The Department of Homeland Security’s top numbers-cruncher has resigned, citing the Trump administration’s “war on immigrants” on his way out.

DHS has been a hub of controversy during the second Trump term: the ICE raids in American cities, the wild spending, the growing archipelago of detention centers, the attempts to hide data about it all. But vanishingly few officials have left the department and been openly critical of the direction it has taken. Marc Rosenblum, who served as executive director of the Office of Homeland Security Statistics and a deputy assistant secretary at the department, is one of the first.

“I’m thrilled to end my relationship with the current administration,” Rosenblum said in a LinkedIn post over the weekend. “Between the war on immigrants, the war on feds, and the war on facts (not to mention the crazy war in Iran and the brazen corruption), I just need a change.”

Rosenblum also served in the first Trump and Biden administrations, initially running DHS’s office of immigration statistics, and then overseeing stats for the entire department—collating numbers on everything from cybersecurity intrusions to trafficking investigations to deportations. His office peaked at 45 people in the Biden years, and started to shrink since.

“He had one of the most interesting charges at DHS,” a former colleague tells WIRED of Rosenblum. “There’s 22 component agencies, and so there are 22 different ways of aggregating data that were just completely siloed.”

“So Marc’s charge was to consolidate all the data and put it in a way that internally, I at headquarters could know exactly how many Coast Guard interdictions happened off the Coast Guard station in Miami, and how many of those were Cuban, Haitian, Nicaragua, Venezuelan. And then kind of conceptualize that with the same population of people that were crossing the Southwest border,” the former colleague adds.

A domestic security agency like DHS is never going to be a model of transparency. But Rosenblum, a long-time immigration policy wonk and an immigration specialist for the Congressional Research Service, promised as much openness as possible when he took on his department-wide role in 2023. “We’ll begin releasing data more quickly, with greater granularity and covering a broader scope of DHS activities,” he told Federal News Network that November.

That data itself became controversial in Trump 2.0. The Office of Homeland Security Statistics’ website on ICE detentions, Border Patrol encounters, and DHS repatriations hasn’t been updated since February 11, 2025, just weeks into the second Trump term. What information has been pried from DHS since then, often through Freedom of Information Act lawsuits, has been at times spotty and unreliable, analysts say.

“There’s no accountability, no way to assess, no public understanding about what’s really going on that is not curated by a press release from the agency,” David Bier, the Cato Institute’s director of immigration studies, told NOTUS in November.

DHS didn’t immediately respond to a request to comment for this story. Neither did Rosenblum. But he did allude to the difficulty of publishing quality data under this administration in his LinkedIn post. “My OHSS co-workers are outstanding career federal civil servants. They are smart, mission-focused experts who produce high quality results in a challenging and often hostile work environment,” he wrote.

“Marc was exactly what every American should want in a career public servant,” Luis Miranda, a top DHS spokesman during the Biden years, tells WIRED. “The Trump administration has governed by silencing or ignoring facts and information they believe doesn’t line up with their extreme rhetoric, and that has damaging implications, so it’s no surprise career officials whose job is transparency are unable to do those jobs.”

Apple is being sued for alleged negligence regarding its App Store security measures. In a lawsuit filed on Friday in the U.S. District Court of Northern California, three plaintiffs claim they were tricked into downloading and installing a fraudulent crypto wallet app, leading them to collectively lose more than $1.8 million.

The lawsuit centers on Apple’s claim that it secures its App Store by reviewing apps before they go live to protect users from fraudulent and malicious apps. In this case, the plaintiffs downloaded an app called Sparrow Wallet, even though the official Sparrow Bitcoin wallet is not available on iOS.

The plaintiffs transferred their Bitcoin to the fraudulent app, losing large amounts of the virtual currency, according to the complaint. Plaintiff James Ramirez lost about $875,000; plaintiff Christopher Ellis lost around $840,000, and plaintiff Jalen Delgado lost roughly $120,000.

The complaint targets one of Apple’s longtime competitive arguments: that its tight control over its App Store makes its platform safer than those offered by its rivals. The company has used this claim to push back against deregulation of the app ecosystem, including third-party app stores and sideloading.

“As part of a sustained marketing campaign, Apple has positioned itself, its products and services, as offering a level of security and trustworthiness superior to any competing technology company,” the new filing states. “This includes assurances about the safety of applications distributed through its App Store. By retaining exclusive control over which apps are permitted on Apple devices, Apple has structured its platform to ensure that consumers depend entirely on its promise of safety and reliability,” it reads.

The complaint also accuses Apple of knowingly hosting fraudulent apps, pointing to public criticism by Sparrow Bitcoin Wallet’s creator, Craig Raw, who said Apple had allowed fake Sparrow Wallet apps to remain on the App Store.

The three are asking for a trial by jury and seek to recoup their lost money and other damages. They also want Apple to provide warnings and disclosures about the App Store’s risks.

Apple declined to comment on the lawsuit. The company did stand behind its security measures, telling TechCrunch that apps impersonating others are a violation of its guidelines and it takes swift action to remove them. Apple added that there are currently no Sparrow Wallet copycats on the App Store.

The company also pointed to its latest analysis of its ecosystem, which found that in 2025 it rejected more than 371,000 submissions that copied other apps, were spam, or otherwise misled users.

Your GPU dashboard says 70% utilization. On paper, the cluster is busy. In practice, a large chunk of that time is spent with your $40,000 accelerators sitting idle, waiting on a file that lives three network hops away on a NAS box. The compute queue is empty, and the pipeline is fine. The problem is that data is just somewhere else.

This is the awkward truth underneath most stalled AI projects. The constraint in modern AI infrastructure stopped being storage capacity years ago. Now, it’s more about data placement and access. What matters is where files live and how they get to GPUs, along with how much copying happens in between. In that sense, AI infrastructure has become less of a storage capacity problem and more of an operational data problem. The Hammerspace Data Platform takes that as its starting point. It sits between your compute and the storage you already own, from NAS to object stores and even the NVMe drives bolted into your GPU servers. It makes all of that data addressable through a single global namespace. Instead of moving data to wherever the GPUs are, the architecture makes the compute aware of where the data already lives.

As a result, rather than treating each storage system as its own operational silo, Hammerspace separates the data layer from the underlying infrastructure, allowing heterogeneous storage, sites, and clouds to operate as part of the same coordinated data environment. Applications and AI pipelines access that data through standard protocols such as NFS, SMB, and S3, without proprietary clients or application rewrites.

Fragmentation is the bottleneck, not bandwidth

Data fragmentation is a big problem for enterprises embarking on an AI journey. Training sets are scattered across departments, sites and clouds.

“The data is in disparate groups and disparate orgs and disparate silos within a company,” says Jonathan Flynn, director of applied systems at Hammerspace. “Having the data in a curated data set for you just to go train is rare. It has to be collected. It has to be moved around from system to system, and then the curation needs to happen in order to actually do the training on it.”

The fragmentation often leaves pipelines copying and staging files between systems that were never designed to talk to each other. None of this shows up on a storage IOPS chart, but it will visibly affect training velocity.

According to Gartner, 57% of organizations believe that their data isn’t AI ready. Alarmingly, two thirds of executives believe that no one in their organization understands all of the data they’ve collected and how to access it. That seems hard to swallow, until you recall that Facebook’s engineers have admitted the same thing. You can’t orchestrate what you can’t see.

Mike Bloom, who covers AR architecture at Hammerspace, says the default vendor response makes the problem worse. “They’ll go to a vendor that will promise them that if they sweep the floor and throw out all of their legacy storage arrays, their brand will solve the problem,” he says, adding that’s like throwing the baby out with the bath water. “Those data sets that are all over the place? They’re not sitting in a corner. They’re sitting on legacy storage arrays.”

The NVMe you already paid for

There is also a less obvious idle resource in most AI environments: the NVMe inside the GPU servers themselves. A modern HGX or DGX box ships with eight to sixteen NVMe drives, each hanging off four lanes of PCIe. Almost every orchestration layer treats that capacity as local scratch space, used by one server and invisible to the rest of the cluster.

Hammerspace calls this “stranded” capacity, and it is now meaningful. It amounts to hundreds of terabytes per server, with two-petabyte GPU servers on the roadmap. Pull all of it into a shared namespace and you have a new layer that Hammerspace calls Tier 0. It uses storage you already paid for, attached to a network you already deployed.

Flynn argues this layer is structurally faster than anything sold as a separate appliance.

“Tier one is typically oriented around storage capacity. A 2U box, 24 NVMe, or 40 NVMe with some of the Dell systems in there,” he calculates. “That’s 96 lanes or 192 lanes of PCI Express, with maybe one or two 400 gigabit NICs, which gives you 16 or 32 lanes. So the over subscription just in the one box is massive.”

His more provocative claim is that it is also the cheapest tier in the rack. The compute and the network are already there. The drives (at least in the case of customers buying GPU servers) are already in the bill of materials. Compared with racking and stacking a dedicated all-flash array, adding metadata servers and a few data movers to existing GPU nodes barely registers as a procurement event.

Assimilating what you already own

Ripping and replacing infrastructure takes time most teams don’t have. The Hammerspace approach is assimilation, which the company describes as a metadata-only operation: scan the existing NAS, ingest the directory tree into the global namespace, and redirect mounts. The bytes never move.

Hammerspace says that fast deployment is a key benefit of this approach. Data access is restored almost immediately, even while assimilation continues in the background.

Underneath this, the source-of-truth NetApp, Qumulo or VAST array keeps serving the bytes, while Hammerspace presents a unified view on top. That has practical consequences.

If something tagged as a training input changes from being a tier-two archive file to a hot input, a policy (Hammerspace calls this an “objective”) can trigger an instance copy onto tier 0 without users having to do anything. “Nobody’s running a copy. Nobody’s running an rsync command,” Flynn says. “It’s all orchestrated based in the file system.”

That same orchestration layer can also support retrieval-augmented generation (RAG), inference, and agentic AI workflows, where distributed enterprise data needs to be continuously curated, governed, and made accessible without relying on large-scale data copying.

Once the training job finishes, that tier 0 copy is automatically vacated. The clean-up matters because the alternative (letting a hot tier fill up) creates a quality-of-service problem for everything else trying to land there.

“Other architectures that have a hot tier and a cold tier often have an issue where the hot tier becomes congested and that endangers the quality of service for the pipeline,” Bloom says. “Rather than requiring organizations to rebuild infrastructure around AI, the Hammerspace approach is designed to operationalize the storage, cloud, and compute environments enterprises already have in place.

Standards-based, with some asterisks

Hammerspace’s positioning leans heavily on the word “standard”. The Samsung-Hammerspace submission that landed inside the top 10 of the IO500 10-Node Production benchmark in November 2025 used standard Linux, the upstream NFSv4.2 client, standard NVMe SSDs and IP-over-InfiniBand. There was no proprietary client, and no custom kernel modules. The company submitted its own results to MLPerf Storage v2.0 showing linear scaling out to 420.8 GB/s across 140 GPUs on five nodes with GPU utilisation above 96%.

That kind of performance is not achievable with traditional NFS architectures, which struggle with the parallel access patterns common in large-scale AI environments. Instead, Hammerspace runs on parallel NFS (pNFS). Instead of letting a single server handle file metadata transfer alongside data transfer, it creates a layout map that the client can then use to transfer data from multiple servers in parallel. That became the RFC 5661 standard in 2010. Hammerspace was also instrumental in extending pNFS in NFSv4.2 in 2018, introducing the Flex Files extension. This is what lets pNFS work with real-world hetergeneous storage across cloud tiers, legacy files, and multi-site deployments.

The larger implication is that open, standards-based infrastructure is no longer inherently at odds with AI-scale performance, challenging the assumption that enterprises must adopt proprietary storage stacks to support large-scale AI workloads.

“With the performance improvements that we contribute into the upstream, we’re actually seeing a decades-old file system transmute into a parallel access system that can rival WEKA, Lustre, and GPFS,” Flynn says.

Multi-site and sovereign by default

Once a single global namespace spans on-prem arrays, cloud object stores and the NVMe inside GPU boxes, the next questions are jurisdictional. Where can a given file legally live? Who is allowed to copy it? The platform handles this through the same objectives mechanism used for performance tiering. Tag a dataset as EU-only and the orchestration layer will exclude it from North American volumes. Tag it as HIPAA-bound and write-once-read-many rules apply.

Because those policies operate at the data layer rather than within individual storage silos, governance persists even as data moves across clouds, sites, and performance tiers. That is becoming increasingly important as AI pipelines, inference workflows, and agentic systems operate across distributed infrastructure rather than within a single environment.

That matters more in 2026 than it did two years ago, since such operational flexibility also changes the economics of AI infrastructure expansion. The SSD supply situation has tightened. NAND and DRAM prices climbed through 2024 and into 2025, driven by AI build-out and hyperscaler hoarding. Buying your way out of a data-movement problem by adding another all-flash array is harder when the flash is harder to get. A control plane that understands workload, location and policy together is now a valuable procurement workaround.

Real-world usage

The most useful data point about whether any of this matters at scale is Meta. The company runs two 24,576-GPU clusters used to train Llama 3 and deploys Hammerspace specifically to enable live job debugging and real-time code propagation across the training pipelines.

If a company with effectively unlimited engineering resources still hits a data-movement ceiling at that scale, the enterprises running a fraction of the workload are almost certainly hitting it too, and the standard answer of “buy more GPU” does not address a problem one layer below the compute plane.

Flynn put the underlying joke about NFS politely. “The joke I always heard was, NFS is not for speed.” That used to be true. The newer claim, that an open, standards-based file system can sit underneath an AI factory and feed it, casts the venerable file protocol in a new light.

ICustomers will likely want to see an independent benchmark of this system’s performance against the likes of VAST, WekaIO and NetApp in heterogeneous customer environments, using test systems not designed by the vendor. Nevertheless, it looks promising. In the meantime, the data placement architecture conversation is certainly the right one to be having.

Sponsored by Hammerspace.

OFFBEAT

Is there a simulator for the wail when you realize your precious one-off bootleg has just been chewed up?

If you have wistful longings for the days when your songs carried the subtle hiss of the cassettes you listened to, we have some reel good news. A new project takes modern music back to the analog era.

The project, available on GitHub, is called “Audio Cassette Simulation” and uses FFmpeg to transcode audio files to get that “wish I’d bought some decent tapes instead of the superstore’s own-brand” sound.

The repository includes simulations of BASF LH Extra C90, Maxell UD C90, Sony CHF60 Type I Normal, Sony CHF90, TDK D90, and even Soviet MK-60 tape. Navigate to the appropriate cassette folder using bash, then run the conversion code (or use it to convert a live stream).

The output is dumped into a ./out folder for a bit of retro enjoyment.

According to the README, “This project simulates cassette tape audio profiles using ffmpeg.

“It applies tape noise, wow and flutter pitch modulation, bandwidth limits, and equalizer adjustments.”

There’s no special magic going on behind the scenes – a glance at the .sh files shows that the real effort has gone into working out the FFmpeg filters to recreate the effect of the required tape. Audiophiles might be horrified to see the output being saved as .mp3 files, but let’s face it: if you’re after that compact cassette sound, the potential audiophile downsides of .mp3 probably won’t be too high on the list of worries.

Compact cassettes first appeared in the 1960s and stored magnetic tape wound between two reels inside a plastic shell. The tape would be unwound from the first reel and wound onto a second reel during use, with the whole thing enclosed in a case (hence “cassette”). Data could be stored on it (indeed, we’re sure plenty of readers have fond memories of home computers that used the media for software) as well as audio, which brings us to the simulator.

Different brands of cassettes (and hardware) could impart differing levels of distortion and hiss to the audio, giving it a unique sound. As with vinyl, cassette sales dropped as customers turned to digital formats decades ago but, also like vinyl, the medium has enjoyed a resurgence in recent years.

The simulator is therefore a bit of a quarter-way house. It can’t recreate the feel of a cassette in the user’s hands. Nor can it replicate the heart-stopping noise of tape being chewed up in a misbehaving mechanism.

But it can take some tediously pristine modern audio and make it sound like it’s the 1980s all over again. ®

The Coca-Cola Company has confirmed that hackers stole data from its dairy subsidiary, Fairlife, during a ransomware attack earlier this month.

In a short statement earlier today, the global beverages giant says that it is still working to restore some of the impacted systems and operations, but most of the production in the U.S. has been resumed.

Coca-Cola disclosed the cyberattack in a filing with the U.S. Securities and Exchange Commission (SEC) on July 16, revealing that a ransomware attack had disrupted production operations at Fairlife.

Fairlife is a producer of ultra-filtered milk, protein shakes, and nutritional drinks. It operates four production facilities in the U.S., and has more than $1 billion in annual retail sales.

At the time, BleepingComputer sent a request for comments regarding the attack, but we received no reply.

A few days later, the Anubis ransomware gang claimed the attack and added Fairlife to the list of victims on its extortion site. The hacker group threatened to leak one terabyte of files allegedly stolen from the company, unless Fairlife paid a ransom.

The threat actor told BleepingComputer that they had encrypted the firm’s Nutanix systems, leaving no possibility of recovery.

As soon as Coca-Cola discovered the breach, the company reported the intrusion to the authorities and did not follow the attacker’s instructions to negotiate.

BleepingComputer contacted Coca-Cola again to validate the threat actor’s allegations, but a spokesperson declined to comment.

“The company previously disclosed that Fairlife experienced a ransomware event,” reads the Coca-Cola statement.

“This event involved access by an unauthorized third party to a portion of the company’s systems and taking of certain data, and a temporary suspension of production operations.”

Regarding Fairlife product availability, Coca-Cola says existing inventory helped cover temporary shortages caused by the production disruption, while product quality and safety were never jeopardized.

The timer that Anubis ransomware had previously set for the public release of the stolen data expired earlier today, and the data is now available for download.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

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Winners & losers: For the first time in nearly two decades, Mojang has announced a significant change in Minecraft’s hardware requirements, a rare move for one of the biggest gaming products in history. The Microsoft-owned studio wants to future-proof Minecraft: Java Edition, but players running older hardware won’t be cheering this time around.

Minecraft: Java Edition now officially recommends 16GB of RAM, and that’s just the starting point. Mojang recently explained that the wildly popular sandbox game is getting a more modern baseline for its system requirements, acknowledging that some players with aging systems will be disappointed as the company works to keep improving its blocky experience for everyone else.

The update arrives after 17 years without a change to Java Edition’s requirements, a long stretch by any video game’s standards. After nearly two decades, Minecraft has essentially grown out of its shoes: players will need stronger PCs to run future builds – no matter the impossibly expensive costs of crucial silicon components such as memory chips and GPUs.

Minecraft’s system requirements officially changed on July 21, covering both the “Fast” and “Fancy” presets. The game’s official store page now lists a 1080p, 30 FPS target for the Fast preset, and a 1080p, 60 FPS target for the Fancy preset. The Fast preset calls for 8GB of RAM with a discrete GPU, or 12GB with an integrated GPU. The Fancy preset, meanwhile, recommends 16GB of RAM for comfortably running Java Edition for the foreseeable future.

A 16GB recommendation might sound like a lot, especially considering Java Edition currently allocates only around 4GB of system memory by default. Mojang is also updating its CPU and GPU requirements, with stronger, modern processors, either x86-64 or Arm64, now needed to properly run the Fancy preset.

Minecraft: Bedrock Edition still has lower system requirements than Java Edition, though players will need the latter to use custom mods on PC. Mojang says the new requirements are still modest compared to modern AAA games, meaning Java Edition should keep running smoothly on most well-equipped gaming systems.

The company explained: “We want to make it clear for players what kind of computer can run Java Edition smoothly. While older devices may still launch the game, they will struggle to deliver a good experience. This can lead to low frame rates, visual issues, long loading times, or performance that doesn’t match what players expect.”

Guaranteed performance on older hardware is officially a thing of the past, but Mojang argues that’s for the best. Leaving aging systems behind will let developers focus on meaningful technical upgrades, starting with the shift from OpenGL to Vulkan.

Microsoft on Monday launched its first cybersecurity-specialized model alongside a new AI cybersecurity platform at a small event in San Francisco, taking a big swipe at major players in the space — namely Anthropic, Google and OpenAI.

The company describes MAI-Cyber-1-Flash as a model that’s built “to find challenging vulnerabilities in complex codebases.” The model is built to animate MDASH, Microsoft’s harness dedicated to software vulnerability identification and remediation.

The new security platform is dubbed Perception, and it’s designed to deploy teams of agents to assist with and automate various security workflows, including identifying and remediating bugs. The platform can also integrate with MDASH.

The company claims MAI-Cyber-1-Flash is significantly more powerful (and more cost-effective) than competitor models, based on its performance on an established AI cybersecurity benchmark.

“We’re very very excited to announce our results,” said Mustafa Suleyman, the co-founder of DeepMind and current CEO of Microsoft AI. “We have MAI-1 Cyber Flash binded [sic] with GPT 5.4 inside of the MDASH harness — which beats out Gemini, GPT 5.5 Cyber, GPT 5.6 Sol, and Mythos 5 on Cyber Gym, which is the primary benchmark that we all use. The golden benchmark.”

“We’re shipping this into production immediately,” he added.

Noting that hackers are increasingly using AI in their cyberattacks, Hayete Gallot, Microsoft’s vice president for security, described Perception as a way for enterprise defenders to “defend against AI with AI at the scale and speed that the attackers have.”

Perception uses agentic red teams, blue teams, and green teams. The red teams can provide detailed simulations of potential attacks — providing context about potential threat actors and the likely vulnerabilities that they might exploit. Blue teams are dedicated to detecting and triaging existing bugs, while green teams take “corrective actions” against those bugs.

Dave Weston, the lead engineer for Perception, described the platform as a massive efficiency upgrade for corporate defenders. “We’ve gone from this taking hours and hours of manual work from multiple specialized folks across the security organization — appsec hunters, remediation engineers, you name it — and in minutes, we have a fix for all of this. Not only do we discover the issues and prioritize them, but we have detection, posture fixing, and even a code fix.”

Though AI has offered new defensive capabilities to companies, its availability to cybercriminals has given rise to a dazzling array of potential threats.

Microsoft’s new security tools, which the company said will be available in preview on November 3, will enter an increasingly crowded field of AI cybersecurity solutions. Earlier this year, Anthropic launched Mythos, a security platform that was released to a small coterie of partner organizations through a program called Glasswing. OpenAI has also launched its own security solution in May through a program called Day Break.