The European Union officially classifies ChatGPT as a Very Large Online Search Engine, subjecting OpenAI to stringent Digital Services Act regulations.
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Federal court records show how far the FBI’s Pegasus review progressed — and why new US spyware reporting will still leave major gaps in government hacking transparency.
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Meta must pay $567 million and change Facebook and Instagram protections for minors under a New Mexico court order it plans to appeal.
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Meta is under renewed scrutiny after researchers found thousands of AI "nudify" ads on Facebook and Instagram, raising questions about the company's advertising enforcement.
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The AI Kill Switch Act would let DHS order developers to slow or shut down frontier models after catastrophic incidents, raising enterprise continuity concerns.
The post House Lawmakers Propose Mandatory Kill Switches for Frontier AI Systems appeared first on TechRepublic.
France will ban social media for children under 15, requiring nationwide age checks and raising privacy and platform compliance concerns.
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Trump administration AI testing chief Chris Fall resigned after just 90 days, leaving CAISI under interim leadership during a critical period for AI safety standards.
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The FCC has proposed requiring identity verification for phone activation, a move supporters say will fight fraud while critics warn it threatens privacy.
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The CMMC assessment pause doesn't remove cybersecurity obligations. Here's why defense contractors should treat it as an opportunity to strengthen governance and AI oversight.
The post CMMC Assessment Pause Leaves Defense Contractors Facing a New Risk appeared first on TechRepublic.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.
Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence.
Local data center opposition is grounded in legitimate concerns about misallocation of land resources when housing is at a premium, pressures on already higher energy prices, and localized environmental impact. Unlike other resource-consuming and polluting industrial facilities, data centers produce very few jobs. The fact that US opposition to data centers seems to be most fierce among lower-income communities reflects righteous indignation with an inequitable bargain, where tech companies and developers profit from exploiting local resources but offer little in return. On a global scale, their carbon footprint could grow unsustainably if usage accelerates. And all this is in aid of a technology that many fear will propagate misinformation, take their jobs, or even cause existential risks for humanity.
For some, data center opposition may feel like the only tangible mechanism for registering their concern, disapproval, or even anger about AI. The problem is that this may be exactly what the AI companies are banking on. They can overcome the protest when it matters to them, and live with a significant fraction of proposals being defeated. More importantly, focusing political opponents on the data center issue obscures the bigger prize they’re after.
While there is a staggering three-quarters of a trillion dollars being spent on data center infrastructure by US companies this year alone, this investment should be taken in perspective. The market for enterprise software, for example, is about twice this size. And it’s small compared with what these companies actually want.
AI companies have their eyes set on capturing all the value created by entire industries. The technology has arguably already conquered customer service and consumer sales. But on the horizon are bigger targets, such as enterprise software development, creative design, management and even legal services. In AI companies and their allies’ vision of the future, AI replaces teachers and doctors. The companies would rather spend time fighting resistance to how fast they are building computing infrastructure than dealing with issues of how their products should be used in those fields, or how those fields should be protected from their products.
And while data center opposition campaigns have been successful in building widespread appeal, their effectiveness in the US is mixed. They seem to be most successful when organizing against speculative, early-stage data center proposals that have a relatively low likelihood to ever see fruition. Meanwhile, advanced-stage, well-capitalized data center projects have proven to have the resources to overcome local opposition. An OpenAI- and Oracle-backed facility in Saline township, Michigan, is breaking ground on construction even after local officials voted to reject it. The developers sued the town of 3,000 and forced a settlement that involved their project going forward. Meanwhile, the Trump administration, a vigorous ally of corporate AI, has signaled its willingness to advance AI infrastructure development by overriding state objections and even using federal lands.
Also consider that rampant data center development may be a momentary spike rather than a longstanding concern. Demand for the centralized computing that data centers provide may well decline over time. The leading Chinese labs, such as Z.ai, are innovating in technical mechanisms to make frontier-class models smaller and cheaper to run. AI power users have become adept at miniaturizing open weight models, ones published free for anyone to download and use, to run locally on their own computers. Apple and Google both support infrastructure stacks for running AI models directly on mobile phones. It could be that the current mania for data centers will look like the fiber optic cable bubble from the early 2000s, as demand shifts to smaller models and AI usage on people’s own devices.
For those concerned primarily with affordability and environmental protection, singling out data center construction is misplaced. Energy rates and inflation today seem to be most visibly affected by the US-Iran war. The US is disinvesting in long-term energy security by ceding the renewable energy industry to China and actively cancelling climate commitments. Consider that 10% of global carbon emissions stem from heating buildings, which dwarfs energy use by AI and could be cut fivefold by using heat pumps powered by renewable energy. With respect to housing affordability, federal housing subsidies have changed little over three decades, in inflation-adjusted terms, even as housing costs have spiked and homeowners have enjoyed robust tax incentives.
As for AI itself, the concentration of power and wealth in these tech companies is the greatest existential risk facing society today. This means we must limit corporate power, especially corporations’ ability to exploit the public and manipulate our political system.
Opposing data centers should be just a starting point. We can advocate for states to regulate AI, to reject irresponsible uses of the technology, and shape corporate behavior. We can fight for AI computation to be taxed, so that the public can capture some of the profit of AI use while also forcing AI companies to internalize more of the energy and environmental consequences associated with its use. And we all can join the global movement for Public AI, an alternative ecosystem for AI that is developed under public control with an incentive structure to create public benefit rather than private profit.
The US midterm elections present ample opportunity for those seeking to control the AI political agenda. In the recent New York congressional Democratic primary, PACs linked to the dueling AI companies Anthropic and OpenAI spent millions of dollars lobbying for or against “AI safety“, the idea that we must urgently monitor and prevent people from using AI to cause catastrophic harms. We’re already seeing a similar dynamic play out in races in Massachusetts and other states.
Why would Anthropic and OpenAI—bitter industry rivals but fundamentally on the same side politically—support opposing viewpoints? Because they both ultimately profit from the mystique: the idea that their products are so powerful that controlling those products is the world’s most important challenge. Here’s the typical read on the dynamic. To one side (backed by OpenAI affiliates), “safety” comes from the appearance of US industry dominating AI innovation, under the slow-moving control of federal lawmakers (and without pesky state regulators in the way). To the other side (backed by Anthropic), “safety” means a heavier regulatory framework that plays to Anthropic’s posturing as the ethics- and compliance-focused AI vendor. In both cases, it’s more marketing than principled concern about safety.
Political organizers should call out and reject the AI companies’ framing of the debate, and reorient campaign agendas around populist resistance to corporate concentration of wealth and power. When AI companies pump millions into legislative races, the result should not be hyperbolic discussion of AI superintelligence. And when a plot of land in a small town is pitched as a data center site, the debate should be about more than the local costs and benefits. It should include out-of-control money in politics, and Citizens United-proof solutions to limit corporate influence like public financing and state regulation.
We all have a vested interest in what’s on the policy agenda, and what the outcomes are. Today, the greatest risk AI poses to society is the exacerbation of inequality and the concentration of wealth. The real problem is trillion-dollar AI companies and their trillionaire oligarchs cozying up to political power in Washington and governments worldwide, and using their money to enact their agenda over the popular will of the people. This is the issue we’d like to see put front and center, and it requires solutions much more extensive than slowing data center development.
New York will ban Meta and other smart glasses from all 1,240 state courts starting July 20 over privacy and recording concerns.
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See what you missed in Daily Tech Insider from June 29–July 2.
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Reliance’s AI roadmap puts Jio CallAgent inside the telecom network while tying India-scale AI ambitions to Jamnagar compute, local-language services, and enterprise compliance questions.
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See what you missed in Daily Tech Insider from June 15–18.
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On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI.
Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more.
Consider these examples. The Health and Human Services’ (HHS) office of administration for children and families hired the world’s “scariest AI company,” Palantir—notorious for its work on behalf of the military, the CIA and ICE—to scan all grant applications to flag those not ideologically aligned with the administration’s dictates. The Federal Bureau of Prisons is developing an AI system to assess the “potential for misconduct for newly admitted inmates,” routing people into high-security confinement before they have actually done anything wrong in their custody. These read like programs fit for a Philip K Dick or George Orwell novel.
Other use cases insert AI into life-and-death decision making. The Department of Veterans Affairs is developing an AI that will listen in on calls to the veterans crisis line, and then gather information from external databases to assess the mental state and suicide risk of the caller.
The Department of Energy is testing the use of AI to control nuclear reactors, targeting a way to autonomously respond to potential nuclear safety incidents. Here’s one that’s disturbing for its retirement, rather than its deployment: the state department has ended a program to use AI to forecast mass civilian killings, which had been intended to aid conflict prevention.
While it’s easy to raise questions about these and similar uses of AI, the reality is that any of these programs could be implemented responsibly. In some cases, like the HHS system, the AI might be enforcing alignment to a policy prescription that opponents abhor. But that concern is more about the policy itself rather than the idea that agencies should comply with executive orders.
In other cases, there may even be bipartisan agreement on the goal, like taking urgent action to help veterans at risk of self-harm. Lots of work and validation is needed to prove AI safe and effective for these use cases and convince the public it is appropriate, but the idea is plausible.
In other cases, a scary-sounding AI use may not even be new. The use of predictive methods and statistics to assign prisoner security classifications goes back decades, even if such systems are often biased and ineffective.
Using autonomous systems for model predictive control (MPC) of nuclear reactors is a well studied, and a widely applied aspect of nuclear plant management. And the recently disclosed addition of AI was initiated under the Biden administration.
But anyone reviewing the 2025 inventory could be forgiven for leaping to severe conclusions. What matters are the details of how the AI system is used, and here the inventory is severely lacking.
The disclosures carry minimal information, and lack the context necessary to understand their purpose and approach. The descriptions are typically just a sentence, and rarely more than a paragraph.
And while the process theoretically involves some form of public consultation, in reality there is generally none. It would take an eagle-eyed citizen to even come across this disclosure. Unless you read FedScoop regularly, or watch the OMB’s federal chief information officer’s GitHub account, you probably missed it.
Only one of the examples cited above (the DoJ) even proposes to involve the public. Under the administration’s policy, it’s not required for the rest because they are not classified as “high impact” use cases—a label that is applied inconsistently across agencies.
We wrote a book surveying applications of AI to democratic processes worldwide, including executive agencies as well as the courts, legislatures and politics. Our conclusion was that, while there are inappropriate applications of AI in governance that should be resisted, an urgent need to reform the economics of AI, and an imperative for renovating the democratic systems it is being unleashed on, there are also valuable and beneficial use cases for AI in government.
Machine translation is a good example. Customs and Border Protection (CBP) has deployed an AI translation system to help officers when human interpreters are not available. The idea that CBP, an agency under heavy scrutiny for reported abuses of human rights, would direct people to talk to a machine instead of a person may strike many as inhumane.
It’s true that human interpreters have very real advantages when it comes to understanding nuance from physical cues and social context. But an officer with a competent AI translator available immediately is better than one who cannot communicate with the person in front of them.
The Trump administration’s AI use case inventory has 70 such translation use cases, up from 58 in the Biden administration’s 2024 disclosure.
Disclosure of AI use cases could be a means to build public confidence and trust, but only if paired with consistent, meaningful public consultation. Washington DC and California are actively engaging the public to determine where and how it’s appropriate to use AI in government processes, or for government to regulate AI use in society.
Both have held public deliberations on this topic at a wide scale, using AI platforms. These examples demonstrate the potential for capturing broad-based public input to steer AI policy.
The international gold standard was arguably set by the French in 2016, via their Digital Republic Act. The law, itself informed by an online citizen consultation, requires all algorithms used to automate government administrative decisions to be subject to public records requests, to be appealable to a human reviewer, and to have mandatory notification of the use of automation to those affected by the decisions.
Canada offers another example of what more rigorous and participatory disclosure might look like. In 2025, they launched an AI use case registry, not unlike the US inventory. However, Canada also has a federal directive mandating a transparent risk-scoring and impact assessment process for automated systems that make administrative decisions about citizens.
That longstanding directive requires a detailed explanation of risks and benefits as well as consultation with certain stakeholders from the conception of the AI use case. The Canadian system could be improved; it could require a public comment period and an obligation for agencies to respond substantively to feedback before engaging in sensitive uses of AI.
AI offers real potential to improve the efficacy, efficiency and accessibility of government. But, equally, there is legitimate reason for public concern and distrust that can only be addressed through transparency and dialog. The US should adopt, at the federal and state level, algorithmic impact risk assessment procedures and public comment processes to facilitate a safe, trusted, equitable transformation of government agencies to take advantage of modern technology.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.
Last month, Anthropic made a remarkable announcement about its new model, Claude Mythos Preview: it was so good at finding security vulnerabilities in software that the company would not release it to the general public. Instead, it would only be available to a select group of companies to scan and fix their own software.
The announcement requires context—but it contained an essential truth.
While Anthropic’s model is really good at finding software vulnerabilities, so are other models. The UK’s AI Security Institute found that OpenAI’s GPT-5.5, already generally available, is comparable in capability. The company Aisle reproduced Anthropic’s published results with smaller, cheaper models.
At the same time, Anthropic’s refusal to publicly release its new model makes a virtue out of necessity. Mythos is very expensive to run, and the company doesn’t appear to have the resources for a general release. What better way to juice the company’s valuation than to hint at capabilities but not prove them, and then have others parrot their claims?
Nonetheless, the truth is scary. Modern generative AI systems—not just Anthropic’s, but OpenAI’s and other, open-source models—are getting really good at finding and exploiting vulnerabilities in software. And that has important ramifications for cybersecurity: on both the offense and the defense.
Attackers will use these capabilities to find, and automatically hack, vulnerabilities in systems of all kinds. They will be able to break into critical systems around the world, sometimes to plant ransomware and make money, sometimes to steal data for espionage purposes, and sometimes to control systems in times of hostility. This will make the world a much more dangerous, and more volatile, place.
But at the same time, defenders will use these same capabilities to find, and then patch, many of those same systems. For example, Mozilla used Mythos to find 271 vulnerabilities in Firefox. Those vulnerabilities have been fixed, and will never again be available to attackers. In the future, AIs automatically finding and fixing vulnerabilities in all software will be a normal part of the development process, which will result in much more secure software.
Of course, it’s not that simple. We should expect a deluge of both attackers using newly found vulnerabilities to break into systems, and at the same time much more frequent software updates for every app and device we use. But lots of systems aren’t patchable, and many systems that are don’t get patched, meaning that many vulnerabilities will stick around. And it does seem that finding and exploiting is easier than finding and fixing. All of this points to a more dangerous short-term future. Organizations will need to adapt their security to this new reality.
But it’s the long term that we need to focus on. Mythos isn’t unique, but it’s more capable than many models that have come before. And it’s less capable than models that will come after. AIs are much better at writing software than they were just six months ago. There’s every reason to believe that they will continue to get better, which means that they will get better at writing more secure software. The endgame gives AI-enhanced defenders advantages over AI-enhanced attackers.
Even more interesting are the broader implications. The same searching, pattern-matching and reasoning capabilities that make these models so good at analyzing software almost certainly apply to similar systems. The tax code isn’t computer code, but it’s a series of algorithms with inputs and outputs. It has vulnerabilities; we call them tax loopholes. It has exploits; we call them tax avoidance strategies. And it has black hat hackers: attorneys and accountants.
Just as these models are finding hundreds of vulnerabilities in complex software systems, we should expect them to be equally effective at finding many new and undiscovered tax loopholes. I am confident that the major investment banks are working on this right now, in secret. They’ve fed AI the tax code of the US, or the UK, or maybe every industrialized country, and tasked the system with looking for money-saving strategies. How many tax loopholes will those AIs find? Ten? One hundred? One thousand? The Double Dutch Irish Sandwich is a tax loophole that involves multiple different tax jurisdictions. Can AIs find loopholes even more complex? We have no idea.
Sure, the AIs will come up with a bunch of tricks that won’t work, but that’s where those attorneys and accountants come in—to verify, and then justify, the loopholes. And then to market them to their wealthy clients.
As goes the tax code, so goes any other complex system of rules and strategies. These models could be tasked with finding loopholes in environmental rules, or food and safety rules—anywhere there are complex regulatory systems and powerful people who want to evade those rules.
The results will be much worse than insecure computers. Tax loopholes result in less revenue collected by governments, and regulatory loopholes allow the powerful to skirt the rules, both of which have all sorts of social ramifications. And while software vendors can patch their systems in days, it generally takes years for a country to amend its tax code. And that process is political, with lobbyists pressuring legislators not to patch. Just look at the carried interest loophole, a US tax dodge that has been exploited for decades. Various administrations have tried to close the vulnerability, but legislators just can’t seem to resist lobbyists long enough to patch it.
AI technologies are poised to remake much of society. Just as the industrial revolution gave humans the ability to consume calories outside of their bodies at scale, the AI revolution will give humans the ability to perform cognitive tasks outside of their bodies at scale. Our systems aren’t designed for that; they’re designed for more human paces of cognition. We’re seeing it right now in the deluge of software vulnerabilities that these models are finding and exploiting. And we will soon see it in a deluge of vulnerabilities in all sorts of other systems of rules. Adapting to this new reality will be hard, but we don’t have any choice.
This essay originally appeared in The Guardian.
As these systems move from "pilot" to "permanent," are you more concerned about the erosion of the physician-patient relationship or the potential for hidden economic "steering" within the algorithms?
The post The Robot Will See You Now appeared first on Security Boulevard.
Bank Negara Malaysia’s updated RMiT framework introduces stricter authentication rules for banks and fintech apps. Learn how passkeys, adaptive MFA, device binding, and risk-based authentication help meet compliance.
The post Bank Negara Malaysia RMiT Update: New Authentication Rules for Fintech and Banks appeared first on Security Boulevard.
The EU's Cyber Resilience Act (Regulation 2024/2847) shifts cybersecurity responsibility upstream. Explore the March 2026 guidance on secure-by-design requirements, software bills of materials (SBOM), and the impact on U.S. manufacturers.
The post The EU CRA – Treating Cybersecurity as Product Liability appeared first on Security Boulevard.
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