyHunters - currently one of the most active and dangerous data exfiltration organizations around - attacked Cl0p.

ShinyHunters leaked plenty of Cl0p’s sensitive data, saying it did so in retaliation for threats of doxxing and physical violence made by the group. We don’t know if Cl0p was preparing to hit Kiteworks, but we wouldn’t be too surprised if it was an attempted brand management after being somewhat embarrassed by ShinyHunters.

So far, there were no reports of actual breaches at any of Kiteworks’ customers, but we’re continuing to monitor the situation.

The company said that as of September 27 2026, the shutdown recommendation has been lifted for all customers. “If you have not already restarted, you may bring your Kiteworks system back online. Customers with self-hosted Advanced Forms should contact Customer Support for assistance. All systems Kiteworks hosts on customers’ behalf have been brought back up and are operating normally.”

• Excel cells used to just be for one value – but multi-value lists are soon to be supported
• Windows and Mac users can now try it out in beta
• You'll still have to specifically insert a list

Microsoft Excel has just seen one of the biggest updates to its cell architecture since launching, as users can now add multiple values into one single cell.

Until now, users have been unable to add comma-separate text, such as lists, into one cell, but a helpful update to the spreadsheet software is about to change that forever, with Excel not only seeing it as one text string, but also understanding that each item in a list is a separate value.

Introducing lists, arrays in cells and nested arrays as new features, Excel Senior Product Manager Jake Armstrong explained: "You can keep those values in one cell, while also keeping them separate for filtering, calculation and more."

Excel cells can now hold multiple values, like lists

Crucially, Excel won't suddenly start behaving like Word overnight, and users will still need to handle some very basic setup to create a list by inserting it from the menu. Commas and semicolons will then go on to denote a list within a single cell, allowing other core Excel functions like filtering to still work.

At the same time, Microsoft is also adding array and nested array support to single cells and four new functions – FLATTEN, HAS, HASANY and HASALL – to interact with them.

For now, though, these updates are rolling out to Microsoft Excel for Windows and Mac Beta Channels, so they're not yet generally available across the board. As such, there are some known issues involving conditional formatting, charts, find & replace and more.

More broadly, Microsoft's Excel roadmap is filled with plenty of other exciting upgrades for Excel as the spreadsheet software continues to transition into far more than just a basic number-handling tool – Microsoft knows that more and more users are using it for complex text entries, via AI and coding interactions, and the roadmap is evidence that Excel is adapting accordingly.

• Researcher claims intense agentic activity at UN likely tied to OpenAI
• Failed API requests caused them to find alternative solutions
• They were even using sandboxed browsers to overcome hurdles

A new report has revealed AI agents which were "highly likely" to have been operated by OpenAI made more than 16,000 scans against UNCTADstat, a statistics platform operated by the UN, and all of this in just two months between April 13 and June 19 2026.

While the report from independent researcher Rowan Howard-Jones covers some of the in-demand topics, like employment data and other economic statistics, the real shocker is the extent at which the agents would go to when straightforward requests failed.

"Agents bruteforced API fields in UNCTADstat to locate endpoints and retrieve data," the report summarizes.

UN data hub flooded with agentic AI activity

Though the author ties several pieces of evidence to OpenAI agents, it's not been confirmed that the ChatGPT maker was behind these streams of traffic. For example, CHATGPTTEST1 and OAI_META_1312 were among the labels used, and there was also a connection to FractalWiki, a public wiki previously associated with OpenAI activity.

One of the biggest shockers was how the AI agents went about retrieving data, because upon failing to gain entry via the API, the agents apparently started behaving as if the supplied key was incorrect, experimenting with other names.

The agents were also observed opening URLs inside a sandboxed browser via Urlquery, allowing them to make request they apparently couldn't make directly from their own environments.

But better still, these processes were seen to have been self-healed and refined over time to improve efficiency.

"We’re reviewing these findings and have reached out to the U.N. to offer a briefing with the team conducting that review," an OpenAI spokesperson told The Wall Street Journal, implying some form of ownership, but importantly, because the API keys were publicly exposed and the information being sought was public, the activity itself has not been deemed as any sort of hack.

However, with a relatively straightforward request leading to some pretty intense behavior, it certainly raises questions about agentic security.

Launching a business has never been easier. AI tools can generate brand names, build websites, create logos, and produce marketing copy in hours.

It's a remarkable shift for entrepreneurs - but the same technology is also making it easier for cybercriminals to create convincing digital impersonations at scale.

Recent allegations by Google claim cybercriminals used AI to generate 1.5 million malicious URLs designed to mimic legitimate organizations.

As AI lowers the cost of creating convincing websites, businesses face a new challenge: proving they're real.

That's why conversation around domains is changing. The hardest part of launching a business is no longer getting online; it's establishing a distinctive brand that customers can recognize, trust, and confidently return to.

The problem isn't .com's relevance but its availability

For decades, securing a .com domain was considered the first milestone of establishing an online presence. It became the default because it was familiar, widely recognized, and trusted.

That hasn't changed. A .com domain still carries value and remains the first choice for many organizations. What has changed is availability.

According to The Domain Name Industry Brief's (DNIB) latest quarterly report, there are more than 166 million .com domains registered with domain registrars. Many short, memorable .com domains are already registered, held defensively, or owned by investors.

Businesses launching today might discover that their ideal domain has long since been taken, forcing them to settle for longer names, additional words, or unconventional spellings that can make brands harder to remember.

This doesn't signal the decline of .com. Instead, it's encouraging organizations to become more intentional about how they establish their online identity. Rather than focusing solely on securing a .com address, businesses are evaluating whether their domain reflects their brand, communicates what they do, and supports long-term recognition.

Organizations are looking beyond convention and exploring domain extensions that better align with their brand, industry, or audience. Not because .com has lost its value, but because a memorable and distinctive online presence has become just as important.

Domains have evolved from web addresses into brand assets

A domain name has traditionally been viewed as a technical requirement; a web address customers type into a browser. Today, it’s much more than that.

A domain shapes the first impression customers have of a business. It appears in search results, LLM citations, email addresses, advertisements, social media profiles, and digital marketing campaigns. It is one of the few digital assets a business owns.

Businesses invest heavily in building audiences across social media platforms, but those platforms ultimately control the experience. Algorithms change, policies evolve, and visibility can change overnight. A domain, however, provides a stable identity that remains under the business’ control.

This shift is changing how organizations approach domain strategy. Businesses are asking whether it reinforces their brand, communicates what they offer, and supports long-term recognition.

Alternative domains have become part of that conversation. A technology startup may choose an .ai domain. An ecommerce retailer might adopt a .store extension. Creative agencies, consultants, and online communities are similarly exploring domain names that better align with their industries and audiences.

These choices are less about following trends and more about improving clarity. A well-chosen domain can communicate something meaningful before a visitor even reaches the homepage.

Digital identity is becoming a competitive advantage

AI has accelerated more than business creation. It has also made it easier to imitate legitimate organizations.

Generative AI simplifies the creation of convincing fake websites. Professional-looking branding, realistic copy, and cloned user experiences can now be produced at a scale that would have been difficult just a few years ago.

As a result, businesses face growing pressure to make their digital presence unmistakably authentic. A customer who lands on the wrong website may not have another opportunity to determine which business is genuine. In this era, a domain is not just a destination for customers. It's part of a broader trust strategy.

Business owners should think beyond individual websites and consider their entire domain portfolio. Relevant brand variations, country-specific domains, and campaign domains all contribute to protecting digital identity, while forgotten domains, expired microsites, or unmanaged registrations can create opportunities for phishing, typosquatting, and brand impersonation.

Internet Corporation for Assigned Names and Numbers (ICANN) recently closed their first application opening for new generic top-level domains since 2012, which means more TLDs will be available for business owners and impersonators. It's not enough to focus only on your primary business domain. Businesses need to consider monitoring registrations and new TLDs and understand where their brand sits within the domain landscape and decide when and how to protect their names.

Protecting online identity is no longer solely the responsibility of cybersecurity teams. It requires coordination across marketing, IT, legal, and security as organizations work together to protect customer trust.

More choice creates more opportunity

The growing range of domain extensions is giving businesses greater flexibility in how they establish their online identity.

For many businesses, .com remains an important part of their domain portfolio. But businesses are recognizing that different extensions can serve different strategic purposes.

A global organization may continue using its .com as its primary corporate website while adopting other extensions for regional initiatives, product launches, marketing campaigns, or specialized services. Startups may choose an extension that more reflects their industry while maintaining consistency across their broader brand.

The conversation is shifting from finding the right extension to building the right domain strategy.

As businesses expand into new markets, introduce new products, or adopt emerging technologies, their digital presence should evolve alongside them. The growing diversity of domain extensions gives organizations more tools to support that evolution.

Owning your identity matters more than ever

A .com domain is still one of the internet's most valuable assets, and there is little reason to believe that will change anytime soon.

The bigger shift is how businesses think about domains. AI can help launch companies faster, but it also helps malicious actors imitate them more convincingly. That’s why domains are more than just navigation tools. They also prove authenticity.

The future is more than choosing between .com and newer domain extensions. It's also about being intentional - building a domain strategy that helps customers recognize the genuine business while supporting long-term growth and resilience in an AI-driven internet.

To put this into practice, businesses should prioritize:

•Monitoring for typosquat and lookalike domains

•Auditing their domain portfolio regularly for unused domains

•Registering brand domains across other reputable domain extensions

•Maintaining an accurate domain inventory

•Making legitimate customer communications easy to verify

Together, this strategy can help protect a brand’s reputation online, no matter what domain extensions are used.

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Social media has become central to how businesses communicate with customers, promote their brands, and build trust. But as businesses have relied more on these channels, they have also become a more attractive target for cybercriminals.

New research has found that social media impersonation and defamation have risen from the fifth greatest cyber threat last year to the top expected cybersecurity threat over the next three years. The data also revealed that employee and executive impersonation, including deepfakes, has entered the top five areas of risk for the first time.

These findings point to a wider challenge for security teams. Cybercriminals are still trying to compromise systems, but they are also increasingly using other ways to exploit legitimate brands’ hard-earned trust. That trust might sit in a senior leader’s online profile, a brand’s social media presence, a customer service account, a domain name, or a website that looks legitimate at first glance.

For security leaders, this means the attack surface is no longer defined only by infrastructure they own and control. It also includes the public-facing channels where customers, employees, and partners interact with the company every day.

Security strategies now need to reflect that reality. A threat may start with a fake profile, but it rarely stays there.

Social media and identity have become frontline cyber risks

Fake profiles and fraudulent customer service accounts are not new tactics, but they are becoming more prevalent and are increasingly being used as entry points for phishing, fraud, counterfeit sales, and wider brand abuse.

And this is all happening at a time when cybersecurity teams are already under pressure. Recent research found that 72% of senior technology leaders said the level of cybersecurity threats faced by their organization in 2025 was either “critical” or “very critical”.

On social media, criminals can meet potential victims where they are already used to engaging with brands. Fake accounts can be used to share malicious links, promote fraudulent schemes, spread false information, or pose as legitimate support channels.

Attackers may then direct users to a “second location”, such as a lookalike website, fake login portal, or phishing page, where victims are encouraged to enter account credentials, payment information, or other sensitive data.

A separate but related risk is counterfeiting. Fraudulent social media accounts can impersonate legitimate brands to advertise fake goods, directing customers to websites where counterfeit products are presented as genuine. In both cases, criminals are using the trust associated with a recognized brand to make the next step of the attack appear credible.

The impact can also go beyond fraud and counterfeiting. Impersonation or fake accounts can be used to spread false or defamatory claims about a company or its employees, potentially damaging reputation and customer confidence even when no direct financial fraud takes place.

As such, social media abuse, domain impersonation, and identity-based fraud should be treated as part of the same threat landscape.

AI is making impersonation faster and more convincing

AI is making impersonation faster and more convincing. Criminals can use AI tools to imitate a brand’s tone of voice, generate convincing customer messages, produce realistic imagery, or create more credible fake profiles at speed.

Deepfakes, synthetic audio, and AI-generated content can also strengthen employee or executive impersonation, particularly when combined with information gathered from multiple sources. For example, an attacker could create a fake executive profile and use AI-generated messages that mimic how that individual communicates, making requests or links appear more credible.

But AI is only one part of the threat. Attackers can also use established techniques to build the infrastructure behind impersonation campaigns. Domain generation algorithms (DGAs), for example, can produce large numbers of plausible lookalike web addresses to support phishing and impersonation campaigns. DGAs are not inherently AI-powered, but they remain a concern for security leaders, with 86% of respondents viewing them as a threat.

These domains can then be combined with fake social media profiles to create a more convincing digital presence. A fake executive account may point to a fraudulent landing page. A counterfeit product post may direct customers to a lookalike domain.

For customers and employees, these attacks are becoming harder to identify based on appearance alone. The signs of fraud may be subtle, and by the time an impersonation attempt is reported, the campaign may already have moved to another account, page, or domain.

Businesses need a more connected approach to protecting trust

Social media impersonation should be treated as part of the wider cybersecurity strategy, rather than as a standalone brand or communications issue. That requires coordination between the teams responsible for protecting organizations, their people, and their customers.

Attacks often move from a fake profile to a lookalike domain, phishing page, or fraudulent website, so companies need a connected view of where threats are emerging. Social media teams may spot one warning sign. Security teams may see another. Legal or brand protection teams may hold the takedown process. A coordinated approach can help companies respond more effectively.

Alongside those measures, organizations need continuous monitoring across social media, domains, websites, and other public-facing digital assets to identify impersonation, brand abuse, and related threats occurring beyond infrastructure they directly control. Bringing these signals together can help teams recognize when activity across different channels forms part of the same campaign.

Clear processes for investigation, escalation, and takedown are essential, particularly as AI enables campaigns to spread, adapt, and reappear rapidly. Businesses need to know who owns the response, what evidence is needed, and how quickly action can be taken.

Technology will also play a growing role. More than half of respondents, 57%, confirmed that they use AI-based monitoring and enforcement solutions, while 44% use AI-based solutions for threat detection and fraud prevention. Given the 24/7 nature of the internet and how quickly malicious accounts and materials can appear, some level of automated detection is increasingly important for identifying threats at scale.

This is an important step, but tools alone will not solve the problem. Companies need the right governance, clear ownership, and close collaboration between security, legal, marketing, and digital teams.

The goal should be to detect and stop impersonation before it reaches customers, employees, or partners and causes wider damage.

Looking ahead

Social media is no longer simply a communications platform that sits outside the traditional cybersecurity perimeter. It has become one of the main environments in which criminals exploit trust, impersonate legitimate people, and manipulate victims into falling for fraud.

As AI makes these attacks faster and more convincing, businesses will need to connect social media protection with their broader approach to identity, domains, and digital brand security. This means monitoring for threats across the full online journey, sharing intelligence between teams, and responding before an impersonation campaign has time to spread.

The companies best equipped to protect customers will recognize that trust itself has become a target and treat the channels where that trust is built as a core part of their cybersecurity strategy.

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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.

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• Adobe Acrobat study claims 99% of C-suite leaders use AI, but only 41% of regular workers use it
• Use cases are far more advanced among business leaders
• Data security and privacy concerns are still major blockers

New Adobe Acrobat research has revealed a growing disparity in AI adoption at work among UK adults, with 99% of C-suite leaders using AI tools compared with just 41% of non-management employees.

But it's not just a case of who's using AI and how much – it's also which tasks they're using it for, and the most common use case for non-management workers is document creation and editing (17%). In other words, basic creative work.

In comparison, leaders are clearly using AI for higher-level decision-making, with use cases spanning data analysis (35%), meeting and task management (33%) and marketing (25%).

Managers are using AI for more reasons than regular workers

As a result of this disparity, 37% of C-suite leaders say they save three to five hours a week thanks to AI, compared with just 13% of knowledge workers. Adobe argues this could imply that access to AI, knowledge of how to use it and support for its adoption may vary depending on the type of employee.

For example, 74% of C-suite leaders say they're willing to share documents with AI, but only 20% of other workers feel the same way – that's a huge difference.

In its report, Adobe begins by recommending some basic AI use cases designed to alleviate some of the administrative pressures workers already face. For example, creating drafts and summaries covering meetings, emails and collaboration can reduce the need for some of the less creative work, freeing up time for more productive work.

But Adobe also stressed the importance of building organization-wide confidence to iron out the same old concerns we've been seeing since day one – once companies can get on top of security, privacy and transparency, workers are less likely to worry about potential risks.

For now, though, with data security and privacy concerns (22%) remaining some of the highest challenges to AI adoption, enterprises have a clear area to improve.

The Voice AI Agents market is growing exponentially. The industry is projected to grow from $2.4 billion in 2024 to $47.5 billion by 2034, while Gartner predicts that by 2028, 70% of customer service journeys will begin with a conversational AI interface.

For global brands, the appeal is obvious. Voice AI offers the potential to deliver consistent, scalable customer support across markets, languages and time zones.

But there is a risk that voice AI will face the same issue faced by outsourced customer service centers, where differences in language and culture create a disconnect and frustration between the brand and customer.

As voice AI advances, language may become less of a barrier. But speaking the same language is not the same as understanding someone.

It’s just one part of the puzzle. Customers bring accents, habits and cultural expectations to every interaction, and they expect support to reflect their reality.

For that reason, localization needs to become a core test of voice AI performance, rather than a translation exercise that happens at the end of development.

What does it mean for voice AI to be ‘local’?

Accents, dialects, terminology and cultural norms can vary considerable even when speaking the same language. In Scotland, for example, a customer might say “aye”, rather than “yes”, refer to something small as “wee”, or talk about “getting the messages” when they mean going shopping. The language is English but understanding the interaction requires familiarity with how that language is used locally.

Cultures also differ in their expectations around directness or politeness. Even something as simple as how a customer is addressed can matter. In some cultures, using titles and surnames is an important sign of respect, while in others, addressing someone by their first name is the norm. A voice agent that is too informal could come across overly familiar, while one that is too formal might feel distant or unnatural.

All this to say that just because an AI might sound like me, it doesn’t necessarily mean it understands me or is equipped to serve someone in my market.

These aren't cosmetic details. They shape an AI agent’s ability to understand intent and respond appropriately and, ultimately, determine whether customers trust the experience it provides.

It doesn’t sound like a robot anymore

This isn’t to say there haven’t been huge advances in AI. There’s a reason why so many brands are investing in voice AI for CX, and that’s because it's becoming so convincing. Just look at ElevenLabs, for example.

The organization has built its success on AI-generated voices that can reproduce not just speech, but the pacing, intonation and emotion that make a voice sound human. Its technology not only spans voice cloning, multilingual speech and conversational agents, helping take synthetic voice from a novelty to something businesses can realistically deploy at scale.

The days when synthetic speech was instantly recognizable as robotic are fading. Early digital voices such as ‘Microsoft Sam’ left little doubt that you were listening to a machine. Today, the line is becoming much harder to distinguish. As voice AI becomes more convincing, our interactions with it can start to feel less like using a piece of technology and more like engaging with another person.

The technology's ability to create a sense of social presence is already apparent beyond customer service. Even actors such as Matthew McConaughey are getting behind AI voice technology, where it can match their performance without requiring them to change the way they sound, demonstrating just how powerful voice and conversational cues can be in shaping our perception.

But the most advanced experiences aren’t universally available, meaning many people may still be exposed to underdeveloped AI voices. What’s more, humans are remarkably good at noticing when something is slightly off. This is what creates the uncanny valley effect.

An unnatural pause, misplaced emphasis or strangely enthusiastic response can quickly break the illusion. Local context adds another layer to that challenge. A voice can sound perfectly human while still sounding culturally out of place. That said, the closer AI gets to human speech, the more conspicuous those moments can become.

Global brands can’t rely on one global answer

This is where global brands need to resist the temptation to treat voice AI as a single product that can be rolled out everywhere.

A frontier model might provide the foundation, but its performance needs to be tested against local data and real customer interactions so it can capture what ‘good’ looks like in terms of:

- The empathy that de-escalates a frustrated customer

- The specific terminology a market actually uses

- The intonation that reads as natural

All of these can't be inferred by a frontier model; it has to be learned market by market, brand by brand. Successful voice AI models will have an ongoing feedback loop which monitors interactions within individual markets, identifies where misunderstanding or friction occurs, refines the experience and tests again.

Local teams can play an active role in this process as well, bringing market expertise into development rather than simply receiving the “finished product”.

It can’t be treated as a job that is ever truly finished. Language and slang changes, customer expectations shift and new trends introduce expressions and behaviors that may not have appeared in the original training data.

Final summary

Getting this wrong can have an impact on a brand’s reputation. As voice AI becomes more human, customers will naturally expect more from it.

Derby City Council recently experienced this with its AI assistant, Darcie, which struggled to understand a presenter with a strong Derbyshire accent who used local expressions such as “mardy” and “duck,” despite having been upgraded to support nine additional languages.

It is a useful reminder that multilingual does not necessarily mean localized. If a system repeatedly misunderstands an accent or fails to recognize local expressions and expectations, customers will not experience it as a technology problem.

Instead, it will shape their perception of the organization behind it. The brands that get real value out of the voice AI boom won’t be the ones whose technology can speak all 7,170 languages in existence today. They will be those whose customers feel the most understood.

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Removing the Qidi Q2 from the box, I was instantly pleased to see that this new machine reflected everything that I have liked about the Qidi Plus4 machine that I looked at a couple of years ago. However, this is a smaller version with a footprint that is better suited to smaller workshops and studios.

Once again, it’s easy to write off these machines. Qidi , while in the business for as long as many of the big-name brands, just isn’t as well-known, and their machines just don’t have that spark of innovation that we see from the likes of Prusa Research, Bambu Lab, Creality, Elegoo or AnyCubic; however, above all, bar possibly Prusa, Qidi have reliability.

It’s been a busy few months, and the Q2 review has been a long time coming. It’s essentially been sitting, printing for 6 months without issue or fuss: switch on, print, whichever material I need for any given job, usually PLA but ASA, ABS and PETG-CF, same reliability, same print quality.

The main issue is that when a machine starts to blend in, just works as part of the workshop and now classroom you don’t tend to think of it as anything other than having always been there, and that really sums up the Qidi , while it may not have the cutting edge features, what it does have is absolute refinement and reliability.

As I have found in both a business and an educational environment, reliability is far more important than any headline speed figures, multitool changers, or other cutting-edge features when all you need to do is print.

QIDI Q2 3D Printer: Design

Positioning the 402 x 438 x 494mm frame of the Qidi Q2 onto the workbench was something of a relief; the past three printers and laser engravers have all recently gone for scale, so to have something smaller and quite as neat and robust as the Q2 to move around is a welcome break. While it is smaller, it still offers a decent print area of 270x270x256; this will enable you to print most objects you’ll need for prototyping, although cosplay props and helmets will require the models to be split and produced in parts.

One of the key points for me was that the Q2 comes in at 18.1kg, so while that is still quite weighty, it’s not up to the backbreaking weight of many of the other new machines, which in smaller workshops means that it’s easy to move and store when needed.

The construction is all good and solid, and with the CoreXY mechanics and the full-metal frame it all feels a little more robust than some of the other printers that I have tested recently. I also like the fact that it, which has been product-designed, is more in line with workshop machinery, so slips into that environment neatly.

In the print workroom, which is where the Qidi machines tend to end up, the Q2 has sat alongside the larger Plus4 handling the smaller props prints and artworks that we’re required for the run-up to summer. The quality, speed and ultimately reliability have made this, alongside the other Qidi machine, a valuable addition.

All the features that are essential for education, especially, are here, with the fully enclosed construction which keeps students' fingers away from the heated internal chamber and, more importantly, the hot end.

The interface is also extremely straightforward, with the 4.3-inch touchscreen making it easy to navigate. What I liked was that the calibration is automatic, so once the machine is set, you can just leave it.

The auto bed levelling with the standard dual-sided textured PEI build plate meant that there were few, if any, misprints, and once prints cool, they’re easy to remove.

When it comes to the build, the Qidi has been designed as a solid and reliable part of the studio, and a printer that blends in rather than standing out and demanding constant attention.

QIDI Q2 3D Printer: Features

Taking a look at the Q2’s features, while impressive, it positions this fully enclosed printer at entry level these days, which, compared with printers just a couple of years ago, shows just how far the technology has progressed.

The fully enclosed design gives the machine its premium look and feel and houses the CoreXY motion system using heavier-weight 1.5GT belts, with linear guide rail used rather than rods on the X-axis and dual independent lead-screw motors controlling the Z-axis. These are all features that ensured Bambu Lab’s position when they launched, and now even in this entry-level machine they’re a standard.

Toolhead speed is 600mm/s, fast but not unusual at this level, with acceleration of up to 20,000mm/s². Compared with the old Cartesian machines, this is impressive, but with many machines now reaching 1000mm/s, it puts this machine's print speeds at a reliable and steady pace.

The machine is shipped with QidiSlicer, which, as with most slicers these days, is a derivative of Prusa Slicer, although here it has been heavily adapted to enable ease of use. What I liked through the test is that the default profiles are solid and well tested. If you’re new to 3D printing, then it’s just a case of selecting your filament type and the software then the machine sorts the rest. Through this test, I stuck to these default profiles, and the consistency with the QIDI and other manufacturer filaments was superb.

The print volume is 270 x 270 x 256mm, which is a decent size, with most standard 3D printers offering around the 250 x 250 x 250 mm volume. The platform itself is an aluminium-substrate heated bed topped by a removable dual-sided textured PEI plate with a bed temperature that can reach 120°C.

The standard hotend nozzle impressively reaches a temperature of 370°C, meaning that out of the box the bimetal 0.4mm nozzle is equipped to deal with engineering materials. As one feature point that really highlights the direction of the QIDI 3D printers, it uses a direct-drive extrusion system with hardened-steel gears, again built for durability and maximum filament support.

As I mentioned, the 0.4mm bimetal nozzle is fitted as standard; however, if you want to swap it out for something else, then there are 0.2, 0.6 and 0.8mm alternatives available. While you can print with abrasive materials as I did through the test, there is a Tungsten Carbide nozzle available which will last far longer.

The material compatibility list is extensive, with the usual PLA, PETG and TPU, and support for ABS, ASA, PA and PC. The BiMetal nozzle will also print carbon- and glass-fibre reinforced polymers, although this is where I would invest the additional £60/$60 for the Tungsten Carbide hotend.

The Q2 is a fully enclosed machine with active PTC chamber heating system that can push the internal temperature up to 65°C; this is ideal for materials such as ABS, ASA and engineering polymers. The heat around the print enables better control over warping and other print problems for these more advanced material types.

There are several points around the engineering materials that are worth knowing before you start using them on this printer. While it can print with them out of the box, if you’re going to be using carbon-fibre or nylon on a regular basis, I would invest in a filament dryer and that hardened hotend.

Looking at a few of the features that have been introduced that actually make this machine an interesting option for small studios and education are additions such as the G3 pre-filter, H12 HEPA filter and coconut-shell activated-carbon filter. These essentially take out some of the fumes and smaller particles that some of the more advanced materials can make.

The Q2 uses hands-free automatic levelling with a load-cell sensor integrated directly into the hotend. The nozzle itself can therefore be used during the levelling process, removing the need for a separate offset probe.

In everyday use, the important point is that calibration is straightforward and, once complete, I’ve found little reason to start making manual fine adjustments.

That’s exactly what I want from a machine being used in education.

The purpose of the printer is to make things. If you’re continually adjusting the printer itself, something has gone wrong with that relationship.

Other advanced features include the 1080p resolution camera that enables you to monitor prints and create time-lapse sequences; this camera is now AI-enhanced, so it will spot defects automatically.

More standard features, including WiFi across a 2.4GHz connection, Ethernet and USB, are all available, and the 4.3-inch, 480 x 272 touchscreen enables you to easily switch through settings.

QIDI Q2 3D Printer: Performance

After having the relative complexity of the multi-filament machines over the last year, it’s actually quite nice to go back to the simplicity of a single filament. There is the option to go multifilament, but in this review I’m just looking at the base machine, and as the first few prints finished, I was instantly impressed by the quality of the PLA prints.

As I ran through the stock models that had been provided and then the additional test prints, the quality and speed were consistent. This is a mid-speed printer, and while it’s not as fast as some of the machines I’ve looked at recently, at this price the speed and quality are well balanced.

What is apparent is that, consistently and unusually due to the volume of printers and engravers that have been coming through the workshop, this machine has been printing consistently for six months without issue. This is part of the reason that I’ve delayed the review: firstly, waiting for the Qidi Box, and secondly, it just became a part of the workshop that was in constant use.

This reliability has been seen with the other QIDI printers that I’ve used, with the Plus4 now having been run for almost two years in the college; it's the one printer of many that has consistently worked and meets the safety requirements.

Checking the quality of the prints and the first layer after the auto calibration is always slow and precise, and at no point have I had to make any adjustments to that initial calibration height.

The prints also hold firm during printing, and if printing multiple prints across the heated bed, they all stick firmly through the process, releasing easily when the platform cools.

What was also apparent was that after a six-week break at the college, where the machine has sat mostly unused, all it took to get it back into action was to power it on. A quick bench test, and the machine was printing perfectly.

Taking a look at the benchmark tests, the results were impressive.

QIDI Q2 3D Printer: Print quality

Dimensional accuracy - score of 4

Target 25 = X: 24.90mm / 0.10mm Error | Y: 24.87mm / 0.13mm Error
Target 20 = X: 19.88mm / 0.12mm Error | Y: 19.80mm / 0.20mm Error
Target 15 = X: 14.90mm / 0.10mm Error | Y: 14.93mm / 0.07mm Error
Target 10 = X: 9.89mm / 0.11mm Error | Y: 9.78mm / 0.22mm Error
Target 5 = X: 4.88mm / 0.12mm Error | Y: 4.89mm / 0.11mm Error
X Error Average = 0.11
Y Error Average = 0.146
X&Y Error Average = 0.128

Fine Flow Control - score of 5

Fine Negative Features - score of 5

Overhangs - score of 5

Bridging - score of 5

XY resonance - score of 2.5

Z-axis alignment - score of 2.5

Adding up the totals gives a final score of 29 out of 30.

QIDI Q2 3D Printer: Final verdict

The review of this 3D printer has stretched over a far longer period than most, with the machine being set up and used for the last six months. While the pricing places this at the entry level, the build quality and reliability make it clear that this machine strikes a careful balance.

It gives you a standard out-of-the-box machine that is a reliable tool in the workshop, and if you work in education or have a small business or design studio, then that is exactly what it provides.

The Q2, used as a single-filament machine as I have looked at it in this review, offers a solid, no-fuss approach and huge support for a good range of materials. Key features include the CoreXY design, cooling system, filter and fully enclosed design that enable everyone from a complete beginner through to professionals to have a machine that will be reliable and easy to use.

The Qidi software is again well thought out, and the stripped-down approach again makes it an ideal option for those who are new to 3D printing; the machine connection and the slicing workflow are all well thought through.

This might not be a machine that shouts. out with the latest features and cutting-edge additions, but instead it refines this style of machine perfectly. If you want more, then there are the other hotend options, and there’s the QIDI box for multifilament printing.

Essentially if you need a no fuss, solid and reliable 3D printer then the Qidi Q2 is a great option, while there is the multifilament option at present as a single filament machine it’s a great price and close to perfect for what it is.

Should You Buy the Qidi Q2 3D printer?

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• X-energy has completed the main structure of its Tennessee fuel plant called TX-1
• The facility could eventually produce 700,000 TRISO fuel pebbles annually
• TX-1 will be America's first new commercial-scale advanced fuel facility in decades

X-energy has completed the main structure of TX-1, a nuclear fuel manufacturing facility located in Oak Ridge, Tennessee.

The 214,000-square-foot plant, developed by subsidiary TRISO-X, represents the first commercial-scale advanced nuclear fuel facility built domestically in over 50 years.

Clark Construction Group, the contractor handling the project, oversaw this vertical construction phase, clearing the path toward interior systems and eventual equipment installation.

Manufacturing capacity built for a growing fleet of reactors

TRISO fuel consists of tiny uranium particles wrapped in multiple carbon- and ceramic-based protective layers for durability.

Those layered coatings contain fission products while letting the fuel withstand temperatures far higher than conventional nuclear fuel typically tolerates.

The TX-1 plant is expected to produce roughly 700,000 TRISO fuel pebbles annually when it commences full operations, consuming about 5 metric tons of uranium, which would supply enough fuel for as many as 11 of X-energy's Xe-100 advanced reactor units combined.

Each Xe-100 reactor module produces 80 megawatts of electricity, with four combined modules forming one standard 320-megawatt plant.

TX-1 will initially supply fuel for X-energy's planned Xe-100 deployment at Dow's UCC Seadrift Operations manufacturing site in Texas.

"Completing vertical construction is a major milestone for TX-1 and another tangible demonstration of the progress our team is making in Oak Ridge," said Joel Duling, president of TRISO-X.

Clark Construction Group will now shift focus toward process equipment, an administration building, and an adjacent graphite matrix powder facility.

Earlier this year, the Nuclear Regulatory Commission granted the plant a 40-year Special Nuclear Material License for operations ahead.

X-energy says this represents the first NRC Category 2 fuel fabrication license issued for processing high-assay low-enriched uranium, known as HALEU.

Expanding beyond one site as demand for advanced fuel grows

TRISO-X has separately expanded its broader Oak Ridge nuclear fuel campus by approximately 70 additional acres of land.

The company also extended its existing research partnership with Oak Ridge National Laboratory while beginning construction on a separate facility.

That new facility, called TX-L, will function as a dedicated research and development site going forward.

Beyond the Dow project, X-energy is pursuing additional Xe-100 deployments with Energy Northwest, Amazon, and Centrica as future customers.

Each new reactor project would add further demand for the specialized fuel TX-1 is being built to eventually produce.

Finishing the building's main structure does not mean fuel manufacturing can begin immediately at this location.

X-energy must still install specialized production equipment, commission interior systems, and prepare the facility for full commercial operations ahead.

No completion date has been disclosed for TX-1, but when completed, it would give the United States its first new domestic source of advanced reactor fuel in decades.

That outcome would also reduce reliance on foreign uranium suppliers while supporting new clean energy projects already planned with Amazon, Energy Northwest, and Centrica.

Via Interesting Engineering