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The AI-powered virtual cell tailors treatments for breast cancer based on samples of each patient’s tumor.

Tailoring cancer treatments is a science and an art.

The same type of tumor can behave very differently from one person to the next, and a drug that works for one patient may fail in another. The uncertainty stacks up when multiple drugs enter the mix. Trial and error is often unavoidable. Meanwhile, cancers keep growing and compounding side effects can plague already beleaguered bodies.

Researchers have long sought to speed up the process of tailoring treatments to patients, and AI might lend a hand. This month, a Chinese team developed an AI-based virtual cell for triple-negative breast cancer—a challenging form of the disease that often evades standard treatments—to predict how individuals will respond to different drugs.

Rather than reconstructing every detail of a cell’s inner workings, the virtual cell focused on just proteins. Trained on a massive, curated dataset tracking protein changes before and after drug treatments, the model outperformed existing drug-tailoring approaches and discovered new combinations that could work even better.

The underlying AI, called ProteinTalks, was also readily adapted to predicting drug responses in other cancers, hinting at a broader reach beyond breast cancer.

That’s not to say the virtual cell is ready for prime time. Researchers tested its predictions in patient-derived cells in lab dishes, and the model can only evaluate two-drug combinations. Whether its recommendations translate into meaningful benefits must be tested in patients.

But the results offer a proof of concept: Virtual cells, even imperfect mimics of their biological counterparts, could one day help physicians find more effective treatments from the get-go.

“This is the first time that a virtual cell model goes out of the laboratory and is tested in a clinical scenario,” study author Tiannan Guo at Westlake University in Hangzhou, China told Nature.

Digital Twins

Every cell is a buzzing city. Proteins zip around a crowded interior, briefly grabbing onto one another to direct cell functions. Fatty molecules maintain the protective outer membrane, while mRNA carries genetic instructions to protein-making factories. All these workers relay feedback to the cell’s control center—the DNA-harboring nucleus—where these signals help switch genes on or off and keep the cell humming.

Recreating this complexity in digital form might sound like a fever dream. But AI is turning it into a scientific race. Unlike finicky biological cells, their virtual counterparts could slash the time and labor needed to run experiments, allowing researchers to test myriad ideas at breakneck speed.

Academia and industry are already chasing this goal.

In an interview, Google DeepMind co-founder Demis Hassabis said the team is developing an AI-powered virtual nucleus, which offers a relatively self-contained starting point from which to build a whole virtual cell. The Chan Zuckerberg Initiative is partnering with Nvidia to develop tools and AI models that would help run and evaluate virtual cells. Meanwhile, the Science for Life Laboratory received funding for its ambitious AlphaCell program, which aims to create AI models that predict how cells work and adapt in health and disease.

Earlier efforts to build virtual cells relied on transcriptomics—that is, a snapshot of gene activity—across single cells. But these measurements don’t necessarily reflect what a protein is doing at any given time and can miss changes.

The new study takes a different route, cutting out the middleman. Instead of inferring protein activity from which genes are active at any given moment, the team trained their AI model directly on the proteins themselves and used the model to power a new type of virtual cell.

The Protein Whisperer

A long-standing roadblock for protein-based AI models is the lack of comprehensive datasets.

To tackle the problem, the team treated 18 immortalized breast cancer cell types—16 of them triple-negative—with 63 FDA-approved anticancer drugs and 59 common drug combinations. They then measured thousands of proteins at four timepoints: before treatment and at 6, 24, and 48 hours afterwards. Altogether, the experiments generated more than 38 million protein measurements, along with cell-survival data, now available in an open-source database.

It’s “one of the largest…resources reported to date,” wrote the team.

ProteinTalks, the AI virtual cell trained on this dataset, could deal with several aspects of cancer treatment.

First, it found over 800 proteins whose levels changed after each drug treatment and zeroed in on a rapidly shifting subset. These could “act as sentinels” of an early drug response, the authors wrote. Most behaved as expected. Some drugs disrupted the cell’s structural scaffolding; others interfered with DNA repair or growth, ultimately causing cells to wither.

Over time, tumors can evade treatments, resulting in their return or spread. The model flagged several protein suspects likely involved in this process. These might serve as signals of resistance or drug targets for tackling it.

The AI could also generalize. When challenged with 81 drugs it hadn’t seen during training, ProteinTalks predicted protein changes with 88 percent accuracy, outperforming several previous models.

The team then trained it on more than 900 drug mixes to see whether it could help identify promising pairs. The virtual cell gave higher scores to combinations that had already been validated experimentally and used in the clinic. This “sanity check” suggests the AI isn’t simply hallucinating results but could generate valuable insights.

Finally, the team asked whether the model could help prioritize treatments for individual patients. They screened 3,000 approved, clinical-stage molecules using proteomics data from three people with the disease. The model identified regimes that matched treatments that had kept the disease at bay—and suggested three additional molecules that could be even more effective. The predictions worked out. When tested in cancer cell samples from patients, the drugs inhibited growth at lower doses than standard therapies.

Although trained on breast cancer, ProteinTalks could also pivot to other tumor types when fed cancer-specific proteomics data. In lab-grown melanoma, colorectal, lung, and pancreatic cancer cells, it found more than 5,100 protein changes, including subsets unique to each cancer type ready for further analysis.

As with other virtual cells, ProteinTalks is still a prototype. Given the hope, and hype, surrounding these models, the team emphasizes that its predictions will need to be tested in animal models and, eventually, clinical trials. Its suggestions could point the way toward better treatments for stubborn cancers, or they could turn out to be AI flights of fancy—drug combinations that look promising on paper but make little biological sense.

There’s another limitation. ProteinTalks doesn’t account for protein interactions, either with one another or with DNA and other biomolecules. Drugs could disrupt these temporary biological “handshakes,” potentially triggering effects that ripple through the cell.

Combining ProteinTalks with AI based on gene activity could add another layer of information and spruce up its predictions. The virtual cell is still a long way from a true digital twin, but piece by piece, the dream is getting closer.

The post A Digital Cell Predicts Which Drugs Will Be Most Effective in Deadly Breast Cancer appeared first on SingularityHub.

Security researchers affiliated with Austria's Graz University of Technology have found flaws in the implementation of file notification systems on Android, Linux, macOS, and Windows that leak potentially compromising system information. "We found decades-old bugs on [these operating systems], all rooted in the file-notification subsystems that every modern OS ships to inform applications when files change," said Sudheendra Raghav Neela, a doctoral student at TU Graz, in an email to The Register. Affected systems include inotify on Linux since 2005, FileObserver on Android since 2008, ReadDirectoryChangesW on Windows since 2000, and FSEvents on macOS since 2007. These file notification subsystems tell the operating system when a file has been opened, changed, written, or deleted. They don't reveal file contents. But file event information functions as a side channel that allows a malicious user to infer the activities of other users of the computer. Armed with file event data, an attacker may be able to conduct inter-keystroke-timing attacks that reveal user input (not only locally but remotely over SSH), website fingerprinting attacks that reveal website visits, and UI redress attacks that allow credential theft. Neela and colleagues Xufan Zhao, Jeanette Angelika Wultsch, Hannes Weissteiner, Florian Draschbacher, Stefan Gast, and Daniel Gruss describe their findings in a paper titled "File Notification Attacks: Templating and Exploiting Side-Channel Leakage from the File-Notification Systems on Linux, Windows, and macOS." The paper, summarized at inoti.fyi, says the basic problem is that unprivileged users can access the file notification subsystem and that on Linux and Windows file information is available even without read access. The attack scenarios mostly involve a local attacker who has access to an account with files that can be read by multiple users. The researchers however note that the list of globally readable files is extensive. Neela said, "On Linux, watching a readable directory leaks events on files inside it you cannot even read: watching /dev/input gives a notification on every keystroke, which we turn into a local inter-keystroke timing attack with a 93.1–100 percent [keystroke accuracy] score across seven users and a remote (SSH) one at 100 percent." He added that the group also carried out an authentication-prompt redress attack on KDE Plasma 6 under Wayland, and website fingerprinting on the top 100 sites at 87.9 percent. The Linux vulnerability (CVE-2025-68788) was partially fixed in December 2025 in kernels 5.10.248, 5.15.198, 6.1.160, 6.6.120, 6.12.64, and 6.18.3. The patch prevents the generation of "access" and "modify" events on special files in /dev/. "On Android, FileObserver goes past the FUSE layer meant to isolate per app storage, so a permissionless app can watch (for example) WhatsApp's private folder and see, by filename and timestamp, exactly when photos, videos, and documents are sent, received, or deleted," said Neela. Though the researchers claim that they responsibly disclosed their findings to security teams for Linux, Android, Windows, and macOS between August and October 2025, no mitigation appears to have been made for Android devices. Apple's macOS provided the least information because no bypasses were found for reading private directories. But FSEvents still allowed the monitoring of various file changes that show up in .plist files. These include audio input and output changes, power settings changes, Bluetooth device and printer updates, network cable-initiated DNS changes, and volume mount/unmount events. Application installations and removals can also be observed. "On Windows, watching the root directory C:\ reports the full path of every file touched anywhere on the system, across all users, regardless of permissions, enough to track which websites on Firefox another user visits in real time at a 97.8 percent [accuracy] score," said Neela. "Microsoft told us this is 'by-design' and that it's an undocumented feature. This response was nominated for the lamest vendor response at the Pwnie Awards 2026." The authors argue that file-notification attacks affect all major operating systems and that further mitigations are needed. They note that despite the protection for device files that has been implemented already, capability checks should be extended to monitoring one's own files and to any readable file. "On Windows, we propose disallowing the monitoring of entire drives," they state in their paper. "On Windows and macOS, the kernel could introduce a permission system (for context, access control, owned files and directories, minifilters)." The researchers' paper is scheduled to appear at ACM CCS 2026 this November in The Hague, Netherlands. ®

Třetí generace! Než se dostaneme k zásadním pokrokům v byznysu, začnu vývojem technologie. Orbital Marine Power mluví o nové generací turbíny. Zatím má jen základní obrysy, ale je tam přesně to, co nadchne technologického fandu – je větší a výkonnější. O2: První generace, technologický ...

Canonical is speeding up Ubuntu kernel releases to one a week as AI-assisted bug hunting helps bury defenders under an ever-growing pile of CVEs. The Ubuntu maker is overhauling how it ships kernel Stable Release Updates (SRUs), replacing its current four-week regular and two-week security cycles with overlapping two-week cycles that will push a kernel release every week. Canonical says the change is needed because the number of reported vulnerabilities has exploded, with AI deserving some of the credit – or blame, depending on which side of the patch queue you're sitting. "Large language models (LLMs) and specialized AI agents have transformed bug discovery from a manual, time-intensive process into a highly automated engine," Canonical said on Wednesday. AI isn't solely responsible for the CVE avalanche. The upstream Linux kernel community became a CVE Numbering Authority in 2024 and began assigning identifiers to thousands of bugs on the basis that almost any kernel flaw affecting a running system could have security implications. Put the two together, and Linux vendors have far more CVEs to deal with. Canonical says the resulting backlog requires faster releases to shrink the window between vulnerabilities becoming public and patched kernels reaching users. Under the new system, each SRU cycle lasts two weeks, but a new one starts every week. The first week is spent integrating patches, preparing and building kernel packages, and carrying out basic checks to make sure nothing catches fire. By the end of that stage, release candidates are published to Ubuntu's -proposed pocket. Week two is reserved for the heavier stuff, including hardware certification, distro integration, and regression testing. Once that's done, the kernel is released. Because the next cycle starts while that testing is under way, Canonical can publish another kernel the following week. For admins who consider even that too leisurely, there's a faster route. Organizations particularly sensitive to patching delays can take release candidates from the -proposed pocket after the first week and run their own acceptance tests. Canonical makes the trade-off clear: those users get access to fixes sooner, but before the company has finished its extensive certification testing. That can make kernel CVE fixes available within a week, provided customers are willing to perform some of the testing themselves. Canonical also wants to leave customers less exposed between disclosure and patch availability. It aims to provide safe workarounds where possible, or recommend general hardening measures where none exist, putting systems into what it calls a "defensible, safer state" within 24 to 48 hours of public disclosure. Those measures are not intended to replace patching, merely to give admins something better than crossing their fingers while a fix makes its way through the release process. The end result is a considerably busier kernel release schedule, although perhaps that's inevitable when machines are increasingly being enlisted to find bugs faster than humans can patch them. AI was supposed to make everyone's jobs easier. Ubuntu's kernel team may want a word. ®

Microsoft 365 E5 and E7 customers can now run security information and event management (SIEM) inside Microsoft Defender at no extra license cost.

Microsoft is delivering the capability through the Integrated Security Operations Center (ISOC) in Microsoft Defender, which combines SIEM with Defender’s existing XDR, threat intelligence, automation and AI tools in a single portal.

“Security cannot operate at AI speed when protection and operations are built as separate systems,” Rob Lefferts, corporate vice president of Microsoft Threat Protection, wrote in a blog post announcing ISOC. He said attackers now use AI agents to automate attacks at scale, and that every handoff between separate tools slows defenders down.

Until now, E5 and E7 included Defender XDR but not SIEM, which Microsoft sells separately as Microsoft Sentinel.

Under ISOC, logs from Microsoft’s own security products are available without ingestion charges. From Oct. 1, data ingested from third-party security tools and other outside sources will be metered at $2.40 per GB on a pay-as-you-go basis, Microsoft said in a technical blog post.

The company opened ISOC as a public preview, meaning it is not fully production-ready, on Sept. 23 and has not said when the preview phase will end. It is available to organizations with an active Microsoft Defender Suite and Microsoft 365 E5 or E7 license that do not already run Microsoft Sentinel, and has no minimum seat requirement.

For organizations whose security stack already runs largely on Microsoft, the trade is attractive, said Yih Khai Wong, senior research manager for security services research at IDC Asia/Pacific.

“Most telemetry already resides in Defender, Entra, and M365, so bundling Sentinel’s SIEM into E5/E7 effectively makes ingestion free, with no separate license needed to move and re-analyze data your own vendor already holds,” Wong said.

A benefit, not a product

“ISOC is not a new standalone product,” Microsoft said in an FAQ accompanying the technical post. Its ISOC product page said capabilities such as workbooks and natural-language-to-SOAR automation “previously required a separate Sentinel purchase.”

According to the technical post, Microsoft began rolling out case management, workbooks and natural-language playbook generation to eligible customers’ Defender portals on Sept. 23, with no additional configuration required.

The included data covers Defender for Endpoint, Office 365, Identity, Cloud Apps and Cloud, plus Microsoft Entra ID Protection logs and Azure and Office 365 activity logs. Retention is 30 days during the preview, rising to 90 days on Nov. 15, the post added.

Everything else costs

Anything beyond that requires an ISOC workspace, which needs an Azure subscription, according to Microsoft’s product documentation. The workspace unlocks more than 500 data connectors, user and entity behavior analytics (UEBA), CI/CD repositories and threat intelligence.

Wong said mixed environments face a different calculation. “For multicloud shops or organizations with significant non-Microsoft telemetry, this requires a real total cost-of-ownership comparison against current SIEM spend.”

The current preview excludes organizations with an active Sentinel workspace. Microsoft said nothing changes for existing Sentinel customers, and those meeting the licensing criteria can choose to move to ISOC from Nov. 15.

Consolidating identity, endpoint, productivity and security operations under one vendor can simplify investigations, Wong said, but it also “creates greater dependency on that vendor’s roadmap, pricing model, availability, and security resilience.”

CISOs running a separate SIEM face a genuine evaluation, not a quick swap, weighing migration effort, retraining of detection content, exit costs and contractual complexity, he added.

Sanchit Vir Gogia, chief analyst at Greyhound Research, advised enterprises interested in ISOC to “reproduce the organisation’s highest-value cross-vendor detections before replacing a mature SIEM,” warning that “Microsoft’s claim of more than 500 connectors establishes reach, not equal treatment of every source.”

Agents take the next step

Microsoft’s Lefferts positioned ISOC as the foundation for the agent strategy Microsoft introduced alongside Project Perception in July.

Gogia questioned the premise that this requires a single vendor. “Microsoft has established a useful integration path. It has not established that one supplier is technically necessary for an agentic SOC,” he said. He noted that Project Perception remains invitation-only in limited public preview.

Microsoft has not published details on how agent actions within ISOC are logged, audited or reversed. Gogia said the critical threshold comes when agents gain authority to make changes, since changes to identity, endpoint or cloud policy “create a blast radius.” He recommended distinct agent identities, narrow tool permissions and independent approval for consequential changes.

The agents themselves add attack surface, Wong warned. “Adversaries may attempt to manipulate the telemetry an agent relies on, poison contextual data, or use prompt injection techniques to influence agent behavior and decision-making,” Wong said.

“Microsoft has made integration easier to buy. The enduring test is whether customers retain the means to challenge and reverse it,” Gogia said.

Microsoft did not immediately respond to a request for comment.

This article first appeared on CSO.

Jamf was a pioneer in Apple device management when it began in 2002. The company was ahead of its time: its founders could see that Apple had a future in the enterprise, but not many others saw it. Jamf now runs 35.2 million devices from 78,000 organizations. And it’s still looking to the future.

I spoke with Jamf CEO Beth Tschida, who joined me for a chat directly after her keynote speech at JNUC, Jamf’s big event for Mac admins, on September 23. 

The AI-driven ecosystem

Tschida wasn’t there to rehash Jamf’s past for relevance in the present, but to share the company’s deep commitment to being where its customers are going to go. That means artificial intelligence, which in Jamf’s case involves weaving AI across its ecosystem of products, from setup to tech support, security, and beyond.

It’s not a new vision — you could argue that some early signs of its intention were visible when the company acquired ZecOps in 2022 — but time moves forward and the company has coalesced around a firm philosophy of how to use AI, where to use AI, and how AI should be deployed in a human-centric way for the benefit of its customers.

“AI runs better on Apple, and Apple runs better on Jamf,” Tschida told me. The next piece to that statement is to consider how AI can be effectively managed by IT, she added. “You have to be able to see it. You have to be able to govern it, and then you want to be able to harness it.”

This points directly to shadow AI, one of the big-picture problems IT has with AI at the moment as employees feed confidential data to the cloud-based AI models. Jamf’s response has been to create new frameworks IT can use to monitor use of AI across their managed fleets. These tools let IT identify use, manage use, or even stop use altogether.

“How can we look at that, see it, govern it, and harness it in the ways that AI is providing that opportunity, right at the endpoint?” Tschida said. 

Precision modeling

The other component is pricing. Even when data is legitimately shared to AI, the cost of inferencing is high and getting higher. Jamf now offers tools to let IT monitor this use and the cost of it, enabling management to suggest lower cost or on-prem AI solutions for tasks where appropriate. After all, why would you pay for Fable when you can get your Apple device to help write that letter?

Tschida explained that part of Jamf’s response to AI costs is the inclusion of granular model controls, which let admins assign different teams access to different models, the idea being to prevent what she called “overmodeling” — the use of expensive high-reasoning models for tasks that lightweight models can handle.

“You have to understand what the need is. You must lay it out there in a governed way. Human in the loop and then you harness it,” she said.

The Mac advantage

But the Jamf vision doesn’t end with AI management; it extends to making active use of the tech. In this case the company has introduced AI-powered tech support for routine problems to free up staff time. There are two strands to this approach: More obviously, easy to access tech support should reduce time spent on routine problem solving, but another compelling aspect of this evolution is the move toward proactive device health.

Traditionally, IT has been reactive: a user experiences slowness, they eventually file a ticket, and IT fixes it. But many users simply “endure” suboptimal performance without ever reporting it, Tschida explained. “A lot of times, users don’t even raise a ticket. They just endure.”

That was then, but the future looks different. “We believe that we now have the right telemetry to look at device health,” Tschida said. “You’re really moving ahead of problems. Users don’t even raise a ticket… you can help them optimize it without them knowing it.”

With over 35 million devices now managed using Jamf, the origin story of the company seems further ahead of its time now than ever. Apple’s enterprise product marketing lead, Jeremy Butcher, appeared at one point during Tschida’s keynote to talk about where Apple now is in the enterprise. He pointed to data that Macs generate 55% fewer help tickets than Windows systems. He confirmed that the MacBook Neo has accelerated enterprise growth for the platform, and pointed out that more Apple devices are purchased in enterprise than any other brand.

With so much energy behind Apple’s platforms, what has Tschida been seeing?

“We know that Anthropic and other companies, they tend to deploy for Mac first. The developers are mostly running Macs. That’s what they prefer to be on, and they’re at the forefront of agentic AI.”

The thing about the growing enterprise Apple ecosystem is that no single company can build the perfect set of solutions for every enterprise. Part of Jamf’s response to this has been the creation of platform APIs, which customers and partners can use to extend the platform to meet their needs. “I love a good API because it allows you to run a road map way faster,” Tschida said.

Former Jamf CEO Dean Hager used to say, “When Apple innovates, Jamf celebrates.” 

His successor echoes his point, with a focus on what Jamf can do for its customers. “Our job is to make sure that Apple runs better with Jamf,” she said. “We’re scaling it, we’re securing it, and we’re making sure that you can… first see it, and govern it, and then harness what you can get from it in a way that’s helpful for our customers.”

Local intelligence, global scale

A second aspect to governance is around sovereign, private, and/or on-premises AI. I asked Tschida what her customers are saying. “There will be some that look at the benefit if you can run at least some of your workflows locally,” she said. “There’s a tokenomics piece of it. There’s a privacy piece of it. There’s a sovereignty piece of it.”

Whatever the motivation, for Jamf the question remains, “How can you determine what we can do and help you do, locally on device, and build that so you can run it at scale? That’s where we’re focused.”

I walked away from the interview feeling a vision for Apple in the enterprise in which IT is augmented by smart tech infrastructure: Macs that request support before users know they need it, systems that self-identify bottlenecks or wasted resources such as over-generous token consumption and let IT take steps. A more intelligent enterprise infrastructure, equipped with real-time awareness, self-reliant with little to no cloud exposure.

For Apple, Jamf, and certainly for the Mac, that seems like several giant steps since the foundation of Jamf in 2002. AI in IT is an accelerant, and Jamf is working with it. “We are doing our best to run at the speed of AI,” said Tschida. 

Now please subscribe to my daily, human-curated Apple-related news headline feed at The Core, or follow me on BlueSky, LinkedIn, or Mastodon.

Reklamy ve hrách, zejména těch mobilních, už bohužel patří ke koloritu dnešní doby. Obtěžování reklamou může současně motivovat k zaplacení plné verze bez reklam, ale co kdyby ty reklamy byly nějak lépe vymyšlené, aby nepůsobily jen negativně? Microsoft si před pár dny podal patentovou přihlášku, ...

The "third-party[.]com" domain, commonly used as a documentation placeholder, has been observed serving a ClickFix lure to Windows browsers while displaying a harmless decoy to other users. "third-party[.]com has been a generic documentation placeholder for years, the same role example.com plays," Manifold Security's Head of Research, Ax Sharma, said. "Unlike 'example[.]com,' third-party[.]com

The "third-party[.]com" domain, commonly used as a documentation placeholder, has been observed serving a ClickFix lure to Windows browsers while displaying a harmless decoy to other users. "third-party[.]com has been a generic documentation placeholder for years, the same role example.com plays," Manifold Security's Head of Research, Ax Sharma, said. "Unlike 'example[.]com,' third-party[.]com Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Blíží se říjen a s ním i tradiční hororová sezóna. Studio One O One Games se svým husí kůži nahánějícím příběhem přišlo o něco dřív. I když mělo skvělý nápad o pokračování pokrevní linie italského básníka, jeho provedení je přinejlepším průměrné.

Asus has warned eShop customers that an intruder got into part of its online store and may have helped themselves to contact details and order records. The PC maker disclosed the incident in an email sent to customers, first reported by KitGuru, in which it said had identified "unauthorized access to part of the Asus eShop environment," although exactly when that access occurred remains unclear. "Our investigation indicates that certain customer order information, including contact details and order records, may have been accessed," the company said. There is at least some good news for anyone who has handed Asus their card details. The company said no payment card, bank account, or other financial information was involved in the breach. Asus also said it isn't currently aware of the compromised information being misused or of any affected customers suffering harm. The company said it took steps to contain the incident after discovering the unauthorized access, launched an investigation, and introduced additional measures to secure the affected systems. That investigation remains ongoing, but Asus said it had found no evidence of continued unauthorized access. What Asus hasn't said is how many customers are caught up in the mess, when the intrusion began, how long the attacker had access, which countries are affected, or how whoever was behind the break-in managed to get into the eShop environment in the first place. The details that did escape, however, could give scammers a decent head start. Asus warned that the stolen details could give scammers enough to make phishing emails, texts, and phone calls about its products or customers' orders look rather more convincing. Asus told customers to keep an eye out for unexpected messages mentioning previous purchases, though it reckons the risk of anyone actually misusing the data remains low. This isn't the PC maker's first recent brush with data thieves. In December, Asus confirmed that one of its suppliers had been hacked after the Everest ransomware gang claimed to have pinched 1 TB of data from itself, ArcSoft, and Qualcomm. The company said the haul included some camera source code used in its phones, but maintained that its own systems and customer data were untouched. The Register asked Asus for more details about the latest breach, including how many customers were affected and when and how the intrusion occurred, but has not yet received a response. Asus is yet to comment publicly on the incident, and there is no mention of the breach on its eShop. So it's the usual post-breach drill: beware unexpected emails, texts, and calls. Except this time, whoever's behind them may have the receipts. ®

Google potvrdil neobvyklý bezpečnostní incident z letošního května. Modely Gemini při testování svých hackerských schopností získaly přístup do systémů tří skutečných společností. Nikdo modelu nezadal, aby napadal firmy. Kvůli chybě v testovacím prostředí je považoval za součást cvičení. Jakmile ...

An active ClickFix campaign has been observed compromising legitimate Ukrainian business websites to inject bogus Cloudflare verification pages and trick victims into downloading a previously undocumented information stealer called Psychedelic. "When a visitor interacts with the page, the lure copies a Windows Installer command to the clipboard and instructs the visitor to paste it into the

An active ClickFix campaign has been observed compromising legitimate Ukrainian business websites to inject bogus Cloudflare verification pages and trick victims into downloading a previously undocumented information stealer called Psychedelic. "When a visitor interacts with the page, the lure copies a Windows Installer command to the clipboard and instructs the visitor to paste it into the Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

FedRAMP's new VDR and VER requirements make vulnerability management more continuous, with faster scanning, tighter remediation deadlines, and stronger evidence requirements. Anecdotes explains why the December 7 deadline is just the beginning of a broader shift toward continuous, automated compliance validation. [...]

Autonomní vůz Waymo vjel v Denveru mezi stánky na farmářském trhu • Lidé si nevěděli rady, jak vlastně auto bez řidiče zastavit nebo přesměrovat • Město Denver nyní řeší komunikaci s bezpilotními vozidly v krizových situacích

A high-severity Roundcube Webmail vulnerability patched in May is now being actively exploited in attacks, according to the Canadian Centre for Cyber Security. [...]

It’s been five years since Google first offered a glimpse of Project Starline, a three-dimensional videoconferencing platform billed at the time as as a “magic window” that enables realistic and immersive calls.

On Wednesday, Google announced that Google Beam — as the software element of the product is now called — is generally available for purchase, built into HP’s Dimension video meeting hardware. The Dimension hardware reportedly costs around $25,000 per unit; Google did not provide details for costs related to hardware or additional software licenses. The combined products will be sold in six countries at first: Canada, France, Germany, Japan, UK, and US.

Google has also partnered with co-working space provider Industrious to make Beam available to try at its locations across the US, including Atlanta, Chicago, New York City, and Palo Alto.

Beam “offers the potential for immersive, 3D engagement, without the challenge of wearables,” said Irwin Lazar, principal analyst at Metrigy.

“This is one of those ‘you have to try it’ experiences to understand the value proposition,” said Avi Greengart, president and founder of Techsponential. “It really does feel like you’re talking to someone a half a world away, with all your micro-expressions and nonverbal cues intact.

“This doesn’t just save you travel time and budget, it enables closer collaboration throughout a project or working relationship that provides different value than occasional live visits, which you may still need.”

Google began developing the Beam technology several years before it was unveiled amidst the COVID-19 pandemic and shift to remote working as offices were shuttered. It combines AI-generated 3D images of participants with spatial audio to create the sense of being in the same room.

The hardware has evolved considerably since then, with Google and HP shrinking the original large video booth into a smaller system that more closely resembles a conventional videoconferencing setup.

The 65-inch Dimension display renders meeting participants in their full size, and includes the cameras, 12-microphone array, and LED lighting required for the Beam software. Google Meet and Zoom video meeting apps are supported.

While Beam currently enables one-to-one meetings, Google recently highlighted its efforts to enable group calls, too. This is currently at an experimental stage.

Google claimed that an internal study showed 50% of Google staffers who used Beam felt more connected, resulting in a 21% drop in the need for follow-up meetings.

Select customers were granted early access last year, ahead of today’s full launch. One of them, management consulting firm Bain, uses Beam to interview job candidates remotely, reporting that it helps interviewers pick up on nuances of body language and eye contact without needing to meet in person. This also saved on travel costs, the company said in a promotional video.

It remains to be seen whether the technology will gain a wider audience. The price tag will “present somewhat of a barrier to adoption,” said Lazar. He expects early adopters will target specific use cases such as “executive meetings, hiring interviews, product demos, and high-stakes engagement.”

Greentgart described Beam as a “network-effect system,” as the technology is required on both ends of a Beam call. “That makes the expansion of Google Beam on HP Dimension to more locations crucial,” he said. “I expect that some companies will try it at a conference center before investing in endpoints inside their own companies.”

Microsoft has added a “pay-as-you-go” option for OneDrive storage, providing an additional way for Microsoft 365 commercial customers to buy extra cloud storage capacity for users.

Previously, increasing storage capacity for OneDrive users meant purchasing capacity “packs” for individual accounts, available in 100GB, 500GB, and 1TB through 6TB sizes.

With the new consumption-based billing, Microsoft 365 admins can select which users’ accounts are eligible for additional capacity, with any usage above the licensed storage quota then automatically billed.  

The new billing option is currently in public preview for Microsoft 365 business customers, ahead of general availability starting November.

Pay-as-you-go billing is turned off by default: Admins can set it up via the Microsoft 365 admin center, with options to set budget limits and alerts. The existing overall 25TB per-user limit for OneDrive remains in place.

Additional OneDrive storage costs $0.20/GB/month. Microsoft does not publish the pricing for the OneDrive capacity “packs.”

The OneDrive update follows a similar move with SharePoint, with a public preview of pay-as-you-go pricing for extra storage starting June, ahead of wider availability for GCC, GCC High, and DoD customers in November.

Microsoft will host a OneDrive + Copilot digital event on Oct 20 with updates on AI features coming to the cloud storage application.

A decade ago, UiPath built its business on watching people work, recording keystrokes and clicks to automate tasks employees already did by hand. This week, amid an industry consumed by autonomous agents, the company’s newest product suggests that same instinct still has a place.

UiPath used its Fusion conference in Las Vegas to launch Cartographer, a tool for building what the company calls a Map of Work: a living, governed record of the informal, undocumented knowledge that keeps enterprise processes running, the exceptions, workarounds, and judgment calls that never make it into an official workflow document.

Existing automation tools capture the click-by-click steps of a task. Cartographer works at the process level instead, documenting how work actually gets done versus how it’s designed to run. From the main stage, CEO Daniel Dines described this information as a missing manual enterprises need before trusting agents with real work. “A process map tells you how work is designed to run,” he said. “UiPath Cartographer is the key to uncovering the operational knowledge of how that work actually runs.”

Even with Cartographer, the first drafts of a Map of Work “will be incomplete for sure,” Dines admitted on stage.

That missing knowledge has kept enterprises’ most complex, exception-heavy processes out of automation’s reach, said Mark Geene, UiPath’s group senior vice president of agentic solutions. He characterized Cartographer as the next stage in a five-year push to extend the automation platform into messier enterprise areas. Capturing that knowledge, if it works, opens the door to automating processes once too undocumented and judgment-dependent to hand over to machines at all.

How well it will work is a different question. Analysts and customers on the ground described a tool that’s promising on paper, but whose real test comes later, in whether enterprises do the disciplined work of capturing and maintaining what Cartographer helps them uncover.

What’s different

That capture process has a real limit, said Amy Loomis, a research director at IDC, one that depends on whether people actually share what they know. “That step will only be as strong as the tribal knowledge of teams and the ability to capture it accurately,” she said.

Pairing deterministic guardrails with the inherently unpredictable behavior of large language models (LLMs) differentiates UiPath’s approach, Loomis said. She pointed to the spectrum of options UiPath will offer: prebuilt maps ready out of the box at one end, a curated Process Atlas in the middle, and Cartographer itself handling processes requiring full customization. A human “cartographer” role, an existing business analyst repositioned to own a Map of Work, is the other new piece, she said.

Getting a full picture of a process has been a persistent problem, one that often doesn’t surface until well after a bot is live, said Andrea Simpson, IT manager of automation at USI Insurance Services. “So much knowledge is in a person’s head, and it can be hard to get it out of our subject matter experts,” she said.

Knowledge management — a problem enterprises have been dealing with since the cold war — is the pain point UiPath aims to ease. But there are limits. Some knowledge will likely stay out of reach, Simpson said.

USI is an early adopter of Maestro, UiPath’s orchestration tool, to automate its marketing processes, tying together producer meetings, carrier submissions, and client communication. But the biggest hole is what happens off the record: a phone call, a conversation at someone’s desk. “We wouldn’t want that automated,” she said. “We want to keep that human.” That knowledge never touches a company system until a producer chooses to report it.

On-the-job training for agents

Perfection won’t be required to get started. “The goal isn’t to understand all the rules and processes from all the years you’ve been doing a process,” said UiPath’s Geene. “The goal is to capture enough information to get agents executing the process. The key is to get live early.”

Typically, once an LLM is built and deployed, “their brains freeze,” said Doug Marcey, CTO of Coronis Health, a healthcare revenue-cycle-management company that has used UiPath for about a year and a half. Cartographer’s promise, he said, is closer to on-the-job training for agents, gaining institutional knowledge the way a new hire gradually would, with each new exception routed through a “Decision Ledger” for a human owner to approve before feeding back into the map. That trail matters as much as the automation itself in a regulated industry. “The difficulty in healthcare is that compliance is all about vibes,” Marcey said. “The Decision Ledger would be very helpful for us.”

Coronis’s denial-management work shows what captured judgment can do. Agents draw on claims data, orchestration coordinates the workflow, and generative AI drafts appeal letters, a combination that’s made it financially viable to contest denials on smaller claims that previously would have been written off, Marcey said. Another UiPath healthcare customer was losing money on tens of millions of denied claims because generating an appeal could take 45 to 75 minutes, so it often wouldn’t happen despite a 50%-to-60% overturn rate on filed appeals, Geene said. With agents built on a Cartographer-style Map of Work, that process now takes four to five minutes.

The human factor

UiPath’s human cartographer role suggests the tool is meant to reshape jobs, not eliminate them. “This isn’t about replacing people,” said IDC’s Loomis.

At USI, much of a business analyst’s time today goes into repetitive work, translating what a stakeholder describes into documentation a developer can build from, work Simpson is eager to hand off. “It’s going to save so much time,” she said. “It’ll grow my team’s knowledge and improve the outcome of our processes.”

How it all shakes out will come down to people, as with any tech-driven transformation. “The hard problem is the change management at the organizational level,” said Marcey of Coronis Health.

And the accountability will remain with humans as well. “We can’t go to clients and say, ‘Hey, the bots screwed up,’” Marcey said.

This article first appeared on CIO.