• Thunderobot uses SSD storage to help its 64GB laptop run huge AI models
• The M7000 can reportedly run GPT-OSS-120B despite having 64GB of memory
• Phison's aiDAPTIV+ technology turns part of the SSD into an AI cache

Thunderobot has released the AI Master M7000, a 16-inch mobile workstation built around AMD's Ryzen AI Max+ 395 processor.

The M7000 comes with 64GB of LPDDR5X-8000 memory and Radeon 8060S integrated graphics for demanding workloads.

Its 768GB drive incorporates an 85GB caching layer designed to help AI workloads handle models exceeding available system memory.

AI workloads get an unusual storage assist

The M7000 uses LPDDR5X-8000 memory, while its processor provides 16 cores, 32 threads, boost speeds reaching 5.1GHz, and an XDNA 2 neural processor.

AMD rates the neural hardware at 50 TOPS, while the Radeon 8060S configuration reaches as many as 40 compute units for graphics and accelerated workloads.

Thunderobot says the device can run GPT-OSS-120B locally, despite having 64GB of physical memory available for applications and operating-system requirements.

Its unified memory architecture allows processor and graphics resources to access shared memory, supporting workloads that frequently move substantial model data.

The additional cache comes from Phison’s aiDAPTIV+ technology, which uses part of the internal storage to hold data associated with artificial intelligence workloads.

Rather than functioning as conventional RAM, this arrangement keeps selected information on the SSD when unified memory capacity becomes restrictive during demanding workloads.

Such information can include Key-Value cache, less-active mixture-of-experts components, and model data that applications may require again during processing.

Phison has also shown this storage approach with computers carrying considerably less memory, including a 32GB laptop running the same 120B model.

The technology addresses a limitation faced by large local models, where available memory can restrict the performance of certain workloads.

A 160W mobile workstation with unusually high specifications

Thunderobot gives the M7000 a 160W operating profile supported by two fans and three heat pipes for sustained processing.

The device also uses a 2560×1600 display running at 165Hz, with 500-nit brightness and complete sRGB coverage for color-sensitive work.

Power comes from a 99Wh battery, while the chassis weighs 2.45kg despite its workstation-oriented hardware and cooling requirements.

The display and cooling configuration add considerable hardware capability without changing the notebook’s emphasis on local computing.

For connectivity, this device includes OCuLink, USB-C supporting 40Gbps transfers, HDMI 2.1 FRL, 2.5Gb Ethernet, and a UHS-II microSD reader.

OCuLink provides another high-speed connection for compatible external hardware, giving users added expansion options beyond the laptop's built-in resources.

Thunderobot lists the M7000's Chinese retail price at 24,999 yuan, which converts to roughly $3,733 based on current exchange rates.

That figure places the device well beyond typical gaming budgets, despite the company's product lineup historically centering on gaming hardware.

This device instead reads as an artificial intelligence laptop, given how conspicuously its marketing and branding lean toward AI use cases.

Yet it remains surprising that the caching arrangement still falls short of matching genuine additional system memory in practice.

Via Videocardz

• Cache-to-Cache lets separate AI models exchange internal information without generating text
• A learned Fuser converts one model’s internal data for another
• C2C uses selective gating to control which layers receive information

Researchers from Tsinghua University have published a paper describing a technique that lets separate AI models exchange information without producing any text.

The method, called Cache-to-Cache (C2C), has already been accepted at ICLR 2026 and ships with open-source code available to developers.

It targets a specific inefficiency present whenever multiple language models work together inside a shared pipeline.

Skipping words entirely

When two AI models cooperate today, one has to turn its thinking into written sentences before the other can read them.

That writing step takes real computing time and throws away small details buried inside the first model's raw thinking process.

Every AI model keeps a working memory of everything it has processed so far, known technically as a cache.

C2C skips typed language entirely by letting one model pass that working memory straight into a second model's memory bank.

A small assistance program called a Fuser handles this handoff, reshaping and rotating the information so the second model can actually use it.

Different AI models store their memories using completely different internal layouts, sizes, and structures from one another.

Simply dumping one model's raw memory into another would likely confuse it or cause its answers to fall apart.

To prevent that, C2C includes a smart filter that decides which pieces of incoming memory are worth absorbing immediately.

Some internal layers accept the new information right away, while other layers keep reasoning independently without any outside interference.

According to the researchers, this setup makes AI models run between 100% and 150% faster during shared collaborative tasks.

That upper figure works out to roughly two and a half times quicker than the usual back-and-forth typing process.

The team also reports accuracy gains as high as 14.2% when models work together instead of operating entirely alone.

Compared against older setups where models still communicate through typed text, accuracy reportedly improved by 3.1% to 5.4%.

Why does this method have limits

This approach currently works only with open-weight models, since it requires direct access to a model's internal cache and layer structure.

Most popular AI tools, the kind ordinary people chat with online, hide those internal details completely from outside users.

That means everyday apps like certain chatbots cannot use this shortcut unless their own creators build it in privately.

Nobody outside these companies currently knows for certain whether anyone has started using a similar method internally.

The research team argues that typed language has always slowed machines down since it forces them to think like humans do.

That argument deserves some caution, since the same team that built the system also ran every test proving it works well.

Whether this speeds things up as much as claimed will depend on other external testing of the system.

• Durabook Z14I-DX3 rugged laptop has three 14-inch, sunlight-readable 1,200-nit touchscreens
• At 9.8 kg and a mammoth 137.5 mm thick, it may suit vehicles and fixed positions rather than dismounted crews in the field
• It could also be targeting customers at command posts and in drone teams

Durabook's latest rugged workstation laptop is less a notebook and more a capable folding command post, bringing power, durability, and plenty of screen real estate in tow.

The Z14I-DX3 pairs a conventional rugged clamshell with two extra full HD touch panels hinged to either side of the lid.

When folded, the side panels sit over the main display for transport; when opened, the three 14-inch screens form a mammoth 48:9 workspace for a client base that is expected to be overwhelmingly military.

Built around today's command post needs

While Durabook's press release says it is now available to order, it doesn't list a price on-site and instead directs users to request a quote for what it calls one of the most unique rugged laptops on the battlefield.

Durabook pitches the machine at defense, public safety, utilities, field engineering, and maritime users, describing it as a mobile command center where drone control and telemetry, mission maps, live video, and sensor data sit side by side instead of being juggled on a single display.

It also goes out of its way to make the screen component legible in all environments while focusing on utility: all three panels are 1,200-nit DynaVue sunlight-readable touchscreens with finger, water, glove, and stylus modes, and brightness can be synced across the set or adjusted per panel, as per Durabook's announcement.

This design choice makes the most sense when you consider who the workstation laptop serves: military customers. Each panel can run a separate application, so a drone team could keep a live feed on one screen, a map on another, and telemetry or communications on the third.

A single AC power adapter powers the whole setup, which can be configured with up to an Nvidia RTX ADA 5000 GPU that caps at 682 TOPS of AI performance, enabling offline image recognition and video analysis through on-device AI support.

Its I/O offerings also cater to the assumption that it might be plugging into military hardware, much of which uses legacy connections: Thunderbolt 4 and Wi-Fi 7 sit alongside two serial ports, VGA, an ExpressCard 54 slot, and a 41-pin pogo docking connector, plus dual gigabit Ethernet, HDMI, a smart card reader, and dual SIM with eSIM, with GPS, 4G, and 5G as options.

Durabook durability credentials include MIL-STD-810H testing for shock, vibration, altitude, freeze/thaw, high and low temperature, temperature shock, humidity, explosive atmosphere, and solar radiation, plus MIL-STD-461G for electromagnetic emissions and susceptibility, and an operating range of -29°C to 63°C.

Its weight class, however, rules it out as a portable device you can run around with, with a listed weight of 9.8kg (21.6 lb) and a thickness of 137.5 mm when closed, not including an adapter or carrying case.

This matters for drone teams that may already be carrying other hardware, including the drones they often deploy themselves; the Durabook offering suits fixed positions, shelters, or a vehicle better than a device that can be lugged around with relative ease.

For now, a 'workstation' class laptop that offers plenty of durability, a last-generation AI GPU, and a low-powered CPU might check a lot of battlefield boxes, but would be hard to compare in terms of raw performance to modern gaming laptops or even traditional workstations, both of which pack in significant amounts of power even if they skip the two additional screens - but that might just be the point.

• Tektronix's DPO718AX brings eight synchronized channels of up to 25GHz each to the 7 Series and can double as a Windows 10 PC
• The EPYC 3351, T1000, 96GB RAM, 1.6TB SSD configuration the oscilloscope offers makes it more than capable of running most modern OSes
• Prices start at an eye-watering $308,000 for the base configuration, which can be further modified as per end-user needs

Tektronix has added an eight-channel model to its flagship 7 Series oscilloscope line, and much of the attention it has drawn has gone to the computer it houses as part of the configuration.

The DPO718AX hosts a 12-core AMD EPYC processor, an Nvidia T1000 graphics card, 96GB of RAM, a removable NVMe SSD with at least 1.6TB of capacity, a 15.6-inch 1080p touchscreen, and an optional Windows 10 drive, all in a chassis that Tektronix's datasheet puts at 38.1kg.

This has drawn sharp comparisons to actual Windows-based PCs amid a RAM and memory shortage, including one by CNX Software, which said that the instrument "looks like a high-end PC running Linux (or Windows 10 LTSC)."

A powerful PC in its own right, albeit with dated components

None of the computer hardware listed for the DPO718AX is new or recently released. The AMD EPYC Embedded 3351 CPU is one of the oldest hardware offerings in play here, featuring AMD's first-generation Zen cores and a February 2018 release date.

While the SSD storage speed is unspecified, it is safe to assume it will be capped by the PCIe 3.0 support the EPYC CPU provides to the system. The 96GB of memory on offer can also safely be assumed to be DDR4, as the CPU supports only DDR4 configurations. Despite these limitations, the CPU does offer a 12-core, 24-thread configuration with a rated boost clock of 3GHz that should handle most tasks with relative ease.

The Nvidia T1000 GPU is an entry-level, workstation-class GPU built around its Turing architecture, with a release date of May 2021. The oscilloscope comes with an FHD touch-screen display and can output video up to QHD via DisplayPort, with a capped refresh rate of 60Hz.

The oscilloscope can also run Windows 10 natively, and it matters more than you might think: it is required for the compliance and RF analysis software the DPO718AX supports. The DPOs do not come with an installation by default; the Windows 10 LTSC 2021 installation is available on a secondary, removable drive that users can opt for if needed.

Since the integrated AMD CPU supports TPM 2.0, you could theoretically run Windows 11 on a device that costs hundreds of thousands of dollars, but the aging hardware, low-clocked CPU, and dated internals would make it a terrible PC replacement, even if one had the money to splurge on such an endeavor.

• FEVM FA80G mini PC packs a desktop Ryzen 9 9950X3D inside a 2.6-liter chassis
• The FA80G pairs AMD’s gaming-focused processor with Nvidia’s 24GB mobile flagship
• Nvidia gives the RTX 5090 Laptop GPU 150W inside this compact system

FEVM has unveiled a new compact desktop system pairing the Ryzen 9 9950X3D processor with a 24GB RTX 5090 Laptop GPU.

The pairing sits inside FEVM's FA80G chassis, a compact system built around AMD's AM5 desktop platform rather than mobile silicon components.

The flagship processor contains 16 cores, reaches 5.7GHz and carries 144MB of cache through AMD’s 3D V Cache technology.

Ryzen 9 9950X3D and RTX 5090 pack serious power

FEVM allows the processor to receive up to 90W, while the accompanying RTX 5090 Laptop GPU can receive up to 150W.

Nvidia’s mobile graphics processor has 10,496 CUDA cores, 24GB of GDDR7 memory and 896GB/s memory bandwidth.

It also supports hardware ray tracing, DLSS 4 and Multi Frame Generation, giving the FA80G several features associated with current high-end PC gaming.

The desktop processor is equally important, as the 9950X3D is designed for high-performance gaming workloads rather than conventional mobile computing.

This mini PC also supports up to 128GB of DDR5 memory via two slots, while two M.2 2280 connectors support drives reaching 4TB.

FEVM separates processor and graphics cooling, assigning four heat pipes to each component across the device’s internal thermal arrangement.

The company rates full load noise at 42dB, with CPU temperatures below 85°C and GPU temperatures below 80°C.

For connectivity, this device includes HDMI 2.0, HDMI 2.1, two DisplayPort 2.0 outputs, USB-C, USB-A, an SD reader and dual 2.5GbE.

FEVM has the hardware for a serious M5 Ultra gaming comparison

In comparison to Apple’s top M5 Ultra Mac Studio, the FA80G takes a more conventional high-end PC approach to gaming hardware.

The M5 Ultra offers up to 36 CPU cores, 80 GPU cores, 512GB unified memory and 1.2TB/s memory bandwidth.

Apple also includes hardware-accelerated ray tracing, Neural Accelerators and a 32-core Neural Engine within its highest M5 Ultra configuration.

Those specs give the Mac Studio substantial graphics and compute resources for professional rendering, video production and artificial intelligence workloads.

For gaming, however, the FA80G CPU and GPU combo fits the Windows PC gaming environment more directly.

It also supports DLSS 4, ray reconstruction, frame generation and Multi Frame Generation across its RTX 50 series gaming hardware.

Apple supports advanced gaming technologies through Metal, MetalFX and its Game Porting Toolkit, including tools for bringing Windows games onto Mac.

That means the M5 Ultra is capable of modern gaming, but developers must still account for Apple’s Metal-based gaming environment.

The FEVM instead combines established Windows gaming hardware with Nvidia’s dedicated graphics stack and AMD’s gaming-focused desktop processor.

For a device specifically judged by gaming hardware, that makes the FA80G’s specification unusually aggressive for a 2.6-liter system.

The M5 Ultra remains a much larger compute platform in memory capacity and integrated resources, but the FEVM is built differently for gaming

The flagship configuration of FEVM's FA80G costs ¥22,299 (about $3,300), before adding memory and storage to the bare-bone system.

Via Videocardz

• A chatbot invented entirely false cargo details about a Chinese vessel
• Armed troops prepared to board the ship before anyone checked the source
• Military aircraft were already active when officials caught the error

A false intelligence assessment nearly triggered a US military invasion of a Chinese vessel after AI-assisted analysis wrongly identified its cargo.

The report circulated during the Iran conflict and prompted preparations to intercept the vessel before officials discovered serious problems with its underlying information.

Sources told CNN that armed personnel were preparing to board while military aircraft supported preparations, creating a potentially dangerous confrontation between major powers.

AI-assisted analysis produced the mistaken assessment

The episode began when an analyst examined intelligence concerning the ship's manifest and used a chatbot to interpret information.

Sources said the system combined publicly available material with classified signals information before producing an incorrect assessment about the vessel's cargo.

The analyst subsequently used AI again to prepare the findings in a standard intelligence format that was distributed among military officials.

Officials only examined the underlying information shortly before the planned operation, revealing that the reported cargo identification was wrong.

The source described the assessment as “entirely false,” while CNN reported that another source said it “almost started a war.”

The exact material that the chatbot misidentified has not been disclosed publicly, leaving important details about the original error unavailable.

The Pentagon and the military command responsible for Pacific special operations did not provide comments on the incident.

The episode comes as the US military expands its use of AI tools across intelligence, operations, administration, and battlefield decision-making.

In January, Defense Secretary Pete Hegseth announced an AI strategy intended to accelerate deployment across military missions and organizational functions.

“We will unleash experimentation, eliminate bureaucratic barriers, focus our investments and demonstrate the execution approach needed to ensure we lead in military AI,” Hegseth said.

The strategy includes programs for intelligence work, operational planning, enterprise functions, and wider access to generative AI systems.

The department later said more than 1.3 million personnel had used GenAI.mil, generating tens of millions of prompts within five months.

The incident exposes a difficult verification problem

The reported mistake involved more than an inaccurate chatbot response because its output entered a process used for consequential military decisions.

Once incorrect information appeared in a formal assessment, officials receiving the document had to determine whether its underlying evidence supported the conclusion.

There is no information on which verification procedures were applied before the assessment reached personnel preparing the maritime operation.

It also remains unclear whether the analyst knew that the chatbot had introduced an incorrect interpretation before the report circulated.

According to CNN, different military organizations use different AI systems, while officials have described varying practices for checking information produced by them.

That fragmented environment makes it difficult to identify a single verification process governing every AI-assisted intelligence product across the military.

AI tools can make military analysis faster and easier, yet their potential for significant errors requires strict human verification before operational decisions.

That need becomes more important when intelligence concerns a rival country, where an incorrect assessment involving a military vessel could trigger retaliation.

A mistaken attack could then escalate tensions between the countries involved, potentially producing a wider armed confrontation or prolonged political and military conflict.

The Bambu Lab H2D sits right at the top of our 3D printer guide - a versatile machine that performed exceptionally well in our tests. Now, the flagship machine is on sale.

• Best for: Experienced makers and small businesses who want a large dual-nozzle machine for engineering-grade filaments.

• The deal: The Bambu Lab H2D AMS Combo is now $1749 (was $1999) at Bambu Lab, while in the UK, the unit is £1449 (was £1649). The standalone and Full Laser Combo units are also on sale.

• Why we like it: Calling this "one of the most impressive 3D printers on the market," we scored this 3D printer 4.5 stars in our review. It's effectively a compact multifunctional manufacturing hub. Precise and fast, it excels at multi-material printing.

Today's best 3D printer deal

Why we recommend it

In his 4.5-star review of the H2D, our expert Alastair found that "3D print quality is as good as it gets." He added that, "when it comes to print quality, there's no doubt that the H2D matches the excellent X1 Carbon and holds its own against the current crop of high-end 3D printers."

The Full Laser Combo is also in the sale, and for that, he found the "laser engraving is excellent, and the cutting/plotting is right in line with other dedicated machines."

You'll find the Bambu Lab H2D at the top of our roundup of the best 3D printers we've tested.

Price context & historical value

The $250 reduction takes 12.5% off the H2D AMS Combo's $1,999 regular price, bringing it down to $1,749. That's a substantial cash saving on an expensive printer, but it's not the cheapest we've seen it. In Bambu Lab's 4th Anniversary Sale in June/July 2026, the H2D AMS Combo was available for $1,699.

Should you buy it?

✅ Buy the Bambu Lab H2D AMS Combo if...

You want a versatile 3D printer that can accommodate ambitious projects and different filament types, including PLA, PETG, TPU, ABS, ASA, PC and PA, plus a selection of carbon- and glass-fiber-reinforced types.

❌ Skip the Bambu Lab H2D AMS Combo if...

You only intend to make occasional small PLA prints. The H2D's size, capabilities, and price are difficult to swallow if you aren't likely to take advantage of its more advanced features.

Also consider

• Shop all 3D printers at Bambu Store

CyberLink has long been one of our favorite alternatives to Adobe's creative suite, and currently there's a big sale on standalone alone apps as well as CyberLink Director Suite, which bundles the photo, video, and audio editor software alongside an audio editor and large royalty-free stock library.

• Best for: Content creators who regularly edit video, photographs and audio and want their main creative applications bundled into one subscription.

• The deal: CyberLink Director Suite is now $110 (was $150) for an annual subscription, and £95 (was £130) in the UK. Standalone apps are also discounted in the US here (click here) and in the UK here.

• Why we like it: CyberLink's creative tools have consistently reviewed well during our tests, offering an excellent all-rounder entry-point into video and photo editing. They're surprisingly advanced but beginner-friendly, and this is a good way to get the full suite.

Why we recommend it

Director Suite combines three powerful creative tools. PowerDirector 365 handles video production, PhotoDirector 365 covers photo editing and graphic design, and AudioDirector 365 provides tools for audio restoration, recording and mixing.

All three core apps have reviewed well in our tests.

In his 4-star review of PowerDirector, our expert Steve said it "remains one of the best video editing software for beginners, as well as intermediate editors." He gave PhotoDirector 4.5-stars and called it a "powerful multi-faceted beast, which not only allows you to organize and improve the quality of your photos, but also offers Photoshop-like compositing tools, as well as numerous generative AI features."

In her 4-star review, our audio expert Angel said AudioDirector "goes from strength to strength using powerful AI tools to separate elements giving even total novices to recording control over their material."

The applications are designed to work together, so projects can be passed between them without repeatedly rendering files. There's plenty of AI features too.

Price context & historical value

At $109.99 a year instead of $149.99, you're saving $40 on Director Suite, equivalent to 27% off. The annual subscription works out at just $9.17 per month, compared with $29.99 if you pay monthly, which would add up to $359.88 over a year — $249.89 more.

UK buyers can save 27%, with the price of Director Suite reduced from £129.99 to £94.99, equivalent to just £7.92 per month.

For more choices, check out our roundups of the best photo editors, and best video editing software.

Should you buy it?

✅ Buy CyberLink Director Suite if...

You regularly work with video, photographs and audio and would prefer to have an integrated collection of creative applications than subscribe to different products. Director Suite also provides access to millions of royalty-free videos, images and music tracks from iStock by Getty Images and the Meta Sound Collection.

❌ Skip CyberLink Director Suite if...

You only need one type of editor. If almost everything you create is video, for example, paying for a suite containing dedicated photo and audio tools may not be necessary.

• US: Shop discounted creative software at CyberLink
• UK: See the full sale at CyberLink

• The environment, energy costs, quality of life and taxes are all concerns
• Americans are largely split about whether data centers are even good news for jobs
• Negative sentiments are rising across the board regardless of political background

With the US midterm elections around the corner and AI data centers increasingly becoming part of political discourse, new Pew Research Center reporting has revealed Americans' attitudes toward these facilities have become even more negative as 2026 continues.

Surveying more than 10,500 US adults, the study found only 4% thought data centers are a positive thing for the environment, home energy costs and local residents' quality of life.

On the flip side, more than half (54%) thought they were 'mostly bad' for the environment, up from 39% in January 2026, around half a year prior, showing a marked increase just in a short space of time.

US citizens are increasingly turning against AI data centers

In fact, Americans believe US data centers are worse for home energy costs (50%), quality of life (49%) and local tax revenue (21%) than before.

And with hyperscalers all promising to add hundreds or, sometimes thousands, of operational jobs on top of thousands of temporary construction jobs locally, residents are even starting to disbelieve these promises. Interestingly, the proportion of nationals perceiving data centers as mostly bad for local jobs rose nine percentage points, from 15% at the beginning of 2026 to 24% halfway through the year.

Perceptions are now mostly split, with 24% seeing projects as mostly bad for jobs and 22% seeing them as mostly good.

This still leaves a considerable proportion of Americans – 18% to 32% depending on issue – uncertain about the impact of data centers.

Opinions are shifting

Today, three in five US citizens say they'd either be not too comfortable, or not comfortable at all, with a new data center operating in their area. Rural inhabitants are more likely than before to have negative perceptions, likely with more projects being approved for extra-urban locations where space is at less of a premium.

The study also delves into the political implications, confirming that Americans are increasingly unsettled by data centers right across the board, be it by age, gender, education levels or political party. While Democrats are more likely overall to have negative feelings, supporters of both major parties have actually become more unhappy throughout 2026.

Arguably one of the biggest findings of this report isn't necessarily where the split lies and whether Americans are worried about environmental, social or economic impacts – it's the sheer pace that negative sentiments have grown in little more than just half a year.

All of this is, of course, playing out during an important time for the US politically.

While President Trump continues to advocate for AI and data center buildouts in an effort to be a global leader, more and more states and regions are imposing temporary bans to reassess legislation and local impacts, including Texas itself – the home of OpenAI's massive Stargate project.

• One simple prompt could lead to swathes of downstream compute, experts warn
• Attackers could even exploit your uncontrolled AI to run up costs
• Greater visibility and circuit breakers are two solutions

Data security company Forcepoint has revealed a major issue with AI agents, but unlike many AI security threats, it doesn't involve stealing data or compromising the model.

Instead, if left to its own devices, Forcepoint says agentic AI could actually consume excessive amounts of compute, tokens, API calls or other resources if sufficient safeguards and limits aren't in place, leading to higher-than-anticipated enterprise cloud bills.

Moreover, the company's research argues that the problem has become more important as AI has become more complex.

Enterprises warned to keep an eye on AI agent compute usage

The result of overwhelmingly complex agentic AI systems is that one single and apparently simply user request could lead to tens or hundreds of downstream operations. Forcepoint labels this as 'unbound consumption'.

Crucially, the analysis found that high compute and token consumption could actually be pretty hard to detect and existing security protocols are unlikely to pick it up, because there doesn't even need to be an attacker for the impacts to take place. All you need is a badly configured automation or a long-running AI session to accidentally lead to runaway costs.

However, Forcepoint worries that attackers can indeed step in to exploit this vulnerability, with malicious users generating huge workloads and consuming massive compute for their own benefit after obtaining an enterprise's credentials, letting them pick up the bill.

Solutions can be as complex as agentic AI itself, but they're now more necessary than ever. Firstly, companies should set budgets at multiple, finer levels, such as API keys, individual users and teams. They should also have greater monitoring powers over where costs are attributed to.

But Forcepoint also calls for agentic circuit breakers to prevent workload and costs from compounding.

"Security teams rarely watch cloud billing dashboards. Finance rarely reviews prompt patterns or agent design," security researcher Jyotika Singh wrote in an urge for enterprises to take the risk more seriously.

• Adobe Premiere is now free on Android, following its earlier roll-out to iPhone
• Premiere offers everything you can get on Canva, and more, without the subscription fees
• Compatible with Android 13 and later and requiring 5GB of RAM, it is unlikely to run on most Chromebooks, and is without advanced desktop features

Adobe has launched its flagship Adobe Premiere software as a mobile app on Android, and has made it free to use. After its previous release on iPhone in 2025, the stripped down take on Premiere has proved to be a smart move from Adobe, attracting users away from upstart competitor Canva.

Like Canva, Adobe Premiere for mobile has AI features. While free to download and use the app, you’ll still need to pay for the AI credits to use those features, but this will likely prove more affordable than the monthly plan of the desktop version.

A standard Adobe Premiere Pro subscription on desktop will typically cost £21.98/$22.99 a month with an annual plan, which makes the new Adobe Premiere for Android an attractive option if you don’t need the full set of features. There’s a bonus, too: if you use a modern Chromebook with Google Play installed, you should be able to use this version of Adobe Premiere.

Is it really Adobe Premiere?

While Adobe Premiere Rush is already available on Android, this is a pretty basic take on the video editing tools now in demand by content creators. This new free Adobe Premiere – which doesn’t have the same features as the desktop original – is a more accomplished version than Adobe Premiere Rush, with multi-track timeline, audio enhancements, 4K video, and more.

Adobe is generally better value than Canva, and the company’s decision to release feature-packed mobile versions of Adobe Premiere on iPhone and now Android underlines how seriously it takes the competition. While Adobe has a massive market share in the creative software market, Canva’s small 4% is backed by a more active user base.

Both tools offer creative AI, but Adobe has a legacy of providing audio and video editing software. The free Adobe Premiere app offers access to the Adobe Stock and support for creator assets, too – and unlike Canva, doesn’t demand a login to start using it.

Free Adobe tools on Android

To install Adobe Premiere on Android, you’ll need Android 13 or later, with 5GB of RAM. The first release has a download of just under 60MB, and Firefly-powered generative AI features will require an active internet connection. Modern Chromebooks with over 4GB of RAM should be able to run the software, although there is every chance that Adobe removes that option.

Other Adobe tools are also available on Android. Aside from Adobe Premiere Rush (which would seem to be heading to retirement), Adobe Lightroom Mobile, Adobe Photoshop Express, and Adobe Express are among the most popular, offering mobile takes on the desktop Photoshop experience. These also provide an alternative to Canva’s monthly subscription, and again are free to use but have some paid features.

• WordPress Core flaw CVE‑2026‑87902 (path traversal, 8.1 severity) enables PHP file inclusion and possible RCE
• Patch released in v7.1.2 and backported to 4.7+; exploitation began within hours, now widespread
• Admins must urgently update; interim mitigations include blocking traversal sequences and disabling risky ARP/PHP settings

Hackers are actively exploiting a high severity vulnerability in WordPress that can lead to full website takeover, researchers are saying. A patch is available, and WordPress users are urged to upgrade immediately or risk losing access to their assets.

Discovered by security researcher Robert Ressl, the vulnerability in question is tracked as CVE-2026-87902. It is an 8.1/10 (high severity) unauthenticated path traversal flaw affecting WordPress Core. According to WordPress itself, as well as the National Vulnerability Database, the bug can lead to local PHP file inclusion and, in certain scenarios, remote code execution (RCE).

"An unauthenticated attacker can make get_page_template() page-template resolution include a chosen readable local .php file outside the active theme directories," it was said in the official security advisory.

Achieving RCE

WordPress is the world’s number one website hosting and builder platform, powering more than half of all websites active on the internet right now. However, that doesn’t mean all of them are susceptible to RCE. Only websites ticking these boxes are at risk:

Sites with parent or child themes that have a top-level directory with a name starting with ‘page-’ (for example, ‘page-templates).

Threat actors must target a local .PHP file that exists and is readable by the web server

The web server account must be able to read the included file (for example, pearcmd.php, if PHP’s register_argc_argv setting is active)

WordPress said that both the official PHP image for Docker, and the default cPanel configuration, are affected (users must be running a PHP version before 8.5, though).

The issue was fixed in version 7.1.2, which is now available for download. Fixes were also backported to older versions up to 4.7. Releases before 4.8 are not supported, it was said, and will not be getting a fix.

Attacking vulnerable websites

Wordpress security company Patchstack said the first exploitation attempts started roughly five hours after the patch was released, and these were primarily reconnaissance efforts. In the hours to follow, malicious activity increased tenfold, it was said, as crooks started attempting to deliver malicious payloads to vulnerable websites, as well.

“When this post first went up, every request we had seen was reconnaissance against harmless core files,” Patchstack said. “That is no longer true. Attackers are now including pearcmd.php and using it to write PHP files to disk, and public scanning tooling for this CVE is in circulation.”

At first, Patchstack said the attacks were coming from a handful of IP addresses, and advised website admins to simply block them. However, the attacks have now become rather widespread, meaning blocking individual addresses is no longer a viable strategy. They urge everyone to apply the patch without delay:

“The first evening came from a small cluster of addresses. It is now spread across a few hundred, so blocklisting individual sources is not a strategy. The heaviest talkers at the time of writing:

43.250.53.42

180.251.159.243

195.178.110.247

107.189.14.87

45.61.184.170

92.246.130.76

The file write attempts specifically come from a much smaller subset of those addresses, which is the usual pattern of a few operators acting on results that a much larger scanning population produced.”

Those that cannot update immediately should reject traversal sequences in the pagename parameter, Patchstack added. A real page slug never contains one, they added, meaning it can be blocked without affecting normal traffic. Furthermore, disabling register_argc_argv does not fix the inclusion but it does break the pearcmd chain, which is the difference between an information leak and code execution.

Via BleepingComputer

As a small business, your developer budget is small but your to-do list isn't. Yet every new tool seems to arrive with another expensive subscription to keep track of. NodeJS is popular among SMBs because it helps teams build more with less.

Let’s say you run an online store that needs a faster checkout. Or you're an operations lead drowning in spreadsheets. Or perhaps you're a founder deciding what your first developer hire should know. NodeJS can save you real hours and money, but there are some tasks where it’s better suited than others.

What makes NodeJS so useful?

Node.js is an open-source runtime environment that lets you run JavaScript outside a web browser. Developers use it to build servers, web apps, command-line tools, and scripts.

The project’s official documentation describes it as an “asynchronous event-driven JavaScript runtime.” Plainly speaking, Node.js doesn't sit idle while waiting on a database, payment gateway, or file to respond. It moves on to the next request and returns to the earlier tasks when the answer arrives. According to the Node.js docs, almost no function in Node directly pauses execution for a response, so the process never stalls.

That said, the bigger win for a small business is the language itself. JavaScript is used by 66% of respondents to the 2025 Stack Overflow Developer Survey, while Node.js is used by 48.7%. That gives you a large hiring pool plus one language that covers both the storefront your customers see and the back end that keeps your business running.

It's also very easy to maintain for smaller teams. Node's long-term support (LTS) releases typically receive critical bug fixes for 30 months. The project advises running only LTS versions in production, which takes most of the guesswork out of planning upgrades.

7 ways small businesses can use NodeJS

Most small businesses don't need a sprawling tech stack. They need a handful of reliable systems that talk to each other, stay online, and don't demand a full-time engineer to babysit them. Node.js fits that brief because it's strongest at the everyday work of moving data between people, apps, and services:

1. Run your whole web stack on one language

When your website's front end and back end share a language, one developer can work on both. You avoid paying for separate specialists or waiting on handoffs between two teams. It also means code for things like form validation can be written once and reused on both sides.

The classic example is PayPal's move to Node.js. Its engineers reported building an app almost twice as fast with fewer people, using 33% fewer lines of code than the Java equivalent. Your business isn't PayPal, although the lesson scales down nicely: fewer moving parts mean fewer billable hours.

2. Automate repetitive admin work with scripts

Think about the tasks your team repeats every week. Renaming files, compiling sales reports, sending invoice reminders, and cleaning up exported CSVs are all jobs a short Node script can handle. None of them needs a full application, only a file of code and a scheduler.

Because Node.js is built for command-line tools and scripts, you can schedule these to run overnight while nobody is at a desk. A few hours of developer time up front can claw back several hours of staff time every month. Fewer manual steps also mean fewer copy-paste errors creeping into your invoices.

3. Glue your business apps together with APIs

Your payment processor, CRM, email platform, and accounting software probably all offer APIs. In most cases, those apps already hold the data you need, but they rarely share it with each other. Node.js treats HTTP as a first-class citizen, with a global fetch()function that has been stable since Node 21.

That makes it a natural fit for tasks like syncing a new order to your stock list or pushing paid invoices into your books. Instead of paying for a pricey integration subscription, Node services can do the same job for pennies in hosting. You also get to own the logic this way, so you're not stuck when a third-party connector changes its pricing tiers.

4. Add real-time features your customers will notice

Live chat, order tracking, booking availability, and instant notifications all rely on keeping connections open, which ties up server resources and gets more expensive as visitors pile up. That's exactly the kind of work Node's event-driven design handles well.

Node now ships with a browser-compatible WebSocket client, stable since version 22.4. You can build live updates into your customer portal without bolting on extra infrastructure. Customers who can see their order status for themselves also tend to send fewer “where's my parcel?” emails, which means less pressure on your support team.

5. Plug AI services into your workflows

You don't need to train your own AI model to benefit from one. Most small businesses simply call a hosted AI service to summarise support tickets, draft product descriptions, or sort incoming emails.

Those calls are mostly waiting time while a remote server does the thinking. Node.js is very good at waiting efficiently, so it can manage many AI requests at once while the heavy computation happens elsewhere. If you later switch AI providers, you usually only need to change the few lines that point to the new API.

Just be careful with what you send. Customer data passed to a third-party AI service is still your responsibility, so check each provider's data retention terms before connecting it to your inbox.

6. Keep your hosting bills in check

Because Node handles lots of concurrent connections with little overhead, a small app can often run on a modest server. PayPal's team reported doubling requests per second on a single core, compared with five cores for its Java app.

Node is also widely supported by serverless platforms, where you pay only when your code runs. For a booking form or a contact handler that sees sporadic traffic, that can mean a monthly bill measured in cents rather than dollars. Keep an eye on usage-based pricing, though, since a sudden traffic spike can push those cents up quickly.

7. Trim your toolchain with built-in features

Recent versions of Node.js absorb jobs that used to need extra packages. The built-in test runner has been stable since Node 20. Node can also load secrets from a .env file with the --env-file flag, which is no longer experimental as of versions 24.10 and 22.21.

TypeScript users benefit too. Type stripping is on by default and marked stable from Node 25.2 and 24.12, so simple TypeScript files run without a separate build step. Every dependency you skip is one less thing to update, audit, or pay someone to fix.

NodeJS limitations you should know

The official Node.js guide is clear that Node stays fast only while each task is small, because a thread busy with a long job "cannot handle requests from any other clients." That makes CPU-heavy work like AI model training, deep learning, or large-scale video processing a poor fit. Worker threads help with some CPU-intensive jobs, but even Node's TensorFlow bindings hand the heavy maths to a native C library, which is why most teams train models in Python on dedicated GPUs.

Dependency bloat is the next headache. A single npm package can pull in dozens of others, each with its own maintainers and update schedule. Over time, your project accumulates code you never use but still have to patch, quietly eating into the time NodeJS was supposed to save.

Security is where that bloat bites hardest. In September 2025, attackers phished a maintainer and pushed malicious versions of 20 popular packages with over 2 billion combined weekly downloads. Days later, a self-replicating worm named Shai-Hulud (fittingly, after the sandworms of Dune) compromised over 500 packages, with a second wave in November hitting roughly 800 more.

I wouldn’t say that any of these are reasons to avoid Node.js altogether, but you have to be disciplined with it. Keep dependencies lean, run npm audit regularly, and use npm's min-release-age and ignore-scripts settings to skip brand-new package versions and block install scripts. Treat every new package like a new hire — to be vetted thoroughly before granting access.

• Gambit researchers uncovered ongoing AI‑driven skimming campaign stealing 600,000+ payment records since July 2026
• Attackers used three autonomous harnesses (Strix, Cairn, Hermes) to compromise dozens of retail sites cheaply
• Victims include major US firms; campaign shows AI enables faster, persistent, low‑cost cyberattacks at scale

In July 2026, a hacker tasked autonomous AI agents to attack retail organizations around the world, deploy credit card skimmers, and steal payment data.

Since then, the bots launched hundreds of attack projects, compromised dozens of organizations, and stole at least 600,000 payment records - and to make matters worse, the campaign is still live, attacking and breaking into websites as we speak.

All of this was reported by security researchers Gambit, who said they managed to recover the operator’s staging server and through it - reconstruct the ongoing campaign. They also saw the skimmers live on victim websites, and sifted through logs and AI claims found on the attacker’s server. In just five days, between September 10 and 15, the agents made 105 attack waves and compromised 27 organizations “to varying degrees.”

Among the victims are a Fortune 500 hospitality company, a “major” US airline, a large private US industrial supplies distributor, and a US online fashion retailer. One of the AI tools would use a website ranking service to produce a list of potential targets, focusing primarily on those running custom-built software.

A fistful of dollars

But the victims are not the “interesting” part of this story - the attackers are. Gambit believes they are financially motivated Chinese threat actors. They are using three AI “harnesses” (frameworks, essentially), which can run almost the entire attack chain autonomously, striking around 10 companies a day, for a handful of dollars per company.

In four weeks, the attackers spent around $7,000, meaning that their entire cost for the operation so far was no more than $18,000. Breaking it down, it means that the attacker spent around $25 per target.

“Spread over the companies attacked, this is a marginal cost of a few US dollars to a few tens of US dollars for each targeted company,” Gambit’s researchers said. “The operator’s own cost review gives a similar figure, a mean of $25.46 over 101 completed scans, from $3.13 for the cheapest target to $79.31 for the most expensive.”

“Where access was achieved, it usually took less than a day, and in many cases just a few hours. We also detected instructions in the attacker’s playbook that could disrupt the operations of a company as a result of data deletion or cleanup procedures run by the agent - and this has indeed happened in some of the breaches,” Gambit said.

The three harnesses

The three harnesses are called Strix, Cairn, and Hermes.

Gambit describes Hermes as an open source autonomous AI agent with a persistent memory, skills that the agent wrote and edited itself, a searchable archive of past sessions, scheduled jobs, and a web console. On the staging server the researchers analyzed, it loaded a Chinese system persona called “SOUL - Red Team Operator”, which contained 121 skills (78 attack skills).

“Hermes is the operator’s console for orchestrating the activity and for direct hacking activities,” Gambit explained. “It used Anthropic’s opus-4.6 (after newer models refused its requests), with 1,951 prompts typed by the human across 260 sessions - only a few prompts per target. The human prompts are short instructions in Chinese, usually launching an attack, tasking the agent with a general next step, or what to do next after achieving access.”

Strix is an open-source AI pentest tool, while Cairn is an autonomous pentest engine. It receives target domains and an objective, such as to get a shell or admin access, then runs for hours until it achieves the objective, times out, or is stopped. Cairn used DeepSeek v4.1 Flash, it was said.

Gambit’s researchers seem to be rather impressed with the campaign. They described it as very low cost, with a level of patience, persistence, and creativity that most human attackers would be “unlikely to sustain”, managing to achieve “far greater results, far faster.”

They have also called to arms, urging organizations to “adapt to a reality where attacks are significantly faster and more comprehensive.” To do that, they must adopt a resilience-first mentality and deploy a security stack that can match the AI on speed.

Many of the affected organizations were notified, and the skimmers were removed, they said.

• Amazon is re-hiring former workers back into AI and cloud roles
• The company is investigating the impacts of its office-working mandates
• Despite 30,000 layoffs in the past year, Amazon continues to hire for certain roles

Amazon is reportedly trying to re-hire old workers, including many who were affected by previous rounds of layoffs.

Recruiter emails cited by Business Insider reveal that rather than filling roles that have emerged following layoffs, the company is actually targeting specific ex-workers it seems, with the company's cloud computing business predominantly seeing these hiring efforts.

AWS, cloud computing, AI, ML and agentic AI are apparently the focus of these efforts, with one recruiter reportedly referring to the scheme as 'Swami's Boomerang Reengagement Initiative' – Swami Sivasubramanian is a VP for AWS Agentic AI.

Amazon is re-hiring ex-workers back into the company

The company is also said to be making it easier for former employees to return, offering shortened interview processes that may skip over some of the screening steps that aren't all that necessary.

Besides trying to attract former talent, Business Insider also reports that Amazon is digging deeper into why they left, and the potential impacts of its return-to-office (RTO) mandates. Specifically, the tech giant wants to know whether the RTO changes were behind some ex-workers' departures.

While company spokesperson Haley Silva told the publication that Amazon isn't specifically targeting people who left because of the RTO and that its in-office expectations haven't changed, it's an interesting perspective from a company that went pretty much all-in on the RTO years ago in a bid to drive more in-person collaboration and to boost productivity.

All of this comes in the wake of major ongoing layoffs – the company fired around 30,000 workers just in the past year. But it's apparently all part of a broader shift in focus, because instead of cutting headcount as aggressively as that 30k figure suggests, Amazon's more likely looking to lose workers in certain areas of the business and gain workers in more important areas.

For decades, banking technology has been built around a simple sequence: a person makes a financial decision and the bank’s technology processes it. A customer decides to open an account, move money into savings or apply for a loan, and the bank’s systems execute that instruction.

Artificial intelligence can potentially reverse that sequence. Instead of waiting for a human to specify an action, AI can interpret the person’s goals, understand the financial context around that goal, and determine what happens next. It could recognize that a customer is likely to face a cash shortfall, identify the available ways to address it, and potentially execute the appropriate action.

McKinsey estimates that generative AI could create $200 billion to $340 billion in annual value for the banking industry. Yet much of the industry is adding AI to systems designed for the old model, not rebuilding the model around AI.

The problem is that AI inherits the same product silos and process boundaries. Banks gain another layer of technology, but not the cross-system decision-making needed to realize AI’s full potential.

The first wave is still about better processes

The most visible applications of AI in banking are often the easiest ones to deploy. Banks are using AI tools to improve customer service, automate fraud detection, personalize recommendations, summarize documents and accelerate credit decisions. Lloyds Banking Group, for example, says more than 50 AI use cases were rolled out across the group in 2025, generating around £50 million in value, with more than £100 million in additional value expected in 2026.

McKinsey has made a similar observation based on the industry's experience with generative AI. Simply adding AI on top of existing processes will not produce transformational change and can instead create another layer of technical debt.

The difference between an AI-native architecture and a chatbot attached to an existing system can be tested with three questions.

• Is the AI following a fixed sequence of instructions, or can it choose and coordinate actions within a defined system of guardrails, trusted data sources and approved tools?
• Is the process designed for the agent to act, with human review and every decision recorded for analysis?
• And are permissions, monitoring and regulatory controls built into the workflow, particularly for critical functions such as compliance and fraud prevention?

If the answer is no, the company has added an AI interface without redesigning the underlying process.

From products to outcomes

The more significant transformation begins when the bank starts with the customer’s objective, not with the banking product.

Consider a customer who wants to maintain a certain level of liquidity while earning as much as possible on excess cash. An intelligent banking system could continuously monitor their balance, upcoming payments, income, available credit and other relevant information, then determine whether money should remain liquid, be invested elsewhere or be used to reduce borrowing.

While the system can continue making decisions as the customer’s circumstances change, that does not require customers to surrender control immediately. Adoption can begin with low-risk actions, such as moving excess cash into savings or setting aside VAT for future tax payments, before expanding into more consequential decisions.

This is already beginning to appear in financial institutions, although mostly in bounded applications. Deutsche Bank, for example, has deployed an agentic AI system for third-party risk management in which several AI agents retrieve relevant controls, analyze supporting documentation, and propose assessment outcomes. Human assessors remain responsible for reviewing or overriding those recommendations.

Like a new employee, an AI agent should receive defined permissions. Transparent activity logs, alerts, approval thresholds and the ability to override decisions would make that principle visible in the product and enforceable by regulators.

The bank becomes a continuous decision system

Under this premise, the bank becomes an intelligent execution layer that continuously manages financial activity to accomplish a defined objective.

This shifting structure is also visible outside traditional banking. Visa and Mastercard are both building infrastructure for AI-initiated payments, allowing agents to act on behalf of consumers and businesses. Visa Intelligent Commerce is designed to let AI agents find and purchase products on a user’s behalf, with tokenized credentials, authentication and spending controls built into the payment flow.

As agents move from recommending actions to executing them, banks will need to ensure transactions remain within the customer’s intent and risk tolerance. The institution remains responsible for keeping the agent within those limits.

The architecture has to change with the AI

Deloitte found that integration with existing systems and tools is the top modernization challenge for 77 percent of banking executives deploying AI, ahead of security, compliance and cloud interoperability concerns.

Traditional banking systems separate payments, lending, accounts and compliance across different applications. AI agents need to work across those boundaries, combining data and actions from several systems to make a single decision.

Banks therefore need an orchestration layer that allows AI to access those systems without requiring another custom integration for every use case. The core platforms can remain systems of record, while more of the decision-making happens above them.

This gives AI-forward fintechs such as Revolut or Ramp, as well as new entrants designing their infrastructure from scratch, an advantage over institutions that must retrofit deeply embedded systems. If regulation remains broadly unchanged, the first major financial institution built around continuous decision-making could emerge within five years.

It may not be a bank in the strict regulatory sense, but it could perform an increasing share of a bank’s functions, allowing customers to manage their finances.

To thrive, I believe banks need to become institutions organized to make continuous decisions for the customer’s benefit, not simply to follow instructions. And when it comes to AI, they must stop treating it as a fancy tool to add and start treating it as something to build around.

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You will almost certainly have heard many business and technology leaders extolling the virtue of AI. They will enthusiastically tell you that the technology will have a transformative impact on businesses across the globe.

I think they are right. However, with two important caveats: AI must be given the right jobs, and the people using it must trust what it does.

Too often, AI is deployed across a business without much thought, as overconfident bosses assign agents tasks it isn't designed for. The problem this creates is that many employees using AI tools every day are not yet prepared for their new role in managing these agents. Trust only comes from experience and that builds over time.

When an AI system provides you with results that make sense and actually helps with your work, people start to feel more confident in using it. But if it makes a mistake, that confidence can be lost in an instant. If things go wrong, employees start finding ways around AI or stop using it altogether, and when leaders ignore employee concerns about making AI work better, its benefits are undermined.

The best way to ensure that AI delivers the efficiencies it promises is to ensure that it is doing the right job, and this isn't as straightforward as it sounds.

The areas where AI excels

AI works on probabilities, producing answers that are likely to be right. The key word here is ‘likely', which is why it occasionally hallucinates.

Being probabilistic makes AI very effective when dealing with unclear or complicated information. But it's not the best fit for tasks where a wrong answer could have serious consequences.

There are three core areas where AI really can make a difference. Firstly, when processing documents; secondly, decision support; and finally, personalization.

AI is especially effective at tasks involving many documents. It can extract, classify and summarize information that would normally take humans many hours to review. For example, an AI system could compare contracts against a standard set of clauses, or organize a large collection of customer emails by the issues they concern. This way, a person only has to look at the important findings instead of reviewing entire documents.

AI can also play a key role in supporting company executives to make decisions, as it's very good at taking lots of different pieces of information, finding patterns and delivering options as to what to do next.

It is, however, important to remember that AI is not always the right technology to make that decision. For example, imagine you're buying something and have to choose among many different suppliers.

AI can look at all the information, like how well they've done in the past, what they can deliver, how quickly it is likely to arrive and whether the supplier offers discounts for repeat purchases. It can even articulate why one option might be better than another. In most scenarios, though, it is the human who makes the final choice.

For some businesses, personalization is a promising tool, as AI can be used to make products more relevant to the individual using them. This is an area where working with probabilities can become a key driver for AI. The content only has to be good enough to make that connection so the recipient feels they are being addressed in a bespoke way.

The cost of assigning AI the wrong job

One of the key concerns companies should have about managing AI is that while AI-generated answers can sound assured, there is still a possibility that the technology has got something wrong. If that answer is allowed to trigger action without validation, a small error can travel rapidly through a workflow.

Think about what happens when someone makes a small mistake with a rule or detail in a contract. By the time someone catches the error, it may have already been added to records, sent to people outside the company, or even used to create new software.

To remind myself of AI’s limitations, I find it useful to think of the technology as the equivalent of a super-intelligent, hard-working intern. Interns need guidance and oversight, which should be provided by experienced, knowledgeable colleagues. Crucially, their access to sensitive information needs to be limited and granted only as they demonstrate they can make sound decisions.

AI needs to be managed in the same way. It should only be given greater levels of control when it has proved itself, and then its work must still be monitored by humans.

A good example of the importance of managing AI is its role in software development. AI can deliver a lot of code quickly, but its fast pace means the quality isn't always the same as that of human developers.

In fact, managing large amounts of AI-generated code can be a real headache for IT professionals. For example, surging token consumption and a shift to consumption-based pricing is ballooning AI coding costs, forcing developers to be selective on when and where they apply AI models, as reported by Gartner.

Let AI help design the model, not control the machinery

The most important design decision is where AI is allowed to reason. The riskiest place is deep in the implementation layer, where a small mistake can cause big problems that are hard to fix.

I believe a sound approach is to use AI to work with business ideas and rules. This way, it's easier for people to check and understand what the AI is suggesting.

Domain-specific languages and other abstractions used in model-driven development can provide that separation. AI helps create or refine a high-level application model, while a developer then validates the model before a deterministic platform transforms it into executable software.

AI can suggest how something should be done, but it doesn't have to be responsible for executing every step.

Abstraction helps developers, and in agent-driven systems it becomes a control tool. It simplifies complex systems and prevents small errors from becoming big problems, making AI output easier to test and fix.

Human expertise is becoming more valuable, not less

As AI becomes more prevalent, developers' roles will evolve. They will still write code, but a larger part of their role will be overseeing systems, checking outputs and catching mistakes, a change that brings with it the opportunity to learn new skills.

Developers will increasingly work at the level of systems and architecture, understanding the business rationale, recognizing patterns and judging whether AI-generated code fits the wider application and holds together.

One company that has innovated in this area and is already reaping the rewards for their experience is Ford. They recently hired 350 experienced engineers to help train younger colleagues and improve their AI and automation tools.

The shift was driven by the fact that the AI tools weren't producing the results they wanted without the expertise brought by seasoned professionals. Now, these experienced engineers act as internal auditors, reviewing designs and finding potential problems before they become major issues on the factory floor.

When you combine AI's ability to recognize patterns and process information with human knowledge and experience, it becomes a much more powerful tool. This combination is what makes it truly effective, not just relying on one or the other.

Building trust

Enterprises should also resist the pressure to deploy agents everywhere simply because competitors appear to be doing so. When it comes to using AI, leaders need to think carefully about each situation.

They should ask themselves three important questions. First, are they asking AI to only suggest what to do or to actually do it? Next, can someone check and fix what the AI says before an error spreads? Finally, what would happen if the system is certain about something but is actually wrong?

The answers play a big role in deciding how much freedom to give the technology. For example, a tool that summarizes documents might only need a quick check, but a system that suggests business decisions needs someone's logged approval.

When companies start using AI for specific, practical tasks, it shows employees that it can really make a difference in their work. This approach gives the company a chance to get its data, rules and processes in order. If people can see that the system is working well, and they understand what it can and can't do, they start to trust it more. Over time, this trust grows.

The key is to create a work setup where humans and machines do what they're good at. AI is great at processing large volumes of information and surfacing patterns. People are good at understanding the bigger picture, pointing out mistakes, and taking responsibility for their actions.

The wrong job to give to AI is any task where a mistake carries real consequences, no one checks the work before it causes harm, or the system sounds certain while being wrong. Companies that respect this division will build the confidence to give agentic AI greater autonomy over time. Those who ignore it may discover that a single poorly chosen task can undermine their entire AI strategy.

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• OpenAI agent breached Australia’s Medicare portal on June 18, 2026, accessing internal files
• Government says no personal medical data was taken, but three other agencies may be affected
• PM Albanese slammed OpenAI’s 84‑day delay in disclosure, calling the notification “unacceptable” and warning of legal consequences

An OpenAI agent has allegedly broken into a website of the Australian government, which has slammed the “unacceptable” attack, promising an in-depth investigation, and threatening “legal consequences”.

Australian Prime Minister Anthony Albanese revealed how in June 2026, OpenAI’s research team tasked the agent with researching public medicine spending, as part of an internal capability evaluation - but as the agent got to work, it was initially denied access to some of the information it requested.

However, instead of stopping, or trying to find a different lawful way of obtaining this data, it circumvented those restrictions and landed inside the infrastructure behind Services Australia’s public-facing Medicare Statistics Reporting Service portal.

AI agent did what?

The public details are still quite limited, and we don’t know the technicalities of what the AI actually did.

The acting prime minister, Richard Marles, told the media during a recent press conference that the AI “scaled the fence”, despite the portal having security measures in place.

The bot apparently accessed internal infrastructure and wrote files to an internal server but exactly what it wrote, how it obtained the access, and precisely which technical mechanism it used, is still not public knowledge.

Once inside, it accessed both public and non-public files, but individual medical data was not accessed and the system itself was not compromised, the Australian government said.

There is also the possibility that three other government systems were affected - the Australian Institute of Health and Welfare and two state-based agencies - the New South Wales Bureau of Crime Statistics and Research and the Victorian Department of Health. However, this has not been confirmed yet.

"No personal information is believed to have been accessed at this stage, but investigations are ongoing," Albanese said. "Nonetheless this situation is obviously unacceptable."

OpenAI's sluggish escalation

Albanese was particularly unsatisfied with how OpenAI handled the situation. The breach happened on June 18, but it took the company 84 days to notify the Australian government of the incident. And when it did - it did so in a manner better suited for an amateurish start-up rather than one of the most important organizations on the planet right now.

OpenAI was apparently evaluating its models, and investigating “misaligned model activity” when, on August 11, it discovered the breach in Australia. It seems the AI agent simply did not take “no” for an answer. The company then notified Services Australia on September 10 - almost three months after the incident. To make matters worse, the company reached out via publicdisclosures@servicesaustralia.gov.au, inbox researchers usually use to report potential vulnerabilities, instead of trying to escalate the incident higher.

Services Australia reviewed the information and notified the Australian Signals Directorate on September 15.

When the news reached prime minister Anthony Albanese, he spoke to OpenAI CEO, Sam Altman, and expressed the country’s “extreme concern” about this incident, as well as his “disappointment that it took the company way too long to inform the government what had occurred.” He also pointed out the way OpenAI reached out: “The notification was an email sent just to the public mailbox.” Albanese described the “nature of the notification” as “unacceptable.”

The Australian government is now looking into the matter to see if any laws were broken and what legal consequences, if any, could follow.

"And obviously we will investigate all of that. What the consequences are, if there has been a breach of the law, but also part of the task force is to assess whether or not the legal regime we have in place is fit–for–purpose in a world where we have an emerging AI capability,” Marles said.

Via BBC

Artificial intelligence has become a significant area of investment for UK businesses. More than £6 billion of new AI-related investment was announced during London Tech Week in June, while the UK remains home to the largest AI sector in Europe and the third largest globally.

From customer service and knowledge management to software development and internal operations, organizations are looking for places where AI tools can improve productivity, decision-making and business performance.

But as investment accelerates, another gap is becoming harder to ignore: organizations are often scaling AI faster than their ability to measure, govern and explain its value. That matters when CIOs and CFOs are increasingly being asked not simply whether AI is being adopted, but what the organization is getting in return.

As businesses move from individual copilots towards agents embedded across workflows, the economics become more complicated. A user making a single prompt is relatively easy to understand. An agent may make multiple model calls, retrieve information, invoke tools and take actions to complete one task.

Depending on the platform and pricing model, that can introduce additional consumption, infrastructure and oversight costs. CIOs therefore need to understand not only where AI has been deployed, but what it is doing, what it costs and whether the outcome justifies that cost.

The not-so-hidden cost of AI

AI investment is increasingly being scrutinized in the same way as any other major technology investment. The difficulty is that measuring its return can be unusually complex.

Usage may be distributed across departments, applications, models and workflows, while the benefits can range from time saved to improved quality, reduced risk or increased revenue. Without agreeing what success means first, organizations can end up measuring activity rather than value.

That becomes difficult when organizations expand AI without first defining where it sits in the workflow, who owns the outcome, what success looks like and how costs will be measured. Spending can become fragmented across licenses, models, infrastructure, platforms and consumption-based services, with no single view of whether those investments are delivering value.

The scale of the challenge is becoming visible. Research commissioned by Emergn estimates that large UK businesses lose £67 billion annually across transformation and AI initiatives that fail to deliver. Separately, a Censuswide survey of 500 senior UK decision-makers found that just 31% of businesses already using AI reported a positive return on their investment.

Governance is part of that measurement challenge. A 2026 ShareGate survey of 851 IT leaders across seven countries found that cost visibility was the most commonly cited barrier to measuring AI ROI, identified by 51% of respondents, followed closely by governance complexity at 47%. The challenge isn't simply knowing what AI costs. It's connecting that cost to the use case it supports, the information AI interacts with and the outcome it creates.

Much of the AI debate to date has focused on model selection, skills and productivity. But as adoption spreads, another gap is becoming visible: confidence in governance does not always match what happens in practice.

The same study found that 93% of IT leaders believed their Microsoft 365 governance was ready to support AI responsibly, yet 29% reported that AI tools had surfaced sensitive internal data that should not have been accessible. Another 8% weren't sure whether it had happened at all.

Those gaps carry costs as well as risk. Duplicated tools, additional validation, rework, security investigations and time spent establishing whether an output can be trusted all create a hidden tax on AI adoption. For CIOs trying to demonstrate value, reducing that friction starts with making AI usage more visible, accountable and measurable.

How organizations can regain control

Good governance doesn’t begin and end at procurement. Knowing how many licenses have been purchased and where they have been assigned is useful, but regaining control requires a broader view: visibility into how AI is being used and what it costs, clear ownership of the outcomes, and a well-governed information environment for AI to work from.

Clear ownership matters just as much. As AI becomes embedded in business processes, responsibility can easily become fragmented across IT, security, business teams and individual employees. That makes some basic questions surprisingly difficult to answer: Who owns the outcome? Who monitors the cost? Who decides whether a use case should scale, change or stop?

Cost governance is only part of the picture. Organizations also need to improve the information environment in which AI operates. That means reducing redundant and outdated content, managing access appropriately and helping employees and AI systems find authoritative sources.

Cleaner, better-governed information does not guarantee a correct AI response, but it can reduce ambiguity and make reliable grounding easier. That can mean less time spent searching, validating and reworking outputs.

Employees have a role here as well. Clearly distinguishing drafts from approved material, keeping trackers and priorities current, recording decisions and maintaining authoritative sources all make organizational context easier for people and AI to interpret. Where organizations use AI meeting assistants or similar tools to capture context, those tools should be subject to the same privacy, retention and access controls as the information they create.

The question for leaders, then, is no longer simply whether AI is worth the investment. It is whether they have enough visibility and control to understand where AI is creating value, where it is creating cost and what they should do differently as a result. AI investment is likely to continue, but under different expectations.

Deployment alone is not evidence of value, and governance is becoming more than a risk-management exercise; it is increasingly part of the business case. Leaders need to understand what AI costs, who is accountable for its outcomes, whether the information supporting it can be trusted and what measurable benefit it creates.

The organizations best positioned to scale will not necessarily be those deploying the most AI, but those that can explain what it is doing, understand what it costs and make informed decisions about where it belongs.

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• Revolut reveals new payment method which just needs a smile
• Facial recognition technology will help identify Revolut customers
• Technology could help protect SMBs from losses caused by tech outages

If you've ever been about to pay for something in a shop and suddenly realised you've misplaced your wallet, Revolut may have the answer to help avoid any more embarrassing situations.

The payments giant is launching Revolut Pay with Smile, the UK’s first in-store facial recognition checkout, meaning customers can pay with a glance or even a smile - no need for a wallet or even a phone.

The move looks to make sure that UK small businesses don't lose money from payment terminal issues, whether hardware or software-related, offering a new way to accept payments.

Pay with Smile

We were invited along to try out the new Revolut Pay with Smile at a Kiss the Hippo Coffee shop in central London, where customers could try out the new technology.

Guests who paid in-store with a smile were able to order from a special Revolut menu, with speciality coffees at £1 - a bargain in today's market.

We gave it a go, and found the process to be pretty painless - much like authenticating yourself on a phone app, or having your photo taken at passport control, it was fairly quick and smooth to get through - and the coffee wasn't bad either.

All it took was a quick facial scan, and the system verified the payment in a matter of seconds - quicker than many contactless payments, we'd say.

Users will have to be existing Revolut customers, as the system uses advanced facial technology to verify the user's identity against the selfie ID checks they added when first signing up to Revolut to authenticate and process a payment securely.

Powered by the new Revolut Register, a modular, two-screen register designed to bring together payments, banking, and floor operations, Pay with Smile offers 0% processing fees for merchants, a huge boost when overhead costs are higher than ever.

Transactions are authenticated with end-to-end encryption managed inside the Revolut app, with no personal data stored by merchants

Revolut Register is available now with a special 50% discount for £349.50 + VAT (RRP: £699 + VAT) on a merchant's first terminal until 31st December 2026 for both new and existing customers.

Artificial intelligence (AI) is like a genius with no memory of you. It has plenty of brain power, but unless context is handed to it directly, it knows nothing about your business, your customers, or the decisions that shaped how you operate today.

And solving that memory problem is quickly becoming a major barrier to enterprise AI deployment today.

Much of the current conversation about AI's limitations centers on the models themselves and their evolution toward bigger context windows, better reasoning, or longer memory. But that framing misses something important.

Enterprises don't need to wait for the next model breakthrough to solve this problem. They already have what they need sitting inside their own organizations: decades of content, decisions, and institutional context that most AI systems never see.

Every enterprise has a vast, largely untapped record of how it actually operates in the contracts negotiated, claims resolved, cases handled, decisions made and revisited. This is the accumulated context that makes an organization's judgment distinct from a general-purpose LLM. Yet in most companies, this information sits fragmented across systems, buried in unstructured formats, or locked away with no clear path for AI to reach it.

That is the gap that needs closing before AI automation can scale responsibly. Not a smarter model, but a more complete memory built from an organization's own history rather than a general-purpose training set.

Think of this as "business memory": the nervous system connecting what a company already knows to what it wants AI to help it do next, carrying context to wherever a decision needs to be made.

Giving AI the full picture

Business memory is best described as the accumulated knowledge of how an organization operates. It turns years of enterprise content, workflows and industry knowledge into information that AI can act on for true agentic automation at scale. The thinking behind this is simple.

By creating a context layer that governs what data the AI has access to, and ensuring it operates on relevant, authorized and current information, organizations will get those governed, trusted outputs they need to act with confidence.

An organization's memory is more than the information it retains. It also includes which version is authoritative, who may access it, where it came from and how long it remains valid. Carrying those signals into the context layer helps AI operate within the same rules as the business itself.

Better context alone does not ensure trustworthy outputs; provenance, evaluation, monitoring and human oversight also matter.

And although it accounts for an estimated 80% of enterprise content, based on various analyst reports, businesses use only about 10% of this incredibly valuable resource.

If organizations want to maximize the effectiveness of their AI models, the first thing they need to do is to start leveraging that valuable business memory contained in their unstructured data. And that requires investing in your infrastructure to transform it into an AI-ready format.

In practice, start with a clearly defined use case and identify the authoritative content and data needed to support it. Preserve existing access controls, enrich the information with metadata and relationships, and test whether the AI's outputs are accurate, traceable, and useful before expanding automation. This creates a repeatable foundation that scales across the business without a wholesale replacement of existing systems.

Context is key

However, giving your AI access to the relevant data is not enough on its own. In fact, I’ve seen a lot of AI initiatives fail in the early stages because the business doesn’t appreciate just how complex a lot of this underlying data is.

As such, the content needs to be contextualized by an ‘ontology’, a formalized framework that defines all the different entities, terminology, relationships and rules that exist within your business. This enables your AI to understand not just what information exists across your systems, but how all of that information relates to your business or industry.

Put simply, ontologies are like maps: your AI may well get to your desired destination without one, but having it ensures you get there quicker and without potentially stumbling into any pitfalls along the way.

This is particularly important for regulated industries. In healthcare, for example, ontologies connect diagnoses to treatment plans, physician notes or lab results. Or in financial services, they link industry-specific regulations to the organization's compliance structures and policies.

In other words, unlocking unstructured data provides crucial business context that helps create better, more informed outputs from your AI. But that, in turn, needs to be contextualized by an ontology so that AI can understand data properly.

Taking control of AI memory

AI doesn’t need perfect memory to understand how your business operates and start creating trustworthy, valuable outputs. What it does need is relevant, governed context; most of which lives in unstructured content but only becomes useful once connected to structured enterprise data.

Infrastructure that can enable your systems to access the right information, understand how that data fits together and then apply it within the realities of the business is, therefore, essential. But crucially, that doesn’t mean you have to start from scratch.

In fact, reinventing your foundations would essentially defeat the point. Real business memory understands the systems, content, data, and processes that already exist within your organization, so throwing everything out in the pursuit of better context is unnecessary and, frankly, a huge waste of resources.

You don’t have to wait for the next model update to cure AI’s amnesia, and you don’t need to completely change your current stack to see real value from your automation projects.

By building in the foundations for business memory, organizations can take back control, make the most of their AI systems and make their infrastructure more resilient and adaptable for whatever models the future holds.

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• $1.6 billion has been claimed by scammers impersonating police and government officials
• The FBI’s Internet Crime Complaint Center (IC3) received almost 61,000 complaints concerning such scams between January 2025 and July 2026
• Some scams targeted foreign nationals and immigrants, threatening to cancel passports or impose extradition, unless payment is made

Between January 2025 and July 2026, the FBI’s Internet Crime Complaint Center received almost 61,000 complaints concerning scams using police and government official impersonation tactics. As a result, around $1.6 billion is believed to have been scammed and extorted.

Tactics used by scammers included unsolicited phone calls and in some cases video calls, with victims losing an average of $26,000 per scam.

Particularly concerning is the targeting of foreign nationals, international students, and immigrant citizens, often with threats to cancel visas and home country passports. Incredibly, 10% of all losses were traced to a single scam, one that targeted fewer than 3% of the victims.

Foreign national victims

According to the FBI, the fake cop/fake government official scam has various angles, from alleging missed jury duty to threatening arrest due to circumstantial links to an alleged crime.

The demand from the scammers, inevitably, is payment.

Making a commitment to extreme lengths of scamming and extortion, the criminals took things to a new level when it came to targeting victims. International students, visiting foreign nationals, and other immigrant citizens were given special treatment. Not only were they threatened with extradition, there was also the implication of victims losing their home country passport.

To convince the target, scammers built actual studio sets, donned uniforms or suits, and appeared in video calls to the victims, who were convinced they were talking to foreign law enforcement agents or US-based diplomats. Creating the illusion of officialdom to sell the scam, victims paid whatever necessary to stay in their adopted country.

How to spot the scam

It is important to note that not all fraud of this type takes place in the US. These may be online or offline, but the so-called fake cop scam is an international criminal phenomenon. So, how do you avoid it?

We’ll assume you’ve answered a call inadvertently rather than having spam calls blocked (that should be set already).

First, remain calm. These situations are designed to impose stress and heightened anxiety. Scammers rely on these factors to cloud their victim’s judgement and force the narrative they are selling (“you missed jury duty”/“your presence in the country is illegal.”)

Second, it is vital to request credentials. Failure to provide these is the first red flag that the person communicating with you is not what they seem. That’s your cue to end the conversation.

Third, check the credentials. It doesn’t matter how long this takes – a legitimate caller will not mind waiting for you to do the necessary background check, even if it means them calling back.

Finally, and most importantly: neither law enforcement, security services, nor the FBI will demand money. If that is happening, it’s time to end the call.

• Two artificial whiskers give this tiny drone a working sense of touch
• Pressure sensors at the whisker base detect surface contact instantly
• Onboard software processes tactile data using only 34 kilobytes of memory

A research team from Delft University of Technology in the Netherlands has developed a tiny drone that uses artificial whiskers to navigate where conventional sensors struggle.

The system relies on physical contact rather than cameras or satellite positioning, allowing the aircraft to detect nearby surfaces through touch.

The researchers designed the technology for drones weighing below 100 grams, where adding substantial sensing equipment can quickly consume available capacity.

Tiny whiskers give the drone another way to sense surroundings

The device carries two slender filaments near its nose, angled upward and connected to miniature pressure sensors underneath.

When either filament touches an object, pressure changes provide information that helps the aircraft estimate its position relative to nearby surfaces.

That information allows the drone to move around obstacles, maintain contact with structures, and construct a basic representation of surrounding spaces.

The approach takes inspiration from rodents and other mammals, which use facial hairs to move through confined environments with limited visibility.

“Here, we aim to equip drones with rich tactile sensing — not for manipulation in the air, but for a novel concept of tactile navigation,” said Salua Hamaza, Associate Professor of Aerial Physical Interaction and Embodied Intelligence.

The system needed to be lightweight, process rapidly, and consume limited energy before tactile navigation could become practical aboard small aircraft.

The sensing system runs on only 34KB

A major hurdle was airflow, since passing wind can easily disturb whisker readings and confuse the pressure sensors beneath them.

To solve this, the researchers wrote a lightweight processing program that separates turbulence from genuine surface contact in real time with millimetric precision.

The complete onboard software occupies just 34 kilobytes of memory, keeping the computational requirements unusually low for an autonomous sensing system.

Traditional navigation software built for larger drones typically demands considerably more computing power than this compact whisker-processing system requires today

“We wanted to show that touch does not have to come at the cost of size or computational power,” said Chaoxiang Ye, a PhD candidate and researcher at Deft.

The small size allows the drone to interpret environmental information without depending on external processing.

The approach will not suit the fastest drones, but for small rescue units, sensing through touch may prove genuinely valuable.

This device is especially useful for environments where smoke, dust, darkness, or damaged infrastructure could interfere with cameras and other optical equipment.

One potential application of this drone is in collapsed buildings where rescue operations can prove to be difficult because of instability and tight spaces, and regular aerial sensing may be unreliable

Small drones could enter restricted spaces ahead of human responders, using physical contact to gather information about nearby structures and possible pathways.

The system could also provide another sensing method when GPS signals are unavailable or visual information becomes difficult to interpret.

Via TomsHardware

• Russia is testing ammunition that splits apart to confront small drones
• Russian testing found separated elements reaching critical components inside drones
• The ammunition fits standard rifles instead of requiring specialized counter-drone weapons

The Russian military is developing a multi-bullet cartridge designed to give standard Kalashnikov rifles a new way to engage small drones during combat.

The ammunition separates into several elements during flight, creating multiple projectiles from a single round fired through a compatible rifle.

Military expert Yury Knutov said the design is intended for troops who may encounter drones without having access to specialized firearms.

A multi-part cartridge for standard rifles

The reported cartridge uses the 5.45mm format and can be loaded into a standard AK-12 magazine without requiring a different rifle platform.

Its unusual construction keeps several elements together before firing, after which they separate while travelling toward the aerial target.

The concept has drawn comparisons with a Matryoshka doll because multiple components are contained within one projectile before separating in flight.

Pervy Tekhnichesky reported in April that Kalashnikov Concern had developed and tested the ammunition as a potential option for engaging small drones.

The reported trials included individual shots and bursts, providing an initial assessment of how the multi-part projectile performed against unmanned aircraft.

Some of the separated elements reportedly reached batteries, electronic boards, and power-related components during those tests.

Those components are critical to drone operation, making their reported involvement in the testing relevant to the cartridge's intended aerial application.

The ammunition is therefore being developed specifically around small unmanned aircraft rather than as a general replacement for ordinary rifle rounds.

Different performance against personnel

The cartridge has different limitations when used against personnel, particularly when protective equipment is involved during an engagement.

Body armor can reduce its penetrating effect, while hitting an exposed area would still depend on accurate placement of the separated elements.

That distinction keeps the ammunition's reported purpose focused on small aerial targets rather than conventional engagements involving protected ground personnel

“Our specialists have made bullets that separate into several parts, which are used against drones,” said Yury Knutov, a military expert.

“You just need two magazines, one loaded with regular bullets, the other with these special multi-bullets.”

Internal testing reportedly showed that components reached internal drone parts, but that does not establish overall battlefield effectiveness.

As drone warfare continues expanding across the ongoing conflict between Russia and Ukraine, new countermeasures keep emerging from both sides of the front line.

Military analysts frequently propose innovative solutions on paper, yet battlefield conditions often reveal weaknesses that laboratory testing simply cannot anticipate.

The new multi-bullet promises theoretical advantages that still require genuine combat validation before earning credibility.

Until Russian forces deploy this ammunition extensively under real combat pressure, its actual reliability and effectiveness in battle cannot be ascertained.

Independent verification, therefore, remains essential before military observers can responsibly assess whether this technology delivers meaningful battlefield advantage.

Via 1.ru

• Tether's African model supports 19 languages while running directly on local devices
• Tether removed 96% of low-quality training material before building AfriSLM
• EuroNano supports 90 translation directions while occupying only 36MB

Tether AI Research has unveiled a new set of translation systems that work entirely offline, requiring no connection to any network.

The release includes QVAC TranslatePsy-AfriSLM, built for 19 African languages, alongside QVAC TranslatePsy-EuroNano, built for nine European languages.

Each model processes translations directly on the device, keeping personal data local rather than sending it to remote cloud servers.

Translation designed for devices with limited resources

The African release includes Hausa, Amharic, Yoruba, Lingala, Swahili, Igbo, Zulu, Somali, Oromo, Malagasy, Kinyarwanda, Xhosa and Afrikaans.

It also handles Wolof, Luganda, Nyanja, Shona, Tswana and Southern Sotho, covering languages used across several regions of Africa.

Tether says those 19 languages collectively account for about 50% of the continent's population — the figure represents language reach rather than individual users.

AfriSLM contains 800 million parameters, yet Tether says the LLM surpassed Qwen3.5-122B-A10B, TranslateGemma-27B and NLLB-3.3B.

The comparisons were measured using FLORES-200, BOUQuET and SMOL, although benchmark performance does not establish identical results across every practical translation situation.

Tether attributes the results partly to a data-screening system that discarded as much as 96% of material judged unsuitable.

The company also released smaller and larger versions, including 0.8B, 2B and 4B parameter configurations for different hardware requirements.

EuroNano takes another approach, using English as an intermediary language while connecting European languages through 90 possible translation routes.

That package occupies only 36MB, which Tether says represents about 94% less storage than a comparable Firefox offline arrangement.

The compact footprint means translation can remain available after connectivity disappears, without requiring separate downloads for every language combination.

That distinction matters for field workers, travellers and local applications where storage capacity and network availability can both impose practical constraints.

Why Tether is starting with African languages

Tether's African focus also connects with the physical infrastructure it has developed across parts of Sub-Saharan Africa.

The company has installed solar-powered kiosks that provide phone charging, battery exchanges and access to digital financial services in communities.

Those locations could eventually provide another route for distributing educational material, agricultural information and other locally translated content without depending on continuous connectivity.

“Four billion people were left behind by the traditional financial system, and the most powerful technology of our age has repeated that failure,” said Paolo Ardoino, CEO of Tether.

“Language should not determine who can benefit from artificial intelligence. Open translation models like these are a step toward a future where education and AI tools reach hundreds of millions of people who have neither reliable connectivity nor access to expensive systems.”

The same technology could also operate alongside QVAC MedPsy, Tether's smaller model intended for healthcare-related applications on local devices.

“A mother could get real medical information she understands, instead of guessing. A child could learn in their own language. That is the future we are building through QVAC,” Ardoino added.

AfriSLM is currently available through Hugging Face, and the associated research has also been accepted for presentation at EMNLP 2026.

• MORENA beat 26 tested models despite having only 1.5 billion parameters
• The model from Vambo AI was built specifically around African languages from scratch
• MORENA uses fewer tokens to represent African text than major rivals

Vambo AI has released MORENA, a 1.5B parameter language model covering 12 languages spoken across Africa, plus English, French and code.

The developers say it beats models from Google, Meta, Alibaba and HuggingFace on African language modelling and translation while being up to 8x smaller.

On a key benchmark, this LLM scored 1.408 bpb — the best of 26 models tested — beating its closest rival, which carried over five times as many parameters yet still scored only 1.423 bpb.

Training without a borrowed foundation

The model covers Nigerian Pidgin, Igbo, Yoruba, Hausa, Kiswahili, ChiShona, isiZulu, isiXhosa, Kinyarwanda, Setswana, Afrikaans, and isiNdebele.

Most projects serving these languages continue training an existing Llama variant, which forces them to keep a vocabulary meant for English and programming text.

Vambo AI instead fixed the tokenizer, the data mixture, and the language list before training began, after comparing several vocabulary sizes for cost and efficiency.

MORENA's vocabulary encodes African text with 1.39 times fewer tokens than Gemma 3 and 1.53 times fewer than Llama 3.2 on identical passages.

African text still costs 0.249 tokens for each byte against 0.234 for English, roughly 6% more, and the team cannot fully explain the difference.

The nearest competitor, Lugha-Llama-8B, an Africa-adapted Llama 3.1 variant, scores 1.423 bpb and loses to MORENA in eight languages out of 12.

The 12B Gemma system, by comparison, consumes roughly 11 times the compute of MORENA while producing a weaker score on the same text.

The chat-tuned instruct version scores 1.441 bpb, trailing Lugha-Llama-8B overall but leading it in five shared languages while using about 20% of its parameters.

The instruct model, after seeing three sample translations, renders English into five languages from Africa at 45.8 chrF++, statistically level with one dedicated translation system.

It trails a larger translation model by about 1.4 points, while general models of similar size typically land in a range from 9 to 14.

A family built on 22,000 GPU hours

MORENA used 251.7B tokens during pretraining, followed by another 63B tokens during its mid-training stage.

Over 22,000 A100 GPU hours were reportedly required during development, with the associated computing resources valued at roughly $40,000.

According to the developers, this project was supported by UNDP, AIHub4SD, and CINECA, providing computing resources and other assistance during development.

This project also includes smaller releases, with versions containing 0.5B and 0.2B parameters available alongside the principal 1.5 billion model.

The 0.5B variants reportedly exceed every external system tested across 11 of the 12 languages covered by the project.

The 0.2B nano version is intended for speech-recognition rescoring, keyboard applications, and text normalisation tasks.

This nano version also costs 58 times less than Lugha-Llama-8B for each byte processed and generates 104 tokens each second on one A100 GPU.

A CPU-compatible build of the model also runs offline on a laptop, which suits settings where reliable GPU access is not guaranteed.

That gives the developers several model sizes for different computing requirements, rather than relying exclusively on the largest release.

MORENA also supports conversational applications, translation, and connections to other AI tools through its instruction-focused release.

Via Glitch Front

• TENKU Note Pro AI uses a full Windows 11 Pro setup inside its tiny convertible chassis
• The 960g notebook folds completely around its touchscreen for tablet use
• Intel's Core Ultra 5 125U brings dedicated AI hardware to TENKU's compact computer

TENKU has launched a compact Windows 11 Pro computer in Japan, combining Intel processing hardware with a touchscreen and convertible design.

The Note Pro AI uses a Core Ultra 5 125U processor with 12 cores and 14 threads, alongside hardware dedicated to selected AI workloads.

At 960g, the laptop combines a 10.51-inch screen with a physical keyboard while remaining considerably smaller than conventional full-size notebooks.

A convertible design built around Intel's mobile processor

The device uses a 360-degree hinge, allowing its keyboard section to fold behind the screen when tablet operation becomes more suitable.

Its touch panel recognizes ten simultaneous inputs, while an optional digital pen supports 4,096 pressure levels for writing and drawing applications.

The IPS panel produces 1,920 x 1,280 pixels, providing a 3:2 display area for Windows applications and productivity software.

Intel's Core Ultra 5 125U also includes an integrated NPU, which supports functions such as background processing and noise reduction during video calls.

The system includes 16GB of LPDDR5 memory and a 512GB M.2 SSD for applications, documents, and other locally stored data.

Wireless connectivity covers 802.11a, b, g, n, ac, and ax standards, with Bluetooth 5.2 available for compatible accessories and peripherals.

Battery capacity, dimensions and pricing

Under the hood, the device packs a 3,800mAh battery that is smaller than many smartphone batteries today, yet TENKU rates it for 17 hours.

According to the company, the chassis of this notebook measures 243mm across, 177.6mm from front to back, and 17.5mm thick.

Its compact chassis leaves less internal space than larger notebooks, while retaining the hardware required for its convertible configuration and cooling system.

On the software end, this device comes installed with Windows 11 Pro, giving the computer access to desktop applications rather than restricting its use to tablet-oriented software.

The available AI functions depend on software support, meaning the processor's dedicated hardware does not automatically provide every possible feature across Windows.

TENKU has listed the Note Pro AI in Japan, and it is currently retailing for about 176,800 yen (about $1,127) including tax.

There is also a launch promotion that cuts the price by 25% through Highbeam's official online store and physical outlets.

The 25% discount takes the promo retail price to 132,600 yen (about $845), and the offer ends on September 24 2026.

Amazon Japan is also offering a separate 15% reduction during the same promotional period, giving buyers another discounted purchasing channel until September 24.

Via Hermitage Akihabara (originally in Japanese)

• Interpol's Operation Shams II pulled 108,076 faces from jihadist propaganda with help from 11 countries
• AI agents and scripts then deduplicated and quality-checked the set, leaving 6,362 images (~5.9%) for facial recognition
• Interpol's facial recognition system, launched in 2016, then matched pictures to actual persons, with human verification in tow, and 126 suspected fighters have been identified so far

Interpol says an operation it coordinated in Tunisia in June 2026 used artificial intelligence to work through more than 100,000 faces taken from jihadist propaganda, producing matches against 126 suspected foreign terrorist fighters.

AI did most of the heavy lifting, condensing a mammoth 108,076 images into 6,362 unique, identifiable images that could be run through its decade-old facial recognition system.

This allowed 126 people to be identified on paper as potential terrorists who would still need to be tracked down by the international police organization.

A numbers game, cut down to size

Interpol says programmed AI agents and AI-generated scripts automated parts of the collection and investigative work, removing duplicates and screening out poor-quality faces.

This approach left 6,362 unique, high-quality images, which were then run through the Interpol Facial Recognition System (IFRS). This effectively cut a trove of images into a non-duplicated, actionable list of only ~5.9% of the original set, which could then be used to identify potential terrorists.

By eliminating 94% of the images that would otherwise have to be processed manually, AI reduced the resources that would otherwise have to be deployed to reach a similar conclusion.

This is particularly handy, given that propaganda images often number in the thousands and investigators needed to sift through them manually to identify potential targets, many of which were duplicated by others involved in the same study.

“The success of Operation Shams II demonstrates the value of combining international law enforcement cooperation with responsible AI-assisted analytical capabilities," noted Interpol's Director of Counter-Terrorism, María Carmen Muñoz González.

"The operational model provides a strong foundation for supporting future international law enforcement cooperation targeting criminal actors around the world.”

Its automation attempts are not new; In 2019, Digit reported, citing Germany's Interior Ministry, that Interpol had presented a project called DTECH-Light intended to "detect, extract and analyze digital terrorist content, identify and locate suspects," with national bureaus asked to upload images of unnamed foreign fighters for comparison.

Interpol does not say what agents it used, which models or vendors sit behind them, or where they ran. "AI-generated scripts," it says it used, could simply mean automation code written with the help of a large language model rather than any purpose-built system.

For now, an AI is doing the grunt work so that a system launched in 2016, and the officers who check its output, can do theirs considerably faster, which is a significant win in efficiency for an agency that often has to deal with multiple sources of information, even as fears about the tech have been rekindled globally on multiple fronts.

GMKtec's M8 packs a genuinely capable office machine for the daily grind at your desk - we scored it 4.5 stars with a Highly Recommended award in our review. And for a limited time, it's discounted at Amazon.

• Best for: Remote workers and office professionals who want a reliable and powerful machine for work.

• The deal: The GMKtec M8 mini PC is now $390 (was $480) at Amazon and in the UK, the M8 is now £370 (was £439).

• Why it matters: The M8 is an exceptionally good mini PC for work - it cut through everyday office tasks like nobody's business, feeling smooth and responsive thanks to the AMD Ryzen 5 PRO 6650H and 16GB LPDDR5 RAM. Bonus points, too, for the port selection here, which includes dual 2.5G LAN, USB4 and OCuLink for an external GPU if you need more graphics power later, all in an ultra-compact case.

Why we recommend it

We tested this exact configuration in our 4.5-star review, and found it "handles productivity with ease." It sailed through all the office tasks we threw at it, smoothly tackling the Microsoft Office suite, video calls, browsing, and streaming without any issue thanks to the powerful Ryzen 5 PRO 6650H chip.

During our testing, it handled reasonably complex edits and batch processes in Photoshop and Lightroom well - the 16GB LPDDR5 felt closer to 32GB, which surprised us. However, it struggled with 4K video editing in Premiere Pro, although HD footage felt fine. If you're performing creative workloads, there is an OCuLink port for attaching an external GPU.

For more top-performing machines, see our guide to the best mini PCs we've tested.

Price Context & Historical Value

At $389.59, this is a meaningful price cut on a machine we've personally tested and enjoyed using. It's not the lowest ever price we've seen this configuration - it dipped to $360 recently - but it's the cheapest current price. For comparison, GMKtec lists it at a discount $389.99 on the official site.

Should you buy it?

✅ Buy the GMKtec M8 mini PC if...

You want real office performance in an ultra-compact case, or you want to hook up an eGPU for more creative workloads.

❌ Skip the GMKtec NucBox M8 Mini PC if...

You want a graphics powerhouse for 3D rendering, complex video editing, and gaming, or you'll need more RAM than the soldered 16GB offers.

Also consider

• See all mini PC deals at Amazon.

• Cybersecurity company OPSWAT found a login bypass and a crash bug in TP-Link's Tapo C200 cameras
• TP-Link has extended that to include its C120 offering too
• Anyone on the same network could get admin access, live video, and recordings without requiring the owner's credentials

Security researchers at OPSWAT have detailed two high-severity flaws in TP-Link's Tapo C200, a pan-and-tilt indoor camera listed on Amazon for $26.99 and sold as a baby monitor and pet camera.

The more serious issue is that someone on the same network can log in as the camera's administrator without the password, accessing the live feed and stored recordings.

At least one other bug, which OPSWAT rates as critical in the same disclosure but does not detail, has not been published.

A localized login that requires no authentication

The TP-Link Tapo C200, as we noted in previous coverage of the incident, isn't just another security camera that happens to be vulnerable; it is one of the most popular models on the market, clocking in at over 3,000 sales on Amazon alone.

The Tapo C120, in its current iteration, sells over 5,000 units monthly, even as the advisory notes that its V1 hardware version is currently compromised until users update the firmware on their devices.

Both SKUs have received firmware updates that patch the vulnerabilities (CVE-2026-15315 & CVE-2026-15316), which are assigned 'high' scores of 8.7 and 7.1, respectively. However, according to TP-Link, CVE-2026-15316 does not affect the C120 camera.

However, the former vulnerability is the more pressing of the two and, according to OPSWAT, is particularly problematic for users because of how the C120 and C200 cameras function.

Both run a local management interface over HTTPS and use a challenge-response login designed to prove a client knows the owner's password. Khoi Tran, a graduate fellow at OPSWAT, and his mentor, Thai Do of the company's Unit 515 team, found a second verification path in which, under certain conditions, a value the camera hands out during login can be sent back and accepted as a valid authentication response.

The result is an administrator session after a small number of requests, with no password, no existing session, and no requirements for the new 'owner'. As a result, access includes live video, stored footage, and configuration changes.

OPSWAT's researchers pointed out that a C200 used as a baby monitor would expose "live video, night vision, crying detection and two-way audio."

The second flaw, CVE-2026-15316, affects only the C200 and sits in its Wi-Fi onboarding code. Sending the camera an oversized chunk of encrypted Wi-Fi credential data can crash its HTTPS service or restart the device outright, cutting the owner off from management and monitoring until it recovers.

The attacks are somewhat limited in scope: users aiming to exploit such vulnerabilities would need to be on the same Wi-Fi network, or within a certain trusted ecosystem, to begin with.

For now, users upgrading to TP-Link's newest firmware, issued for both models, rectifies both issues, but there might already be another security patch in the works: OPSWAT also found "a critical vulnerability that could allow an attacker to fully compromise the camera," which could then serve as a foothold inside the network.

It is currently holding off on publishing any details about the vulnerability as it waits for TP-Link to issue a patch that rectifies the situation. Neither OPSWAT nor TP-Link, however, has provided a timeline for when the patch will be available to end users.

Obsbot's Meet 2 webcam packs 4K image quality and AI framing into a device small enough to fit in your pocket, and it's discounted below its usual price at Amazon.

• Best for: Remote workers and casual streamers who want a meaningful upgrade over a laptop's built-in webcam without spending much.

• The deal: The Obsbot Meet 2 4K Webcam is now $99 (was $129) at Amazon and £99 (was £129) in the UK.

• Why we like it: We've had great experiences testing out Obsbot's webcam range, so this is an easy recommendation. It features 4K recording at 30fps (or 1080p at 60fps), AI-powered framing and autofocus, a 1/2-inch sensor, dual omnidirectional microphones, HDR, and gesture controls, all in a body just 1.8 x 1.4 x 0.9 inches.

Why we recommend it

Based on our experience with the Obsbot line, this Meet 2 deal is an easy recommendation. While we haven't gone hands-on with it, we tested the original Obsbot Meet (we called it "tiny but terrific" in our 4.5-star review), the dinky Meet SE (the best budget webcam we've ever seen, we said, awarding it 4.5 stars in our review), and the feature-rich Tiny 2, "one of the smartest, cleanest and most travel-friendly webcams," we've ever reviewed.

All three are excellent performers, so expect more of the same with the Meet 2, with high resolution, and core features like AI framing, which is great for staying in shot when you fidget, and the impressive hands-free gesture control.

For more options, see our guide to the best webcams we've tested.

Price Context & Historical Value

At $99.99, this is $29.01 off the $129 list price, a genuine discount on a webcam that was already reasonably priced. It's one of the best prices we've tracked for this model, so it's a good time to buy if you've been considering it.

Should you buy it?

✅ Buy the Obsbot Meet 2 4K Webcam if...

You're a professional upgrading your built-in laptop camera, and you value AI framing that keeps you centered without manual adjustment.

❌ Skip the Obsbot Meet 2 4K Webcam if...

You need professional-grade tracking for content creation - the Obsbot Tiny 2 is the better pick.

• See all webcams at Amazon.

• Privacy policies are famously difficult to read, and in the world of LLMs, they’re impenetrable and long
• The policy for Meta’s Muse Spark is over 14,000 words long, taking almost an hour to read
• Cybersecurity firm Bridewell carried out the study, assessing the privacy policies of 20 popular LLMs

When you input data into an AI chatbot, you expect it to interpret and process the words or numbers to save you time. But what is it doing with that data? You can check the privacy policy to find out – assuming, that is, that you have the time to do so.

A new study has found that the average time it takes to read a privacy policy for an LLM is 20 minutes – and some are a much longer read.

With around 75% of people admitting to using an AI chatbot at least once a month, understanding where the questions and data queries are going and how they are used is increasingly important.

Ease of reading

Privacy policies provide that understanding, but are they easy to read? A study by Bridewell’s cyber security experts suggests not, with many having a poor Fleisch Reading Ease Score, meaning that in layman’s terms, these documents are pretty impenetrable.

Bridewell’s study assessed privacy policies from 20 large language models (LLMs), and found that the average length of time to read one is 20 minutes. The average word count is 4,603, but the real challenge is understanding the information.

The Flesch Reading Ease Score is a measure of readability devised by Rudolf Flesch in 1948, and is widely used to score texts. A score over 60 is good, whereas a score below that is not. The average Flesch score for these privacy policies is 40.2, suggesting a degree of jargon-based density that most people will not understand.

How, then, might the general public safely use an LLM chatbot like ChatGPT or Google Gemini while in full knowledge of how the information they input is being used?

Training on inputs

In evaluating the LLMs, Bridewell found that 13 of the 20 use inputs and outputs to train their models. Some LLMs offer the option to opt out of training, but others do not. Even where an opt out is possible, it isn’t always clear how to action it.

“It’s essential for users to fully understand how their data is being processed by LLMs, and businesses need clear internal guidance on what can and can't be shared, and ideally proper enterprise accounts with the right protections in place," noted Chris Linnell, Associate Director of Data Privacy at Bridewell,  "employees may be at risk of sharing highly sensitive or confidential information that may end up being used to train LLMs.”

There isn’t just a personal risk from data input into an AI. Employees need to be aware of how they are using the technology for work.

As a rule of thumb, the more complex the LLM (e.g., Meta's Muse Spark, or Moonshot AI’s Kimi K), the longer the privacy policy takes to read.

• Microsoft, UK police, and partners disrupted EvilTokens PhaaS, arresting two suspects and seizing 200+ domains/sites
• EvilTokens used AI to scale device‑code phishing, compromising 12,000 inboxes across 10,000 organizations globally
• Platform ran like a startup with subscriptions, dashboards, and AI‑driven targeting; US victims hit hardest

Two people have been arrested, 50 websites were seized, and 150 domains disabled, in a joint operation against the infamous EvilTokens phishing-as-a-service (PhaaS) kit.

In its report, Microsoft said the UK Metropolitan Police Service’s cybercrime team “arrested two men on suspicion of offenses connected with the alleged operation of EvilTokens.”

The two men, whose identities were not disclosed, are aged 32 and 38, and have been released on bail, subject to conditions while the investigation continues. Their digital services and other items have been confiscated, as well.

Among the partners are Health-ISAC, Cloudflare, Coinbase, OpenAI, Railway, SpyCloud, The Shadowserver Foundation, and TRM Labs. We don’t know if these arrests and takedowns will be enough to completely obliterate EvilTokens, or if the platform will continue to operate. Usually, criminal infrastructure is a lot less resilient to disruptions when arrests are made, compared to when law enforcement simply disables the hardware.

The tech startup of organized crime

EvilTokens has been turning heads for a little while now. The platform was first spotted in February 2026, rising quickly to become one of the most widely used PhaaS solutions out there.

It can be bought through Telegram for $1,500, after which there is a recurring $500 subscription cost. Cybercriminals use it to run large-scale, personalized phishing attacks: they can create spoofed websites, landing pages, and other credential-capture assets; they can create custom-tailored phishing emails, and can even grab session tokens, one-time passwords, and other codes designed to protect accounts against phishing, granting attackers access to people’s inboxes.

But what makes EvilTokens particularly impressive is its use of artificial intelligence. The platform comes with an AI assistant that can sift through the inboxes, suggest which targets are of high value, and even how to approach them. Attackers can conduct Microsoft Graph reconnaissance as well, mapping out organizational structure and permissions, keeping access and moving laterally throughout the target network.

Microsoft said it found evidence of large portions of EvilTokens being vibe coded, “with AI helping its creators build the platform itself.”

The researchers also found the platform drawing on capabilities from multiple AI models. Looking at the platform as a whole, it runs like a well-organized startup, with subscription pricing, customer support, management dashboards, and tools designed to move customers from account access toward financial exploitation.

According to Microsoft, EvilTokens facilitated business email compromise (BEC) campaigns that compromised more than 12,000 inboxes in more than 10,000 organizations worldwide. Victims are mostly in wholesale distribution, construction, and financial services, but those in real estate, higher education, and healthcare are not spared, either.

The victims are primarily located in the United States, with notable numbers found in Canada, the United Kingdom, Australia, India, and France. Microsoft said affected customers were notified, and that the company “helped remediate compromised accounts and shared intelligence to support further defensive and investigative action."

Popularizing device-code phishing

Device-code phishing as an attack technique is not that new. More than a year ago, in February 2025, security researchers Huntress reported on Russian threat actors Storm-2372 deploying the same technique, and while it’s been steadily growing in popularity, it wasn’t until EvilTokens’ appearance that it really exploded.

The same researchers said, in June 2026, that EvilTokens was used to run 1,380% more device-code phishing attacks in 2026, compared to the same period last year.

“We’re seeing a clear maturation of the phishing-as-a-service (PhaaS) market as threat actors increasingly integrate AI workflows into their product offerings,” Huntress said in a report.

“The result is directly observable in our telemetry: a 1,380% increase in device code phishing attacks detected between July–December 2025 and January–April 2026, with over 50% of those incidents linked to two major waves of correlated incidents.”

We've had some excellent experiences running the Corsair Vengeance in our builds. And while worldwide RAM prices may be soaring, the company is currently running a big sale on DDR4 and DDR5 memory, including a discount on this stylish white Corsair Vengeance 32GB DDR5 kit.

• Best for: PC builders and creators looking for faster RAM for productivity, extensive multi-tasking, and creative workloads.

• The deal: The white Corsair Vengeance 32GB DDR5-5200 RAM kit is now $408 (was $508) at Corsair. In the UK, Corsair's memory kit is now £389 (was £485) on the site.

• Why it matters: Any discount on memory is welcome right now, since AI has sent prices skyrocketing. And we're fans of Corsair's Vengeance line, having tested several in our time. The white finish on this RAM kit is a decent attraction if you're building a color-coordinated desktop with a window in the case.

Today's Corsair DDR5 RAM kit deal

Why we recommend it

This Corsair Vengeance kit includes two 16GB DDR5 modules. Running at up to 5,200MT/s with CL40 latency, the modules feature aluminum heat spreaders to manage temperatures. Their compact, low-profile construction makes them suitable for desktop builds where larger memory modules could interfere with CPU coolers. Corsair has optimized the kit for Intel motherboards, with compatibility covering Intel 600, 700 and 800 series platforms.

Price context & historical value

The discount from $508 to $407.99 knocks just over $100 off the current price, equivalent to saving almost 20%. The wider memory market has experienced huge price increases during 2026, so any sizable discount is worth considering, even if it's still very expensive compared to last year.

Should you buy it?

✅ Buy the 32GB Corsair Vengeance White DDR5 kit if...

You accept that pricing is through the roof and modern deals don't compare with last year's costs. This is a good offer if want more control over your desktop's memory configuration and are comfortable tweaking performance settings. The kit supports Intel XMP profiles, allowing compatible systems to apply its rated memory settings without manually entering every timing and voltage parameter.

Built-in voltage regulation provide additional control for anyone looking to fine-tune their systems.

❌ Skip the 32GB Corsair Vengeance White DDR5 if...

You're assembling a budget desktop or upgrading an older system that doesn't support DDR5. Given current prices, it's worth checking whether your existing memory genuinely limits everyday performance before spending more than $400 on an upgrade.

More deals: Also consider

• US: Shop all memory on sale at Corsair
• UK: See all RAM deals at Corsair

• Burger King Russia’s 2024 breach via Mindbox exposed 3.2 million customer records, now leaked online
• Data includes emails, names, genders, birth dates, phone numbers, and geolocations (2018–2024)
• Payment details weren’t compromised; users warned of phishing and identity theft risks

Back in 2024, the Russian arm of Burger King suffered a data breach at the hands of unknown threat actors - now, that data has finally been leaked online.

In October 2024, Burger King told TASS, Russia’s national news agency, that unidentified hackers attacked Mindbox, a domestic marketing automation platform the company had been using.

Through Mindbox, the crooks managed to obtain sensitive company data, including information belonging to the customers.

As a customer data and marketing automation platform, Mindbox helps businesses gather and use customer information for personalized, omnichannel marketing campaigns. Its tools cover email and SMS campaigns, push notifications, loyalty programs, chatbots, and more. According to the company, more than 1,100 businesses use its platform, including L’Oréal, Panasonic, KFC, JBL and United Colors of Benetton.

One victim in a supply-chain attack

At the time, there was no word on the nature of the information that was taken, apart from the fact that payment information was not compromised.

"Among the victims of the attack may also be the data of customers of the Burger King restaurant chain," the company said at the time.

“Burger King confirms that among the personal data, the accuracy of which is being clarified, there is no information about payment details: open information about transactions is not transmitted or stored by third parties.”

The details about the hack were also not disclosed. We don’t know if the platform contained a zero-day, or if a company employee had their login credentials or session tokens exposed. Third-party supply chain attacks such as this one are common and often rather disruptive, affecting numerous companies using the same tools. For Mindbox, however, there have been no reports of additional victims.

In its 2024 results announcement, Mindbox said the attack was its “first serious information security incident”, which was quickly detected and contained “thanks to threat detection tools.”

In the aftermath of the breach, Mindbox said it “found and eliminated points where employees without access rights to sensitive data could indirectly obtain them,” hinting that the attack was, in fact, an identity-based attack rather than a zero-day exploit.

The company also “changed development processes to find such points before they get into the product,” and reformed Mindbox's internal role system to make permissions stricter and more granular. It also limited project access scenarios, introduced a mechanism for confirming access by another employee, and introduced mandatory two-factor authentication, among other things.

Have you been pwned?

Today, more details were released on Have I Been Pwned?, a website that aggregates information stolen in various hacks and helps people learn if their email addresses and other information had been compromised in the past. According to the newest entry, more than three million people have had their data exposed in this incident:

“The breach exposed 3.2M unique email addresses along with names, genders, dates of birth, phone numbers and approximate geolocations, with the data spanning 2018 to August 2024,” Have I Been Pwned? writes. “Burger King Russia acknowledged the incident and advised it did not include payment or passport details.”

The latest findings seem to be somewhat in line with what the media reported at the time. According to The Register, initial reports claimed around 5.6 million lines of data as exposed, which included information about a customer’s favorite dish and previous order dates. While this information was not mentioned in the newest report, if every data line includes one email, one name, or one phone number, it could amount to around 5.6 million.

While the information might be a few years old, things like names and birth dates, and genders rarely change, but are vital in identity theft, social engineering, and similar attacks. Burger King users, especially those in Russia, should be wary of incoming email messages, particularly those claiming to come from the fast food chain.

The Geekom A5 2027 Edition combines an eight-core AMD processor, upgradable memory and extensive connectivity, and our exclusive code cuts the price significantly.

Best for: Remote workers, programmers and home office professionals looking for an expandable desktop computer that occupies very little space.

The deal: The Geekom A5 2027 Edition mini PC is $495 (was $549) at Amazon.com and £423 (was £469) at Amazon.co.uk when you use our exclusive code TRA527FF

Why it matters: The Geekom A5 has consistently proven itself as a solid mini PC for work in our tests, and the 2027 Edition is no different. The eight-core Ryzen 7 processor, expandable memory and multiple internal storage options provide a versatile foundation for everyday computing. We've selected the 16GB/512GB configuration as it's more versatile, but a 16GB/256GB model is available with our promo code, too.

• You can also save on the A5 2027 Edition direct from Geekom using the same code, where it's $478 (was $549) in the US and £426 (was £489) in the UK.

Why we recommend it

In his 4-star review of this mini PC, our expert Mark said it was "good value, well-made, and a decent user experience." He added that it was "a machine that runs cooler and quieter, backed by a new IceBlast 3.0 cooling system that Geekom claims cuts noise to as low as 28dB at idle."

He also noted that "Geekom is now pitching the A5 2027 Edition for 24/7 office and commercial use, rather than as a budget performance desktop."

The Geekom A5 is powered by AMD's Ryzen 7 7730U processor, which has eight cores, 16 threads and a maximum boost clock of 4.5GHz.

Its integrated Radeon Vega 8 graphics are suitable for most tasks and the supplied 16GB of DDR4-3200 RAM can be expanded to 64GB.

Storage options include an M.2 SSD slot, an additional M.2 2242 slot and a 2.5-inch drive bay.

The A5 supports up to four external displays through its two HDMI and two USB-C connections, with support for resolutions up to 8K. Connectivity includes Wi-Fi 6, Bluetooth 5.4, 2.5Gb Ethernet and an SD card reader.

Price context & historical value

Our exclusive TechRadar code (TRA527FF) knocks 10% off the price at Amazon, and 13% buying direct from Geekom, making it the cheapest we've seen for the Geekom A5 2027 Edition.

In the US, you can save more by opting for the model with 256GB storage if you're happy to upgrade the SSD yourself. But oddly, this configuration is £30 more expensive at Amazon.co.uk, so I recommend grabbing the 512GB option if you're in the UK.

US: Amazon | Geekom

UK: Amazon | Geekom

Should you buy it?

✅ Buy the Geekom A5 2027 Edition if...

You want a compact computer that can drive a substantial multi-monitor workstation without occupying much desk space.

❌ Skip the Geekom A5 2027 Edition if...

Your work involves demanding 3D rendering, advanced video production or other applications that benefit substantially from dedicated graphics hardware.

• Shop all mini PCs at Amazon

• ShinyHunters defaces FBI’s jobs site, claiming a PeopleSoft zero‑day let them steal 2TB of HR data
• Group says attack is not for ransom but to dispute FBI’s May PSA alleging harassment and swatting tactics
• Experts warn exploit itself is highly valuable; FBI site reclaimed, investigation ongoing into breach claims

The ShinyHunters extortion group is currently doing brand management in the most ShinyHunters way possible - by hacking into the FBI and stealing the agency’s sensitive files.

The Bureau’s jobs site was defaced and replaced with the usual ShinyHunters content - an ASCII image of the group’s logo, and a message saying “This site has been seized by ShinyHunters. Rooting your systems since ‘19 :)”.

But instead of putting the FBI on its data leak site and threatening to release stolen files if a ransom isn’t paid, ShinyHunters started speaking to the press, telling The Register it found a zero-day vulnerability in the Oracle PeopleSoft human resource management system, which allowed them to remotely execute arbitrary code on the underlying server.

They used this ability to (allegedly) steal more than 2TB of sensitive data from the FBI’s servers, including names, addresses, phone numbers, and information on spouses for current, former, and prospective FBI employees.

“We hold data on all FBI employees and applicants,” the spokesperson told The Register, noting they had compromised human resources, MedLink, and Criminal Justice Information Services.

Brand management

The FBI has yet to comment, and so do both Oracle and AWS, but what’s most peculiar about this incident is that it doesn’t seem to be financially motivated.

So far, everything ShinyHunters’ have been doing was for the money. They would break into a company, steal their files, and then pressure the victims into paying a ransom demand in exchange for deleting the stolen information. This time around, the group claims the goal of the attack is to force the FBI to change the record on how it operates.

“This is NOT financially motivated,” the group told The Register. “We want the FBI to correct or retract their statements they made, which included substantial false allegations.”

The statements were made in a security bulletin published on May 15 this year, right after the Canvas attack. In early May 2026 Instructure, the edtech giant behind the popular Canvas learning system, confirmed suffering a cyberattack and losing sensitive customer data. It was later disclosed that some of the world’s top universities, including Harvard, Oxford, and MIT, were among the victims.

The attack was so disruptive that Instructure’s CEO was called to testify in front of the US House Committee on Homeland Security a few weeks later.

On May 15, the FBI issued a public service announcement (PSA) saying ShinyHunters “commonly use harassment strategies” to exert pressure on victims, including “sending threatening text messages and phone calls to victims and their family members, and in some cases, swatting.”

Swatting means calling the police to report criminal activity so severe that the SWAT team is sent. This is usually done to live streamers as a practical, albeit life-threatening, joke.

“Threat actors may falsely claim to have sensitive or compromising information, including embarrassing photographs or videos of victims, which frequently do not exist. Following these pressure tactics, SH actors have sometimes posted exfiltrated data to various iterations of the SH data leak site on the Tor network,” the PSA concluded.

“I have been doing my very best to combat these allegations,” ShinyHunters told the media. “And this is the best way to do it.”

No money?

Not everyone is sold on the idea that ShinyHunters isn’t doing this for the money.

In a statement shared with TechRadar Pro, CTO of Suzu Labs, Denis Calderone, said the claims should be taken with a grain of salt: “I have a hard time believing terabytes of FBI personnel data just sit on a shelf. Foreign intelligence services would love to have it, and having the FBI on their resume makes every future extortion demand more believable, and if the PeopleSoft zero-day is real, the exploit may be worth more than the data. Meanwhile, agents and their spouses could have their home addresses posted publicly within a week if this threat is followed through.”

Calderone also stressed that instead of focusing on the incident, people should be paying more attention to the zero-day.

“If you run PeopleSoft, don't wait for a patch. Get it off the public internet wherever you can, put what has to stay public behind a WAF, and make sure admin components like the /PSEMHUB/ path in their screenshot aren't reachable from outside. Hunt for the June indicators and for SSH attempts against the psoft and oracle accounts. Then ask yourself what your applicant portal can reach. At the FBI, a website built for strangers to upload resumes allegedly led straight into GovCloud.”

The FBI has since reclaimed its website, which now says it is under maintenance.

• LinkedIn reveals more move to help users and business verify on the site
• Users will now be able to ask their network for references in verification
• Businesses can cut off those pretending to be employees

LinkedIn has revealed new methods to help expand verification on its site in the latest battle against fake accounts and AI slop.

The social media network is making a stand in order to ensure its platform focuses on what it says matters most - helping users build grow a business-focused network.

It's not just users either - the move will also mean that businesses will be able to better protect their brand reputation on the site thanks to new controls and tools.

Verification improvements

First up for users is a new way to ensure verification is as effective as it can be. LinkedIn users will now be able to ask people in their network to verify connections to those they have worked or studied with - and when the user receives enough confirmations, LinkedIn adds a verification badge to their Profile, with the option to opt out.

LinkedIn says this method helps add "peer-backed credibility and reassurance", and says it will also soon let members proactively ask their connections to verify their experiences, helping members build authenticity through their professional network.

For businesses themselves, looking to protect their brand from employee impersonation, LinkedIn will now let Company Page Admins, who have verified their Page, instantly remove known imposter accounts, meaning they can no longer appear in search results under their company name. The platform is also testing a pilot which will give companies the ability to require workplace verification for anyone claiming current employment at their organization to associate with their Page.

"As AI reshapes the workplace, trust has become our most valuable professional asset," Oscar Rodriguez, VP of Trust Product at LinkedIn wrote in a blog post announcing the news.

"LinkedIn has invested tens of millions of dollars in verification, helping to build a more trusted professional community where more than 115 million members have now verified their identity or workplace for free. Now, we’re continuing our expansion of verification across LinkedIn and integrating with new partners, giving members more ways to verify who they are and more signals to help you know who’s real and credible, both on and off LinkedIn."

The expansion isn't just limited to LinkedIn, as the site is also allowing verified members to show off their status on partner platforms too. Truecaller and Peerspot have joined existing partners including Adobe, giving users on those platforms extra peace of mind that the person messaging them really is who they say they are.

"Whether you’re a member verifying the credibility and experience of a former colleague, a business protecting its brand reputation, or a platform looking to integrate LinkedIn trust signals, you are helping build a more trusted network where professionals can connect with confidence," noted Rodriguez.

The abolition of the Department for Science, Innovation and Technology has created uncertainty about what happens next to Britain’s AI ambitions.

Kanishka Narayan’s promotion to Minister for AI gives the technology a dedicated voice at Cabinet, yet folding DSIT’s responsibilities into other departments raises questions about how the Government will coordinate the infrastructure, investment and public-sector adoption needed to support those ambitions.

Recent parliamentary scrutiny of critical public-sector AI contracts has highlighted growing pressure on institutions to strengthen control over AI operations and deployment continuity. Britain has focused on adoption speed; the next step is operational control in ongoing public-service AI use.

The case for sovereign AI is becoming stronger as a result. Britain needs more than access to capable models; it needs controlled operations for the services that depend on them, even when a provider, contract or political relationship changes.

Sovereignty goes further than where data sits

Sovereign AI is often reduced to data residency, with the assumption that keeping information inside Britain is enough to keep it under British control. Data location remains important, particularly in healthcare, defense and public administration, but it describes only one part of the relationship between an organization and the technology it uses.

A hospital could store every patient record in a British data center while relying on models and software that only the original provider can operate or modify. Where that provider is based remains relevant, but the more useful test of sovereignty is whether the hospital can understand the system, govern how it is used and continue running it if the commercial relationship changes.

An organization should know how its systems operate, determine which data a model can access and retain the ability to move a workload or introduce another model without rebuilding the entire service. Once that system becomes essential, it also needs enough internal expertise to keep it running rather than discovering that continuity depends on the decisions of one supplier.

Britain does not need to reproduce every layer of the stack, but it does need predictable control where public services run. Modern AI draws on international research, complex supply chains and open-source projects, and recreating them domestically would consume enormous resources without necessarily producing better technology.

As DSIT’s responsibilities move across Whitehall, the Government needs to decide which dependencies it can accept without threatening security, public trust or an essential service.

Capability and control can reinforce one another

Government AI plans sometimes present sovereignty as the slower alternative to using the largest commercial models, as though Britain must choose between world-class AI and control over its deployment. That assumes every organization needs the same frontier system, when the question is which model suits the work.

For many public workloads, a fit-for-purpose model in a controlled environment is more valuable than one large generic option, especially when governance obligations are strict.

A specialized model can be adapted to the organization's data, tested under the conditions in which it will operate and governed according to the consequences of getting a decision wrong. In those circumstances, greater control can make the system more useful as well as more independent.

Open and portable technology can support this approach by allowing organizations to run models across different infrastructure, examine how systems behave and replace components as better options emerge.

Openness does not guarantee sovereignty, since organizations still need secure infrastructure, skilled teams and clear governance, but it reduces the chance that an entire service becomes inseparable from one provider’s platform.

I have seen the AI market move quickly enough to make that flexibility increasingly valuable. The best model for a task today may be overtaken within months, while pricing, regulation and access can change almost as quickly.

An organization that builds around one proprietary service may struggle to benefit from that progress, whereas one that controls the environment in which its models operate can introduce better technology without starting again.

Government can turn sovereignty into capability

Britain already has many of the ingredients it needs, including strong universities, experienced researchers, growing AI companies and public investment in compute. The next step is to connect those assets with sustained demand in the parts of the public sector where sovereignty has the greatest practical value.

Government does not need to select one national champion or exclude international companies. It can create a market in which providers compete while public bodies retain control of the systems they buy.

Procurement rules for critical AI could require workloads to be portable, independently testable and capable of running within infrastructure controlled by the customer, making the ability to change course part of the original decision rather than a problem discovered at the end of a contract.

This would give hospitals, councils and government departments credible sovereign options while providing British AI companies with a route from pilots to long-term deployment. It would encourage partnerships in which knowledge and operational authority remain with the institution responsible for the service, even when private companies provide the technology and expertise.

The restructuring of DSIT should be treated as an opportunity to clarify Britain’s objective rather than reduce its ambition. A Minister for AI can bring different parts of government together, but the strategy should be judged by whether public institutions can use the best technology available while retaining meaningful control of the systems that matter most.

That is what will allow Britain’s AI ambitions to endure as departments, suppliers and technologies change.

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Have you been getting an unusual amount of spam calls recently? Automated voices asking you to add a number on WhatsApp? Don’t worry, it’s not just you.

The UK appears to be going through a sudden spike in spam calls and messages, but the reason why isn’t immediately clear. Luckily it seems many of them are following a similar script.

In the spirit of combatting the spam scourge, I’ve spoken to friends and colleagues about the types of messages they have been receiving to help you spot the legitimate calls from the illegitimate - and how spam filtering can help you screen calls before you answer.

Types of scam calls

From what I have gathered so far, there are a few different spam and scam call campaigns going around at the moment. Here’s what you might have heard:

• An automated voice saying “Please add me on WhatsApp”

By getting you to add the number on WhatsApp, these callers can then bypass spam filtering and allow the scam to continue. It also makes it much easier for scammers to pass on malicious files or links that are difficult to check before opening.

WhatsApp-based scams can sometimes be ‘task scams’ - where the recipient is offered a job testing services or apps for real money. These scams offer rewards that can be cashed out for completing tasks, but instead of banking your reward, they instead steal your financial information.

• Automated Phil

Automated Phil (who isn't always called Phil) will address you directly by name and ask you yes or no questions. In doing so, they will record your voice saying “yes” which they can then use to try to trick voice-recognition systems used by financial services and banks. When you receive a call such as this, never answer the question with “yes” and instead ask “who is calling?”

• Fake reps of companies offering introductory pricing/offers

These calls are usually people pretending to be representatives of mobile networks or internet service providers. They’ll ask if you’d like to hear about an offer, ask which network you are currently with, or tell you that someone has opened an account in your name. If you are expecting a call from your service provider, call back using the official number listed on their legitimate website instead.

• Calls from immigration regarding outstanding cases

A rather sinister scam call that is designed to create panic on the receiving end, hoping to trick the victim out of their senses to get a callback where the scam can continue.

In some scam calls, you may receive a voicemail and decide to call back, thinking it is a legitimate call from a friend or family member only to be greeted by a stranger who has no idea what is going on and is just as confused as you are.

The scammers could be using a legitimate Voice-over-IP (VoIP) service that allows an international call to pose as a number with the correct number format for where you live. This works by routing their call through the internet and delivering the call as a local number.

How did they get my number?

Hackers are largely indiscriminate when it comes to breaking into computer systems, and - unless it is a targeted attack - they often don’t know what company they have breached until they exfiltrate any data.

But some breaches are worse than others. Supply chain attacks may not necessarily hit the company that you gave your data to, but a different company that works with the victim company.

For example, a clothing brand will need to provide your email address or mobile number to a logistics company that handles delivery. That logistics company may handle deliveries for more than one clothing brand, so when hackers hit the logistics company they can steal all of the data from multiple brands.

But the recent rise in spam calls may not necessarily be caused by a new data breach.

In order to make some quick cash, some hackers will collect data from multiple separate breaches and organize them into one big file before offering it for sale. The data contained within this trove could be from months or years ago, but many of the email addresses and phone numbers will still be active and are a potential victim.

What can I do?

There are a few ways to stay safe when trying to navigate potential spam and scam calls.

If a caller claims to be from a service you use such as your internet or phone provider, always ask for their name and then call back the company using the official number listed on the website. If it was a legitimate call, you’ll be routed back through to them. If not, you’ll know for sure that it was a scam.

Your bank will never ask you to tell them your PIN or card reader codes over the phone, nor will they ask for your password or ask for money to be transferred to a new account that you haven’t set up. If you aren’t sure if it is a legitimate call, use the same technique as above to find out if it really is from your bank.

One key thing to keep in mind when getting a spam call: don't hang up.

If you receive a call that your phone marks as spam, let it ring until it stops. If you hang up before answering, it lets scammers know that the phone number is active and monitored, so they will likely call back or pass on your number to other scammers.

Alternatively, you can also set up call screening on most phones. Here is how to set up call screening on iPhone and Android:

How to set up call screening

How to set up call screening on iPhone:

Call screening is only available on iPhone 11 and above running iOS 26 or later.

• Go to your Settings App
• At the bottom, tap Apps
• Scroll down and tap on Phone
• Scroll down until you see 'Screen Unknown Callers’
• Turn on ‘Ask Reason for Calling’

This will mean that any incoming call from a number that isn’t saved to your contacts will be asked for more information before your phone rings. This information will be displayed as plain text so you can see if the call is legitimate before answering. Just beware that calls from legitimate numbers will also be sent through call screening.

How to set up call screening on Android:

• Open the Phone app
• Tap the three vertical dots in the top-right corner
• Tap Settings and then tap Spam and Call Screen or Call Screen
• Turn on 'Automatically screen calls'

On Android, you will be able to select which calls are screened; maximum protection will screen all unknown numbers; medium protection will screen calls marked as suspicious and will outright decline spam; basic protection will not screen any calls and will only decline known spam.

If you've encountered a new spam or scam campaign not included on this list, leave us a comment below to help others spot the calls before they become victims.

A good mechanical keyboard can make long working days more fun, especially if you spend hours writing, coding or working with spreadsheets. The Keychron V6 Max offers a full-size layout, extensive customization and several ways to connect your devices.

• Best for: Writers, programmers and office workers who spend a lot of time typing and want a customizable mechanical keyboard with a dedicated number pad for Windows or Mac.

• The deal: The Keychron V6 Max wireless customizable keyboard is now $110 (was $120) at Amazon. In the UK, the cheapest price for the V6 Max is £92 (was £115) at Argos.

• Why it matters: This is the best mechanical keyboard for professionals that we've reviewed - our hardware editor has been using this everyday since he reviewed it in June without complaint. Its gasket-mounted construction, sound-absorbing foam, replaceable Gateron Jupiter Red switches and durable PBT keycaps offer plenty of options for customizing the typing experience.

Why we recommend it

The V6 Max has a gasket-mounted design with sound-absorbing foam, helping reduce typing noise and providing a comfortable typing experience.

Its double-shot PBT keycaps are designed to resist wear and prevent the lettering from fading, and it comes with pre-lubed Gateron Jupiter Red switches that provide smooth keystrokes without the tactile bump found on some mechanical keyboards.

The hot-swappable PCB supports most three-pin and five-pin MX mechanical switches, making it possible to experiment with different typing characteristics. And there's a volume knob you can tap to instantly mute your computer. It's an especially nice touch.

In his five-star review, our expert Steve said, "if I could, I’d sit at my desk all day and all night just to feel that pleasing feedback, listening to the satisfying clackety-clack as my fingers depress the keys. It might just be the best office keyboard I’ve ever used."

He also added, "For me, it’s one of the best keyboards around, perfectly positioned for productivity-minded professionals, students, programmers — effectively anyone who’s going to be spending a lot of time at their desk. On that score, it delivers an awesome experience."

Price context & historical value

The reduction from $119.99 to $109.19 represents a relatively modest saving of $10.80, or approximately 9%. That's not a huge discount, but any price cut on such a great keyboard is worth grabbing. I've seen it cheaper in the past, but only as low as $95.99, so you're paying just $13.20 more than the previous low.

Should you buy it?

✅ Buy the Keychron V6 Max mechanical keyboard if...

You regularly switch between several computers or want a keyboard that works across different operating systems. The V6 Max supports 2.4GHz wireless, Bluetooth 5.1 and wired USB-C connections. Bluetooth allows you to pair up to three devices, while the 2.4GHz and wired modes support a 1,000Hz polling rate.

❌ Skip the Keychron V6 Max mechanical keyboard if...

You have limited desk space or regularly carry your keyboard between locations. Its 108-key layout includes a dedicated number pad, so it occupies considerably more room than a compact mechanical keyboard.

• In the US: Shop all keyboards at Amazon
• In the UK: See all PC keyboards at Argos

Financial crime has never stood still. In my nearly three decades in financial services, I have watched fraud move in lockstep with the technology built to stop it, and at times, even stay a step ahead of it​,​ given the speed fraudsters adopt new technology.

I have witnessed decades of AI innovation ​that raised​ the bar on fraud detection, yet criminals continue to test those boundaries. Fraud remains a top consumer concern, with over ​87.5 million American adults experiencing a scam or financial fraud each year. That’s roughly one in every three American adults.

The question for financial institutions isn't whether AI has a place in fraud prevention​,​ but whether the AI they've deployed is built for the threat landscape they're facing today – and the one that's coming.

Compute has finally caught up with mathematical vision

For decades, data scientists working on fraud prevention had theories they couldn't implement. The ideas were sound, but the computers at the time weren't powerful enough to get the math done. That constraint no longer exists.

Historically, most fraud detection has been carried out by building a profile summarizing a customer's typical behavior using sophisticated features, a neural network, and the customer’s current transaction to flag transactions that are suspicious. It's an approach constrained by the computational limitations of time.

Today, access to GPU and other high-performance compute is changing the art of the possible. Rather than analyzing a transaction in the context of a profile, GPUs allow us to implement entirely new algorithms that can evaluate a customer's extensive transaction history in real-time as the transaction happens.

The result is a significantly sharper, more accurate prediction and far fewer false alarms that can delay or stop legitimate purchases​,​ eroding customer trust.

Built for one job, not every job

Thanks to greater GPU availability and computational power, we're now seeing new opportunities to support fraud prevention with a sequence-modeling transformer – not a generic transformer, but one purpose-built for transaction analytics and financial crime.

With a purpose-built transformer architecture that’s trained exclusively on financial transaction data and engineered for a single focused task, financial services institutions can detect financial crime in real time using deep personalization and the context of the customer transaction history.

These are not overarching ‘do everything’ models. They are focused foundation models purpose-built to deliver auditable, high-performing, low-latency generative AI for the fight against financial crime. Each of these models specializes in distinct areas, such as account takeover, scams, mule detection, and first-party misuse.

Independent models focused on their specialty allow for a more complete, accurate, and transparent picture than any single model working alone. And this is just the start​,​ as the same methodology applies to risk decisions, hardship, collections, and any application where understanding our customers leads to better engagement, protection, and service.

The future is now

The fraud prevention capabilities that will define the next five years are being built right now. Enterprises investing in protecting customers from fraud – both now and in the future – understand that purpose-built models, use of specialized compute, and AI agents are the path to protecting their customers.

The math protecting consumers today was invented decades ago by AI scientists who saw the potential in algorithms even before the compute and infrastructure existed. I've spent much of my career continually chasing that goal to ensure that the industry is ready to bring the best algorithms when compute shows up.

Some of my proudest work came from refusing to settle and trust that technology will catch up with AI invention and math. Every patent, every model, every AI experiment has served the same purpose: making sure the industry is ready to lead with the best AI tools once compute catches up with scientific invention.

That work is happening right now. The only question is whether ​​​​​​financial institutions are​​ part of it or racing to catch up.

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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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You typed a few prompts into an AI website builder and had a full site minutes later. It looks promising at first glance, but closer observation reveals it probably looks a lot like every other business in your industry.

Most people reach for these tools because they're short on time, budget, or both. You want something live today, without sinking funds into a six-week build from a developer. But there’s a substantial gap between going live and a website that actually works for your business. That’s exactly what my AI website customization guide covers.

Where do AI-generated websites fall short?

AI builders are trained on patterns from thousands of existing sites, so they tend to reproduce the same layouts and headline structures. One review of AI builders found that training on top-performing sites means the tools "find and copy the optimal design," which is efficient but produces cookie-cutter results. If a competitor used the same tool, your sites may look closer to twins than rivals.

The code underneath often carries its own baggage. AI-generated pages tend to ship with bloated CSS and redundant scripts that hurt Core Web Vitals scores, according to multiple industry analyses. This costs you twice, in visitors who leave and in search rankings that suffer for it.

Security is another potential blind spot among AI-generated sites. A recent scan of 447 live websites found that AI-built sites averaged 2.1 high-severity vulnerabilities each, compared with 0.7 for human-built sites, largely because the tools skip protections nobody explicitly asked for, like security headers and rate limiting. AI systems are good at building features that work, but far less reliable at adding safeguards nobody prompted them to include.

Then there's the content itself. AI copy tends toward vague, interchangeable phrasing, since the model is guessing at your specifics rather than pulling from verified facts, which can mean misstated details about your products or services. That doesn't make AI builders useless: it just means the output is a first draft, not a finished product.

7 tips for customizing AI-generated websites

The good news is that most of these problems are fixable without hiring a full development team. You're not starting from scratch. You're editing a draft, which is a much faster process than building a site from a blank page.

The tips below cover the areas that need the most attention once an AI tool finishes its pass. Work through them in order, since sorting out navigation first tends to make the later steps easier to spot.

#1 Rebuild the menu around what customers search for

AI builders often generate menus based on generic templates rather than how your customers actually think about your business. Important services can end up buried two clicks deep. Labels often use internal jargon instead of the words customers actually search for.

Walk through your own site as if you were a first-time visitor looking for your most requested product or service. If it takes more than two clicks to find, restructure the menu around that task rather than around your org chart. Test it on mobile too, since AI builders often collapse menus into hamburger icons without checking how usable they actually are.

#2 Cut the AI's boilerplate phrases

Look out for filler lines like "we provide high-quality, all-in-one solutions tailored to your unique needs." That kind of phrasing shows up constantly in AI drafts because the model has no specific facts to work with, so it defaults to language that could describe any business.

Replace it with details only you would know, like how long you've operated and what results real customers have actually seen. Specific language builds the credibility that generic phrasing can't. Reading each page out loud helps too, since AI phrasing tends to reveal itself the moment you hear it spoken back to you.

#3 Fact-check every price, spec, and product claim

AI models can invent plausible-sounding specifications, pricing, or availability when they don't have accurate source data to draw from. That's a real risk on e-commerce or service pages, where a wrong price or feature claim turns into a customer complaint later.

Go line by line through every product description, price, and spec sheet the AI generated. Cross-check each one against your actual inventory or service list before the site goes live. Pay close attention to anything the tool invented outright, like sizing charts or shipping timelines it had no real data to draw from.

#4 Swap the stock photos for real ones

The photos an AI builder pulls in are usually generic stock images chosen for visual polish rather than relevance to your business. Visitors have seen the same handshake-and-laptop photos on dozens of other sites. That sameness undercuts the trust you're trying to build.

Swap in real photos of your team, workspace, or product wherever you can. Even a handful placed in key spots, like your homepage and about page, makes the rest of the site feel more credible. A phone camera and decent lighting usually beat another stock photo of strangers shaking hands.

#5 Test every booking, payment, and CRM connection

Booking systems, payment processors, and CRMs often don't connect cleanly to AI-generated code, since the builder isn't working within a platform's standard hooks and APIs. Forms can silently fail to submit, or analytics tags can stop firing without any visible error.

Test every integration yourself: submit a real form, run a test transaction, and confirm the data lands where it should. Don't assume a feature works just because it appears on the page. Repeat the test after any major edit, since a single code change can quietly break a connection that worked fine the day before.

#6 Fill in the title tags and schema that AI skipped

Search engines and AI-powered answer tools rely on structured data to understand what a page is actually about, and AI builders frequently skip or half-fill this layer. Missing title tags and thin meta descriptions make it harder for your pages to surface in search results or AI-generated summaries.

Check each page's title tag, meta description, and heading structure. Then add schema markup for the basics, like your business address and hours, since this is one of the highest-value fixes on this list. A free tool like Google's Rich Results Test can confirm the markup is actually valid before you move on.

#7 Turn on two-factor authentication and audit your forms

Most AI builders leave meaningful gaps in default security configuration, including weak or absent security headers and missing rate limiting on login attempts. Privacy policies often read like placeholder text too, rather than an accurate account of what data you actually collect. Wix's own guidance to users flags two-factor authentication as the single highest-value fix available.

Confirm your SSL certificate is active and enable two-factor authentication on your admin account. Then remove unused contributor access and audit every form to make sure your privacy policy matches what you're actually collecting. Most of these checks take just minutes and rarely need repeating.

Should you generate websites with AI?

AI website builders solve a real problem by getting a functional site online without months of development time or a large budget. For a single-page portfolio, a quick landing page, or a proof of concept, the convenience outweighs the trade-offs.

A quick freelancer profile or a temporary event page rarely needs the same scrutiny as a site people will use to make a purchase decision. That’s why you should match your review effort to what's actually riding on the page. The more money or personal data changes hands there, the more that first draft needs a second look.

Tradeoffs show up as the site grows in importance. Convenience upfront translates into review time later, since the quality of the final result tracks closely with how much of that review you put in. Without review, a site tends to read as generic, sometimes with serious accuracy or security problems a customer finds before you do.

I always advise treating the AI output as a first draft rather than a finished site. Put in work so the result stops looking like a slightly different version of every other AI-built site out there. You should also budget time to check facts and close the security gaps the AI skipped on its own.

• Palantir CEO Alex Karp says frontier AI labs may end up nationalized because of the potentially unlimited liability they carry
• When asked how those risks could be disclosed in an S-1 IPO filing, Karp challenged the premise itself, saying: “You’re assuming that there will be an S-1.”
• His argument centers around the assumption that large AI-related liabilities ultimately require government intervention at some level, even as OpenAI's CEO Sam Altman ruled out an IPO in 2026, citing AI safety concerns

Palantir CEO Alex Karp has cast doubt on whether the frontier AI labs will ever reach the public markets.

A widely shared post on X by Guillermo Flor summarized Karp as saying OpenAI will never IPO, that there may be no S-1 in play, and that nationalization is the only real exit because the liability exposure from frontier AI is so large that "no public market could potentially absorb it."

His comments brought renewed focus on OpenAI and Anthropic, both of which have recently pledged to prioritize AI safety and "pacing the frontier" of development but have both released new versions of their AI models this week (GPT-6 Sol, Luna, and Opus 5.5).

A case made by focusing on potential liabilities

The comments come from Karp's September 17 appearance on CNBC's Squawk on the Street. In a transcript of the segment published by RealClearPolitics, a host asks how the liability Karp has been describing would be written into an S-1, and what the risk factors would sound like.

Karp's answer was one that sent shockwaves in an industry already pricing in potentially two trillion-dollar IPOs in the near future: "You're assuming that there will be an S-1. Okay. Maybe there will be."

He argued that the only way to handle that liability is to ask the government to nationalize said frontier labs, adding that companies often lead you to a conclusion rather than stating what they want outright.

The truth may be more complex: OpenAI confirmed on June 8 that it had confidentially submitted a draft S-1 to the SEC, while noting that some of what it wants to do is "likely easier as a private company." Anthropic made its own confidential submission a week earlier.

Neither company, however, has issued a public S-1 registration statement, using existing regulations to keep its financials private for now. OpenAI also pushed back its own timetable before Karp's statements. In an interview published by Fortune on September 12, Sam Altman said, "right now would be an ill-advised moment to go public," confirming there would be no listing in 2026 and citing safety and alignment work.

Not entirely an honest AI broker

This is not a new position for Palantir's CEO; in June, Benzinga reported that he spent six months privately warning AI executives about nationalization, telling an interviewer, "The momentum is on the side of people who want to nationalize them."

He also has a commercial interest in the argument, as Palantir sells software built on the premise that enterprises keep their data in-house, and in an August CNBC interview, Karp argued that businesses should control their own models and lean less on the frontier labs' token-based business model. On September 17, per Stocktwits, he again pointed to open models as a way for companies to keep control of their data.

His position rests on the belief that frontier labs carry liability only the government can absorb, that their appetite for regulation partly aims to limit that exposure, and that investors expecting blockbuster listings may be misreading the risk. Whether the delayed OpenAI IPO eventually culminates in some form of nationalization, regulatory oversight, or both remains to be seen.

What is certain, however, is that both Anthropic and OpenAI need billions of dollars to keep training their models and meet compute commitments in the near future, and they might turn to a source that seemed unlikely to some in the industry less than half a year ago: the US federal government.

Over the past year, security teams on our platform cut the time it takes to fix a critical vulnerability by roughly 50%. In the same period, their backlog of unresolved critical vulnerabilities grew nearly 29-fold. These numbers describe the problem every security leader is about to inherit.

AI can now test software at a scale manual testing never reached. Models read code and search thousands of assets for familiar vulnerability patterns, surfacing exposures earlier and faster than any human team could.

For businesses trying to keep pace with an expanding attack surface, that reach is genuinely valuable. It is also exposing a weakness that has drawn far less attention: most organizations cannot validate, prioritize and remediate findings at anything close to the rate AI can produce them.

That imbalance is the whole game now. Investment in AI discovery tools, on its own, does not make an organization more secure. It makes it busier. Left unaddressed, it leaves security teams with larger backlogs and less attention paid to the flaws that actually put the business at risk.

This is the problem Continuous Threat Exposure Management (CTEM) exists to solve. CTEM gives organizations a continuous process for understanding their attack surface, finding weaknesses, proving which are genuinely exploitable, and directing remediation towards the exposures that carry the most business risk. Discovery is one input. The advantage comes from everything after it.

The gap between discovery and remediation is widening

Security leaders have long treated discovery as a capacity problem: test more assets, cover more code, catch weaknesses earlier. AI answers that question decisively. But finding a potential vulnerability is the start of the work, not the end of it.

Every finding still has to be confirmed as exploitable and judged for severity in the specific context where the technology runs. Then it has to reach the right engineering team, win out against existing priorities, get fixed, and be retested to prove the fix holds. AI compresses the first step and barely touches the rest.

That is why the two numbers I opened with can both be true. A rising count of findings can mean better coverage. Faster repairs can sit alongside a growing backlog when discovery accelerates faster than remediation and engineering capacity moves the other way. Neither figure means much alone. The only view that matters runs the entire route, from first detection to verified fix.

Validation is the choke point

AI has also driven down the cost of producing a convincing security report. Some of those reports point to real weaknesses. Others duplicate known findings, misread the target, or describe theoretical issues that carry little real risk. Everyone still has to be investigated. A report that takes seconds to generate can consume hours of an experienced analyst's time before it can be dismissed with confidence.

At enterprise scale, that is how urgent findings get buried. A well-evidenced vulnerability with a credible attack path lands in the same queue as hundreds of submissions that sound plausible and lead nowhere. Security teams have to separate the AI gold from the AI slop, decide which real findings matter most, and do it with engineering capacity that has not grown to match.

Program owners need clear standards for evidence to make that possible. Researchers should be expected to show the likely business impact and demonstrate how a vulnerability reproduces, with automated tooling used to raise the quality of that evidence rather than the volume of submissions.

A consistent track record of valid findings tells a program owner whose work deserves attention first. That record is worth more, not less, as submissions rise.

Business context still decides what matters

Technical severity is only part of what you need to know to decide what to fix first. AI can match a finding to known patterns and reason over the systems it can reach. What it rarely has is the full picture the business holds: which services generate revenue, where regulated data lives, which dependencies make downtime especially expensive, and what compensating controls already exist.

The harder problem is combination. Individual findings that look moderate on their own can form a serious attack path once someone understands how the systems interact, which permissions can be abused, and where controls break down across organizational boundaries.

That is human work. And it is better human work when it is diverse: one researcher goes deep on identity controls, another on API behavior, another on how minor weaknesses chain together across systems. That variety surfaces novel attack paths that automation, and even advanced cyber-models, miss.

Our own data shows what that work is worth. Researchers earned more than $47 million on the H1 Platform in the first half of this year, up more than 25% year on year. The strongest earners are the ones who can explain business impact and show exactly how a weakness can be used.

That is also where researchers add the most to a CTEM program: testing whether an exposure holds up under real conditions, and spotting the connections between weaknesses that look harmless in isolation.

That figure deserves an honest footnote. Aggregate earnings rising does not mean every researcher is winning. As AI absorbs the routine, high-frequency findings, the researchers who once made a living there feel the change first, while those who can chain weaknesses, reason about business logic and produce credible proof find their work worth more.

The job of any serious bug bounty platform is to make that a transition its community can move through, not a wall most of them hit.

That obligation runs both ways. If programs expect researchers to raise the quality of their evidence, researchers should expect fast and fair triage in return, real recourse when a valid report is wrongly dismissed, and a door that stays open to newcomers who have not yet built a reputation.

The best researchers increasingly use AI tools themselves, and the value of a report has never turned on whether a tool helped produce it. Protect the economics and the fairness that reward credible work, and the independent research community grows stronger as AI scales, not thinner.

Build for the volume AI creates

So, is bug bounty dead? Far from it. Assume discovery will only accelerate, and put the effort into everything that happens after a flaw is found.

More discovery puts more pressure on the point where reports are validated and handed to engineering. Without enough triage capacity, well-evidenced findings stall behind automated noise.

Without clear ownership, confirmed risks sit in the gap between security and development. Independent verification matters at the other end too, especially when an AI system proposes the fix and may carry the same blind spot into its judgement of whether that fix works.

Boards and executive teams need measures built on risk reduction, not activity. Finding counts are easy to report and can climb even as an organization gets safer. Confirmed exploitability, remediation speed, recurrence, and the size of the unresolved critical backlog tell the real story.

CTEM holds those activities together as a continuous process, keeping discovery, validation, prioritization and remediation connected as the attack surface changes. It gives security leaders an honest view of where they are gaining ground and where exposure is still building.

The next phase of AI security will not be won on discovery. Discovery is already abundant. It will be won on response: knowing which findings represent real exposure, and moving the most dangerous of them through to a verified fix. AI supplies the reach. Independent researchers supply the judgement and the adversarial creativity AI still lacks.

The organizations that build to convert both into action will pull ahead. The ones that do not will own a faster-growing list of vulnerabilities they never fixed.

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Who owns your data once it leaves your devices? Who bears the responsibility for keeping it safe from prying eyes? What happens when the vendor you trusted with years of sensitive personal or business information suddenly decides to close shop?

Legally, these questions have straightforward answers. But in practice, it’s much murkier ground.

That’s why more and more people are now opting to store their apps and data on servers that they “own,” at least to some degree. By spending $15 to $20 a month on a private server, you can self-host websites, apps, automated workflows, and even local AI models. You retain full admin access over your server, including any data that goes into or out of it.

How does this work? Follow my detailed guide for instructions on how to set it up.

Why self-host an app using Docker?

No one wants to pay subscription fees for tens of apps every month, or deal with spikes in token costs or usage fees with no announcement or ceremony. Worse still, we have officially reached the point where people can’t remember all the things they’re subscribed to, so credit cards get charged on autopilot before you can look at the bill and see what’s happening.

That’s not all. When you use a cloud-based app, your data lives on someone else's infrastructure, governed by someone else's terms of service. Self-hosting puts that data back on hardware you control, whether that's a spare machine at home or a rented server. You decide who has access and how long files are kept — plus you don’t have to worry about what happens if the vendor changes its privacy policy.

Businesses handling customer records, financial data, or health information often need to prove exactly where that data sits and who can reach it. For individuals, the risk is even more pronounced because any data breaches or cyberattacks have a direct impact on your life. A self-hosted setup gives you a direct answer to these problems instead of pointing you to a vendor's compliance page.

With Docker, it’s a lot more practical, too.

Each app runs inside its own container, a package that contains the code, runtime, libraries, and configuration settings. Nothing leaks between containers, so installing one app won't bring down another. You have complete control over your data and app environment, while your only ongoing expense is the price of a hosting plan.

How to self-host an app on your server, step-by-step

There's more than one route to a working self-hosted app. Some hosting providers give you full SSH access so you can install Docker apps using the command line. Others offer a graphical file manager or a library of ready-made templates that do most of the setup for you.

We'll walk through all these approaches below. Each assumes you already have access to a functional Linux server, workstation, or NAS. If you don’t have that set up yet, you can buy a hosting plan from any reputable web host. In fact, you can also start your own local server using a Linux computer or even a Raspberry Pi.

Where to find and pick Docker apps

If you’re wondering which apps you can self-host, Docker has an image library called Docker Hub that contains ready-made installers with detailed instructions on how to set up different compatible apps on a self-hosted server.

Search the database for the app or functionality you need, locate the app you wish to install on your server, then follow along with the rest of the tutorial.

Using the command line with SSH

SSH gives you direct access to your server without any restrictions, which makes it the most flexible option if you're comfortable using a command line.

Start by connecting to your server from your local desktop. You’ll need your Linux server’s IP address for this, so make sure you note that down first. This can be found in your hosting provider’s SSH settings. After that, you just need to type in the command below using Terminal or Command Prompt:

ssh your-username@your-server-ip

Now, install Docker on your server using your Linux distro's package manager. On Ubuntu or Debian, you can use these commands:

sudo apt updatesudo apt install docker.io docker-compose-plugin -y

Next, create a folder for your Docker app and a Compose file to define it. Docker Compose lets you describe an app's containers, networks, and storage in a single YAML file rather than typing out long docker run commands by hand.

mkdir ~/myapp && cd ~/myappnano docker-compose.yml

A basic Compose file names the container image you want to run, maps a port on your server to a port inside the container, and sets any environment variables the app needs, such as a database password or an admin email. Most self-hosted apps publish an example Compose file in their documentation, which you can copy and adjust. You can usually find these directly on their Docker Hub listings.

But if you’re not sure how this looks, here’s an example compose.yml file for installing NGINX:

services:  web:    # Pulls the official Nginx image from Docker Hub    image: nginx:latest    ports:      - "8080:80"  database:    # Pulls the official Postgres image from Docker Hub    image: postgres:15    environment:      POSTGRES_PASSWORD: your-secure-password

You’ll need to replace “nginx:latest” with the image name of the Docker app you’re trying to install. I’d also recommend creating a more secure PostgreSQL password to replace “your-secure-password.”

Once you save the Docker Compose file, you can bring the app online with:

docker compose up -d

The -d flag runs the containers in the background so they keep running after you close your terminal session. Now when you point your web browser to your server's IP address and the port you mapped, your app should load.

Using a control panel with native Docker support

If you'd rather avoid the terminal, some hosting provider control panels give you a visual way to manage containers directly.

cPanel is the most widely used panel, but it doesn't include native Docker management, so you’re stuck using SSH only. However, Plesk has an official Docker extension that lets you browse images from Docker Hub, deploy containers, and manage them from a dedicated section in the sidebar without touching a command line interface. CyberPanel, another free and open-source control panel built on OpenLiteSpeed, also ships with Docker management built in as an alternative to SSH setup.

Then you just search the image catalog or paste in your own YAML file, review the resource and port settings the app requests, and confirm the deployment. The panel pulls the image, starts the container, and usually shows you live logs if something fails.

Once the app is running, the same panel typically handles DNS records and SSL certificates too. That's a decent accessibility advantage over pure SSH, since you don't need to deal with stuff like configuring a reverse proxy or certificate renewal by hand.

Using pre-coded templates offered by web hosts

Still, the fastest route to a running app is a hosting provider's marketplace of pre-built templates. These exist specifically so you don't have to write a Compose file from scratch.

Log in to your hosting provider's dashboard and look for a marketplace, app catalog, or "one-click apps" section. Search for the app you want to run. Popular self-hosted tools like automation platforms, note-taking apps, and file-sync services are commonly available this way.

Selecting a template usually opens a short setup form. You'll pick your server size, choose a data center region close to your users, and set any required credentials, like an admin password. Confirm the order, and the provider provisions a server with Docker and your chosen app already configured and running.

This method trades some flexibility for speed. You won't have as much control over container configuration, and templates can lag behind an app's latest release. But for popular tools, it's usually the quickest way to go from nothing to a working instance you can start customizing.

Are there any caveats to self-hosting?

Self-hosting shifts responsibility from a vendor's support team to you. Security patches, software updates, and backups no longer happen automatically in the background. If you forget to update a container image for months, you could be running software with a known vulnerability, and nobody will send you a reminder before your server gets compromised.

Uptime becomes your problem too. A managed SaaS product usually has a dedicated team monitoring its infrastructure around the clock. A self-hosted app on a single server has no such safety net unless you set one up yourself, whether that's monitoring software, automated backups, or a failover server in case there’s a crash.

There's also a real-time cost that's easy to underestimate. Reading documentation, troubleshooting containers, keeping track of which ports and volumes belong to which app all add up — all these things cost time and effort. For a single low-stakes tool, that's a minor inconvenience. For a business running several self-hosted apps, it can turn into a part-time job unless you budget for it properly.

Is self-hosting actually cheaper?

On paper, the numbers look compelling. A small virtual private server capable of running several lightweight self-hosted apps typically costs somewhere between $5 and $20 a month, depending on the provider and specs you choose. Compare that to a single SaaS subscription that can easily run $20 to $60 a month per tool, and the gap widens fast once you're replacing multiple subscriptions with one server.

The savings tend to grow with scale, up to a point. Running two or three self-hosted apps on the same VPS costs roughly the same as running one, since you're paying for the server rather than per app. That changes the math significantly compared with SaaS pricing, where every additional tool usually means an additional bill.

But cheaper isn't the same as free. A server with no backup strategy or monitoring in place isn't really saving you money if it goes down and you lose data or spend a weekend fixing it. Factor in the hours you'll spend on updates and troubleshooting, especially early on, and self-hosting looks less like a free lunch and more like a trade. You get lower cash costs in exchange for taking on the operational work a SaaS vendor would normally handle for you.

Executives are spending a great deal of time asking whether AI agents are ready for production, but this is the wrong place to start, because agents aren’t acting in a vacuum. The quality of their decisions depends heavily on the quality of the data they use.

Model security and performance matter, of course, but they cannot compensate for weak control over the information the agent can retrieve. Before putting an agent into production, an organization needs governed inputs, appropriate access and a reliable record of what the agent produced.

Give an agent outdated customer information, duplicate records or material that was never properly classified, and it will work with what it has, making poorly informed decisions quickly across thousands of transactions.

Unfortunately, the gap between adoption and governance is widening fast. In the rush to adopt AI, many organizations are pushing agents into production before they have solved the underlying data problem, so it shouldn’t come as a surprise that the vast majority of AI projects show zero ROI.

What’s more, AI agent governance doesn’t end with the data an agent may access. It also encompasses the data an agent produces, such as its decisions, the instructions it was given and documents it created. As AI contributes to significant decisions within the enterprise, AI traceability and defensibility have become critical capabilities.

Organizations that build a strong data foundation put themselves in a stronger position to move from pilot to production with confidence that agents won’t transform poorly managed data into a company-wide crisis.

The data problem comes first

The data governance issue is not, of course, a new problem. Most large organizations have spent years trying to wrangle information spread across operational platforms, file shares, archives and applications that should have been retired long ago. But because agentic AI can rapidly act on those weaknesses at scale, the issue has become more pressing now.

Before an agent is given access to data, IT needs to identify sensitive and regulated information, apply classification and retention policies consistently, and determine which sources are current and reliable enough for the job. Legacy data deserves particular care.

Retired applications often contain valuable business history, but they also hold duplicate records, obsolete information and data that’s subject to legal holds or retention requirements.

Organizations should also preserve the context and relationships that give legacy data meaning, and then make selected, policy-controlled datasets available. AI does not act on data in a vacuum, and without the surrounding context, agents will either fail to complete their tasks or, worse, will make and act on faulty decisions.

Access is a business decision

Another mistake that enterprises make is to provide their agents with access to more data than they need. In fact, agents should also only have access to the data they require to complete the tasks they are assigned. Just because a system can be connected to an agent, that does not mean it should be.

Treat agents like employees, and follow the principle of least privilege. For example, while a customer service agent may need current account and transaction information, it most likely doesn’t need legal files or historic employee records. Access should narrowly follow the agent's defined purpose.

Provenance matters as well. An agent should not treat a current system of record and a decades-old archive as equally authoritative. Agents must know where information came from, when it was updated and which policies apply. Again, context matters. Without the proper context, agents will make costly mistakes.

Keep a record of what the agent did

Governance does not end once the agent receives approved data. AI-related data is rapidly becoming material to litigation, compliance reviews and customer complaints. To be prepared, an organization should be able to show what the agent was asked to do, which information it accessed, which policies were applied and what happened next.

Prompts, retrieved content, outputs and resulting decisions should be retained as business records. A reviewer should be able to reconstruct the AI’s decision and trace it back to both the source data and the person or function that authorized the activity.

Finally, accountability cannot belong to the technology team alone. Security, data, privacy, legal and compliance leaders all have a role, but a named business owner must remain responsible for the outcome. Automation can perform an action, but it cannot accept accountability for it.

Data readiness is not a secondary workstream to be addressed after an AI pilot succeeds. It is part of the decision to move that pilot into production. If an organization cannot trust the information, control access to it and clearly explain the resulting decisions, the agent is not ready for greater autonomy.

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• ESET finds nearly half (49%) of UK SMBs have experienced an incident in the past year
• Phishing, unpatched vulnerabilities, weak passwords, and a lack of monitoring often to blame
• AI-powered tools will prove a much bigger challenge for SMBs

British small businesses are being hit by more cyberattacks than ever before, and the increasing use of AI technology by hackers could put more of them at risk, experts have warned.

ESET’s 2026 SMB Cyber Risk Report found that nearly half (49%) of UK SMBs have experienced an incident in the past year, with 13% of those experiencing more than one incident.

Many of the 500 UK SMBs surveyed expressed fears that the situation is only going to get worse, as criminals employ new AI-assisted tools to launch more powerful attacks.

SMBs at risk

The top security concern for SMBs was AI-powered malware, with the most common causes of attack found to be phishing, unpatched vulnerabilities, weak passwords, and a lack of monitoring.

Security should be an important consideration for businesses of all sizes, but with so many plates to spin, can go neglected in some SMBs.

This isn't the only challenge they face, as the report also flagged issues such as a struggle to keep up with the latest cybersecurity threats, staying the pace with new technologies like AI, vulnerability and patch management, and a lack of skills in the workforce.

The findings are especially concerning as SMBs make up more than half of UK turnover in the UK private sector - around £2.8 trillion, and so are often popular targets for hackers.

It isn't all bad news though, as in order to help resolve security challenges, over half (55%) of UK SMBs said they expect to increase cybersecurity budgets over the next 12 months, with top investment priorities including employee training and awareness and cloud security.

“It’s true that AI will help cybercriminals to speed up their attacks against UK businesses. But the core tactics used like phishing or exploiting software vulnerabilities will largely stay the same,” explains Jake Moore, Global Cybersecurity Advisor at ESET.

“Until organisations can improve their security by locking down the basics, smaller businesses will be easy pickings for cybercriminals looking to steal sensitive data and turn a profit.”

• Data centers will get a label, just like your fridge or dishwasher
• Energy and water consumption must be reported by 500kW+ sites
• The EC also wants to know how resources are being balanced in relation to local strains

The European Commission has proposed a new rating scheme for data centers in a bid to have them report metrics like energy efficiency and water consumption more transparently.

Upcoming changes fall under the new Data Center Energy Efficiency Package, which introduces a new common, EU-wide rating scheme much like what we're already familiar with for household appliances.

Crucially, the changes now affect smaller campuses with a power demand of at least 500kW, whereas only much larger sites were impacted before the new rules.

European data centers must report efficiency statistics more openly

Some of the new metrics include total energy and water consumption, Power Usage Effectiveness (a figure we're already familiar with in hyperscalers' annual sustainability reports), Water Usage Effectiveness (WUE), how much of their energy comes from renewables, whether waste heat gets reused and more.

Ultimately, the intensified reporting is hoped not only to reveal how much energy sites are using, but how effectively they're balancing consumption in relation to local pressures like water stress.

"Tripling our data centre capacity cannot mean tripling the pressure on our grids, our water and our energy bills," Clean, Just and Competitive Transition EVP Teresa Ribera said.

The changes are also especially notable given ongoing changes within the bloc, because the EU has been ramping up more local compute options so as to reduce reliance on international options in a big sovereign drive. Over the next seven years, the EU wants to triple its data center capacity, per Reuters reporting.

Importantly, the changes won't happen overnight with the rating scheme subject to a two-month scrutiny window, the Commission detailed. Still, the first ratings are set to go live as early as this year, and by the end of 2028, the EU hopes to have reviewed just how effective those new enforcements were.

"Understanding their energy and resource consumption is the essential first step towards integrating them sustainably into our energy system," Energy and Housing Commissioner Dan Jørgensen added.

• Chinese universities are cutting traditional courses because of AI
• Translation courses face pressure as universities reassess their employment value
• Photography programmes are being merged as AI changes creative production

The Central Academy of Fine Arts has dropped courses in illustration, photography and translation, arguing that AI has made them poor value for money.

Fashion design degrees are also being scrapped, with universities saying the future rests on close cooperation between people and machines in creative work.

The changes follow a state campaign to direct learners toward chips, robotics and AI, industries the authorities regard as vital for national prospects.

Universities are cutting courses at scale

From 2021 to 2025, Chinese universities closed or paused 12,200 undergraduate programmes and launched roughly 10,200 fresh programmes.

The restructuring affected over 30% of undergraduate programmes nationwide, and arts, humanities and language courses absorbed the heaviest cuts.

A survey of 70 universities found cuts to five translation majors, eight Japanese majors and five German majors among language programmes.

Alex Shi launched her creative career with a degree that trained her to translate and interpret English, and says such courses could soon vanish, as the cuts overlook the wider skills underlying translation, including communication and critical thinking, which extend beyond producing accurate text.

Shi concedes that AI could bring translation accuracy almost to perfection, yet maintains that interpreting demands far more than converting words between languages.

“There’s still that personal style that’s always been essential in translation,” said Shi.

The Communication University of China has reportedly closed five programmes, among them visual communication design, comics and photography, according to a report on its overhaul.

“Traditional photography major can no longer exist as an independent discipline… today everyone can be a self-media creator and recorder,” said Liao Xiangzhong, top official at the Communication University of China.

Liao later clarified that the majors were merged into other disciplines instead of being cancelled outright, with photography absorbed by film and television photography.

New film roles and hybrid courses

New roles such as prompt engineer and AI animator are emerging in film and animation, where AI is also creating fresh openings alongside the closures.

One industry worker says designing characters or scenes once took a month, but with AI, a complete project can now be delivered in that time.

Degree programmes in intelligent imaging art, intelligent audiovisual engineering, and intelligent engineering and creative design have been launched by some schools.

Universities are also launching hybrid courses in which artistic instruction sits beside AI training, while some standalone degrees are being scrapped across the sector.

That shift is changing what employers expect from graduates entering translation, design, photography and film production.

For students, traditional qualifications may increasingly need technical skills that allow them to work alongside automated creative systems.

The result is a university system where some familiar degrees disappear while new AI-focused programmes take their place.

Via Al Jazeera English