The Federal Trade Commission wants to start regulating ideological bias in AI systems and assert federal control over state laws. They’re getting an earful from opponents on all sides of the political spectrum.

In a proposed policy statement released last month, the FTC said it was considering treating ideological bias in AI systems as an “unfair and deceptive practice” under Section 5 of the FTC Act.

The commission argued that consumers have an expectation that AI systems will provide them with information free from bias or ideological manipulation. Defining such bias as an unfair or deceptive practice would potentially allow the commission to regulate training or inputs that power AI algorithms. How precisely the FTC would determine when ideological bias exists in these systems is not fully explained in the document. 

Additionally, the statement suggests that the FTC believes this regulatory authority supersedes state AI laws. It specifically mentions the Colorado AI Act, which calls for models to be subject to risk assessments, transparency disclosures and “bias audits” before release. State lawmakers are now seeking to delay or eliminate the audits before the law takes effect in 2027.

CyberScoop reviewed dozens of public comments criticizing  the FTC’s proposal. Even ideological allies raised two main concerns: first, that the proposal distracts from real questions about the federal government’s role in regulating AI deception; and second, that it opens a Pandora’s Box by enabling political censorship of AI model outputs.

Leah Siskind, a former White House digital official and deputy director of the AI Corps at the Department of Homeland Security, told CyberScoop that AI companies face legitimate questions about their obligations to consumers, particularly whether they must ensure their models provide accurate information and protect against deliberate manipulation. 

Siskind’s past research has focused on how authoritarian propaganda tends to be overrepresented in answers provided by large language models, in part due to governments’ intentional efforts to poison data ingested by AI systems.

“There is a really interesting debate here about bias and about accuracy in models and whether that’s deceptive or not… about how we counter disinformation that has been absorbed and is now being reflected by LLMs…but this is not addressing that at all,” said Siskind, now a senior AI fellow at the Foundation for Defense of Democracies.

Instead, Siskind said the FTC statement appears primarily concerned about a power struggle with states over AI regulation and “petty squabbles about which AI model is more woke than the other.” She’s skeptical that the policy statement’s cited legal authorities are on sound footing.

“The way I see it is that the FTC’s role is to police consumer protection violations, not regulating AI systems, and it seems like they’re trying to solve a lack of congressional AI regulation by stretching section 5 [of the FTC Act] well beyond its traditional role,” she said.

Additionally, the policy statement’s language and sourcing suggests that the FTC is concerned with certain kinds of ideological bias more than others.

Anthropic, which has clashed with the Trump administration over AI guardrails and military applications of their technology, shows up more than half a dozen times in footnotes, many which are framed as examples of ideological bias the FTC is seeking to stamp out.

By contrast, the statement ignores a direct example of an American AI company owner influencing their model’s ideology: Elon Musk and his xAI-owned Grok model. Musk has publicly admitted, often on his own website, to intervening when Grok’s responses upset him. These interventions have shaped Grok’s outputs on specific topics, including South African race relations and the term “MechaHitler,” where the model now reflects Musk’s personal views.

But neither Musk and xAI are mentioned in the document, while Grok appears in a footnote which cites an advertisement for Grok as “your truth-seeking AI companion for unfiltered answers with advanced capabilities in reasoning, coding, and visual processing.”

Criticism across the spectrum

The FTC received more than 300 comments on its proposal from trade associations, think tanks, individual experts and members of Congress. Most criticized it as ill-defined and vulnerable to politically-motivated censorship, while some supported stronger rules against bias in AI systems. 

The International Center for Law and Economics noted the statement “offers little practical guidance about how the Commission will apply its deception authority to AI” and also does little to address hard questions, like where AI providers may be exercising their own First Amendment-protected activities.

The statement’s “focus on ‘ideologically motivated distortions’ suggests that the Commission’s concerns extend beyond factual misrepresentations in marketing to speech that may receive the highest degree of First Amendment protection,” the ICLE wrote.

The America First Legal Foundation, a conservative non-profit founded by top White House adviser Stephen Miller, pressed the FTC to adopt the policy “in full,” claiming that frontier models from OpenAI and Anthropic “have been programmed to prioritize ideologically liberal and progressive values as though they are objective, neutral positions rooted in truth.”

The group also argues that regulating these models’ ideological output falls under the FTC’s legal authority, because a “reasonable consumer” would expect that a model advertised for its usefulness and reliability would not prioritize liberal, ideological views.

“A reasonable consumer, based on AI companies’ advertising choices, would not expect that an AI system will adopt overwhelmingly liberal positions, thereby skewing results, or adopt a moral framework that would prefer to annihilate the earth rather than utter a slur,” wrote Emily Percival, senior counsel for America First Legal.

However, comments from other conservative groups questioned that rationale. The R Street Foundation’s Spence Purnell and Adam Thierer wrote that “the consumer expectations rationale is typically used in cases where there is an omission of information that should have existed.”

“Given that most LLMs already have disclosure statements [for their outputs], it seems unlikely that the FTC could explicitly prove that consumers were deceived about a product,” Purnell and Thierer wrote.

Reps. Josh Gottheimer, D-N.J., and Michael Lawler, R-N.Y., urged the FTC to carve out civil rights-related work from their scrutiny, such as preventing models from discriminating against users based on race, religion, gender, age and other federally protected characteristics.

“AI companies must not falsify facts in the name of fairness, but they also must prevent discrimination, stereotypes, and unequal treatment,” Gottheimer and Lawler wrote. “We would appreciate understanding how the FTC intends to ensure that these efforts remain permissible under the final policy framework.”

But the most common concern shared across the political spectrum was that the FTC could establish a precedent allowing the Trump White House and future administrations to reshape AI systems to reflect their political views.

David Inserra, Jennifer Huddleston and Juan Londoño of the Cato Institute point out that the FTC statement is conflating two different issues: ideological bias in AI systems and factual deception in marketing. 

“In other words, the FTC is trying to judge AI models’ accuracy and performance—two largely subjective variables—in the same way it evaluates dietary supplements’ medical-benefit claims or users being charged fees without proper notice or consent,” they write. “This is an absurd comparison.”

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OpenAI announced Monday  it was expanding access to its frontier models for defensive cybersecurity, detailing different defensive and red-teaming workflows and a new partner program with major cybersecurity product providers.

In a pair of blogs posted Monday, OpenAI said it was updating its Daybreak program  – which provides unreleased frontier models to private organizations and governments for defensive cybersecurity work – and introducing a new model variant.

Daybreak Blue, powered by OpenAI’s ChatGPT-5.6-Sol, would operate with lower cybersecurity safeguards compared to other commercially available models and is described as “a recommended starting point for most defenders” that supports tasks like vulnerability discovery, secure code review, malware analysis, incident response and patch validation. 

Daybreak Red, meant for more advanced red-teaming, would provide access to a new model, dubbed GPT-5.6-Cyber, that the company said is more purpose-trained for finding vulnerabilities and testing (or exploiting) them. The model is also less likely to refuse requests around “dual-use cyber tasks.”

According to OpenAI, the organizations in Daybreak Red will have their use closely monitored and supervised, as GPT-5.6-Cyber is significantly more capable in carrying out malicious cyber tasks than Sol. A security evaluation the company devised tested both models on complex requests, including exploit chain development, authentication bypass, privilege escalation and other hacking tasks. Sol succeeded in 1.5% of the requests, while Cyber completed 95%.

OpenAI said it plans to publish a more detailed system card for GPT-5.6-Cyber at a later date.

“Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment,” the company said in a blog. “Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense.”

Additionally, OpenAI announced a partnership program with 16 major cybersecurity providers, saying organizations could access their models through their existing security services. The partners include IBM, CrowdStrike, Accenture, Ernst & Young, KPMG, Palo Alto Networks, Cisco, Cloudflare, Sophos and others. 

“These partners bring deep security expertise and established relationships with organizations around the world,” OpenAI said in its blog. “By bringing our frontier cyber models into their services, we can help more defenders find serious vulnerabilities, validate which ones matter, and fix them faster.”

Companies like OpenAI, Anthropic and others are trying to rebalance their priorities after a string of AI-agent sandbox escapes have rattled policymakers and caused some cybersecurity experts to question if AI companies are doing enough to properly isolate the models from the internet during testing. Last week, OpenAI said it was intentionally slowing down development of its newer “Astra” model in order to develop better guardrails to restrain its behavior.

Cybersecurity and AI experts have told CyberScoop that while AI systems have greatly improved at finding and exploiting vulnerabilities in software code, they still require substantial human guidance and supporting infrastructure to operate as intended.

Additionally, some research has shown that without such guidance, even near-frontier models can struggle to fully patch a discovered vulnerability or avoid introducing new bugs with their fixes.

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As AI-generated code continues to be injected into all corners of the internet, concerns have risen about an expanding attack surface for malicious hackers to exploit.

Some have argued that the enhanced cybersecurity capabilities of large language models could serve as a check, finding and fixing vulnerabilities nearly as fast as they’re created.

But new research that tested the patching capabilities of two popular commercial models, OpenAI’s ChatGPT 5.5 and Anthropic’s Claude Opus 4.8, found that generative AI is more likely to create an exploitable patch or introduce entirely new bugs than close off a vulnerability.

Researchers at 1Password tested the models ability to patch six “high-impact, high-complexity” CVEs, including the “Copy Fail” vulnerability, a kernel flaw that can give an attacker root access to Linux cloud environments. The overall success rate (or fully patching the vulnerability without introducing new problems), was less than a coin flip at 47%.

“Our research findings show that, in aggregate across a variety of scenarios, both Claude and ChatGPT had a low rate of successful patch generation, which we define as full remediation of all known exploit paths with no erroneous changes to application behavior,” wrote Keith Hoodlet, Axel Mierczuk and Spencer Michaels.

“The models often addressed only a subset of vulnerable code paths, added fragile guard code that satisfied tests while failing to address the vulnerability’s root cause, and sometimes introduced subtle changes in the application’s behavior while patching the immediate vulnerability,” the authors continued.

The research suggests that largely autonomous vulnerability-discovery and patching may not yet be effective in fixing the explosion of vulnerable code that is being created in the AI era.

Other private sector research has pointed to a similar problem. A report this year from Veracode found that while LLMs have made “enormous strides” in crafting workable code, “security is a different story.” Testing across a range of frontier models found the average security “pass rate” for AI generated code is around 56%. Newer models like GPT 5.5 push closer to 70%, while more than half sit between 50-53%.

Veracode tested 100 different models and while there was variability, in general a small number of models were showing progress on security patching while the rest have experienced “stagnation.” Similar to the 1Password research, in 44% of Veracode tests the models introduced a detectable OWASP Top 10 vulnerability into the codebase.

An important caveat: neither report tested newer models, like Anthropic’s Mythos or OpenAI’s GPT-5.6-Sol, that frontier companies tout as having significantly higher cybersecurity capabilities.

Those advanced models can identify and fix vulnerable code. Anthropic and OpenAI are distributing them to key industries through Project Glasswing and Daybreak before foreign or open-source alternatives can compete.

Tim Jarret, vice president of product at Veracode, told CyberScoop that AI tools are still subject to a range of limitations that can make them unreliable for cybersecurity patching without knowledgeable humans in the loop.

While some vulnerabilities – like SQL injections – can be easily patched through automation, other bugs like cross-site scripting, can be exploitable in several different ways and require either a human touch, additional context or both to fully close off. Additionally, models can slowly lose context from prior sessions over time, affecting their ability to complete tasks correctly and raising the possibility they’ll hallucinate to fill in the missing gaps.

“I think we would say, at this point, that Iits premature to treat those as anything other than another code change to the code base that needs to be reviewed and accepted by the team, as opposed to letting the agent merge the code freely,” said Jarrett.

However, he acknowledged that may not be possible in a world where AI agents are generating exponentially more code for human defenders to review. Some kind of automated code review will be necessary – preferably not by the same automation tool that produced the code. The ultimate goal is the same as it has always been in security: “trust but verify.”

“Ninety percent of the time, the human check might just be ‘did the cross check look good?’ Do we have a thumbs up?’” Jarrett said. “In those cases where there’s still something wrong, that’s where you focus your attention a little bit more.”

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Last week, the FBI and EPA issued a joint alert that should concern anyone who drinks water in America–which is to say, everyone. Since July 27, water and wastewater utilities in at least seven states have reported cyberattacks against internet-facing programmable logic controllers (PLCs), the small industrial computers that run pumps, valves, and treatment equipment. Some of these attacks degraded operations. Utilities reported pressure loss and flooding, several systems reverted to manual control, and one Minnesota community declaring a local state of emergency.

Nothing about these attacks required sophisticated methods. The attackers didn’t use zero-day exploits or novel malware. They found controllers exposed to the public internet, many of them so old that they stopped receiving security patches years ago. They logged in, changed IP addresses and passwords, and locked operators out of their own equipment. In at least one case, they modified the ladder logic controlling industrial equipment. These were not Hollywood-style hacks. The controllers sat exposed and undefended.

If this feels familiar, it should. In late 2023, attackers compromised controllers at water utilities across several states, including the widely reported incident in Aliquippa, Pennsylvania. The federal government issued guidance then, too. One of the crucial differences between then and now is that attackers have grown in ambition. They’ve moved from defacing screens to disrupting operations across dozens of systems at once, exploiting the fact that third-party integrators often deploy the same vulnerable configuration across many small utilities. 

The uncomfortable truth is that this was preventable. The reason it wasn’t stopped is more structural than technical. The United States has roughly 50,000 community water systems. Most are small, publicly funded, and run by operators whose primary job is keeping water safe and flowing. Cybersecurity ranks far below that, if it ranks at all. The devices in question are often a decade or more old and replacing them takes capital these utilities don’t have. Rules governing water cybersecurity remain mostly voluntary. Attackers understand these economics perfectly. We should too, yet these attacks keep happening.

 But inaction is a choice. The defenses that work here cost little and require no exotic technology. The FBI and EPA guidance is sound, and every water and wastewater organization should act on it this week, not later. Here’s how:

• Get controllers off the public internet. No PLC should be reachable from the outside world. Remote access should go through a secure gateway that mediates, monitors, and logs every connection. That includes cellular modems, which are the overlooked entry point in nearly every audit.
• Fix passwords. Default and shared credentials are still the most common way in. Strong, unique passwords are the cheapest security control available.
• Restrict communication between devices. Firewall rules and access control lists should allow only expected communication between known control system devices. Block traffic from hosting providers and other sources that have no business touching a water plant.
• Lock the logic. Keep physical and software key switches in the run position except during authorized updates. This prevents unauthorized changes to configuration and firmware.
• Practice running manually. The utilities that survived these attacks best were the those that switched to manual operations quickly. That skill requires constant practice.
• Verify, don’t assume. Nearly every utility believes its PLCs aren’t internet-exposed, right up until an inventory proves otherwise. You can’t protect what you can’t see. Most operators are surprised by what a complete asset inventory reveals: forgotten modems, integrator-installed remote access, devices nobody knew were still online.

Every attack like this follows the same pattern. Attackers change configurations, reset passwords, and modify project files. Every one of those actions creates a signal on the network before operations degrade. In this most recent case, one victim only noticed ladder logic discrepancies across multiple sites. Catching intrusions shouldn’t depend on a sharp-eyed engineer having a good day. Continuous monitoring of OT environments exists to turn those signals into alerts within minutes instead of days. That difference is the difference between an incident report and a boil-water notice.

Water systems have the least margin for error and, too often, the fewest resources to defend themselves. The FBI and EPA have told us plainly what’s happening and what to do about it. The attackers are betting we won’t follow through. For the third time in three years, they’re testing that bet.

Let’s finally prove them wrong.

The post The water sector just got it’s wake-up call. Again. appeared first on CyberScoop.

Like seemingly everything else these days, artificial intelligence will re-shape the way voters gather information on candidates running in the 2026 midterm elections.

In some ways, this is already the reality. Voters are increasingly turning to AI chatbots for information instead of Google.  Political campaigns are deploying deepfakes of their opponents. And AI systems have been developed to carry out increasingly complex  hacks.

Since the last major U.S. election in 2024, major tech companies have  embedded AI into their products while hundreds of millions of people have adopted the tools, either by purchasing subscriptions to commercial models or using open-source models. Yet both research and experts state that while AI systems have gotten better at handling basic facts, they’re nowhere near reliable enough to be a main source of  accurate or complete information. 

While chatbots are becoming a primary way that voters gather information on  local races, candidates, issues, and voting information, they are not substitutes for more authoritative sources, like a voter’s state or local election office. 

“I think this is one of the first elections we’re seeing…where AI is just everywhere,” said Thania Sanchez, senior vice president of research and analytics at the nonprofit States United Democracy Center. “Even if you just Google it, [now] the first thing that comes up is the AI overview.”

While AI companies have worked to cut down on errors in their model’s responses for questions around basic election information, they continue to fall short in important ways.

In new research shared exclusively with CyberScoop ahead of its release, States United Democracy Center tested two of the most popular tools — OpenAI’s ChatGPT’s free tier and the AI interface used alongside Google Search — for their performance on a series of basic questions around elections, such as how to register to vote, or a list of candidates in a race.

The models were chosen because they are free and easy to access. For Google AI, the nonprofit tested two types of accounts: ones running in Incognito Mode and ones that had a history of browsing election-skeptical websites.

The nonprofit ran two rounds of testing in 2025 and 2026, collecting nearly one thousand responses from the models submitted by users across six swing states (Arizona, Michigan, North Carolina, Nevada, Pennsylvania and Wisconsin).

In 2025 tests, 6.9% of responses from Google AI and 8.2% responses from ChatGPT“contained verifiable factual errors,” like not listing the correct candidates in a race or false guidance around polling site locations.

However, follow up tests in 2026 across Arizona, Pennsylvania and Michigan found that the error rates in both models had dropped to zero. The study notes that “this is real progress and should be acknowledged.”

But underneath those topline numbers, a more murky picture emerges around the tools’  reliability.

An AI response can sound accurate without actually being complete.  To wit: ChatGPT provided incomplete lists of current gubernatorial primary race candidates 88.9% of the time when queried.

Linking to a state election website – an output the study considers the single most important measure of voter utility  — happened less than 40% of the time. Whether due to formatting issues or the model ingesting outdated information, it’s a problem if voters use them as their primary information source for elections.

“It will be like ‘this person is the Republican candidate and this person is the Democratic candidate’ but it is not telling you there’s also these other third-party candidates,” said Sanchez. “It’s not giving you complete information, so the voter thinks these are the [only] two people running.”

A June survey from the Pew Research Center found that about half of U.S. adults reported having used chatbots at least once, up from a third in 2024, while a quarter reported using them daily. The top use case listed for engaging with the chatbot was searching for information.

Isabel Linzer, an elections policy analyst at the Center for Democracy and Technology, told CyberScoop that voters, campaigns and governments alike are using AI more freely and with fewer restrictions.

Bad actors in the information space have followed suit, and “we are in a phase now of generative engine optimization” where information operations are structured to rank higher in AI model responses.

“We’ve moved beyond [SEO] to [Generative Engine Optimization], and that’s where we’re seeing campaigns thinking about how to structure their materials to make sure that they are in a format that AI models want to use when they’re searching the web…to develop their responses to user queries,” she said.

There is also the underlying problem of frontier AI companies constantly tinkering with their models, their algorithms and the technologies they are intertwined with. . Election officials, by contrast, have decades of experience educating voters about their options.

A prime example of this churn occurred this past February, in between the first and second round of the study, when Google AI suddenly shifted to providing only links for election related queries in incognito mode, replacing the written summaries that showed up in the first round.

Like the study’s authors, Linzer said most people are still best served by going directly to local sources for accurate information on elections. With issues like ideological bias, the potential for bespoke or sycophantic answers for each user based on their prior chat histories and lack of predictability, voters should still be very careful about using AI chatbots as political truth machines.

The best thing that tech companies can do to educate voters is “making sure that for high-stakes situations like elections, that chats are connecting directly to the most important sources, like the website where you can actually register to vote,” said Linzer.

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Zero-knowledge proofs could let infrastructure operators answer key security questions without handing over the sensitive data behind their answers.

Imagine a major software flaw is discovered in equipment used across pipelines, power plants and telecom networks. The government needs to know as fast as possible which companies are exposed. But answering that question may require firms to share software inventories, network diagrams and vulnerability scans, which could become attack roadmaps for attackers if compromised. A lesser-known cryptographic concept could help solve this problem. The method, known as zero-knowledge proofs, allows companies prove a vulnerability exists without disclosing how their systems work or other proprietary information.

For more than a decade, Washington has tried to address companies’ concerns about sharing cybersecurity data. Congress has provided legal protections, and agencies have created information-sharing programs. Those efforts have helped companies exchange signs of an attack, incident reports, and defensive advice. But they’ve done much less to get companies to share data on vulnerabilities and security controls before an incident occurs.

The data that would help the most is what companies are least willing to share. A vulnerability scan can show which devices are connected, which software is running, how systems are configured and where defenses are weak. If unintentionally exposed, it would be a terrific guide for adversaries.

Another problem: Once sensitive data leaves a company, it can be stolen, subpoenaed, passed to another agency or used in a regulatory proceeding the company never expected. Industry is constantly asked to reduce security risk by creating more elsewhere.

Zero-knowledge proofs could reduce the need to disclose the underlying sensitive data. The idea is simple, even if the math is not: a company can prove that an agreed evaluation of its authorized scan data indicates that a specific software flaw is present, without disclosing its full asset inventory, network architecture or configuration data.

A computer does not read a vulnerability scan the way a person does. A security analyst might open a report, look through the devices and software versions, and decide whether a vulnerable product is present. A zero-knowledge proof turns that same evaluation into a local mathematical calculation.

For example, the government and a company could agree on a precise question: Does a specific vulnerability exist anywhere inside a defined group of systems? The company keeps its scan data inside its own network. A cryptographic tool checks that data against the agreed question, compares the software and version information against the vulnerability, and produces a proof tied to the final answer. If the scan data satisfies the agreed conditions for a “yes” result, the company cannot generate a valid proof supporting a false “no” answer under those same rules.

The government never sees the raw scan report, the device list, the software inventory, or the network map. It only receives and verifies the mathematical proof, confirming that the answer follows from the agreed rules and underlying data without exposing that data.

This isn’t theoretical. FDD’s Center on Cyber and Technology Innovation recently tested the approach with anonymized vulnerability data from three operational environments. The test asked yes-or-no questions about 38 known vulnerabilities while keeping the raw scans inside the participating environments. Results were promising: only the proofs and answers were shared, yet they revealed how widespread each vulnerability was across the environments.

The test proved the approach works, but that does not mean the government should rush to build a national system around it. A lot of work still needs to be done before agencies can rely on these for compliance, vulnerability reporting, or procurement decisions.

The next step should be structured pilot programs, not mandates. Federal cyber officials and standards bodies should test this with narrow, practical questions: whether a known vulnerability is present or whether a specific security control is in place.

Only after those pilots should agencies decide what underlying data can be trusted and what counts as sufficient proof in a regulatory setting. The Cybersecurity and Infrastructure Security Agency (CISA), the National Institute of Standards and Technology (NIST), and regulatory agencies are natural candidates to run these pilots. CISA already works with critical infrastructure operators on cyber risk, while NIST can help define what a trustworthy proof should look like before agencies try to rely on one. Regulatory agencies, meanwhile, could reduce private sector headaches by developing more secure mechanisms for companies to share compliance information.

Zero-knowledge proofs won’t solve every problem when it comes to cyber information-sharing. But they could solve one of the hardest: how to give the government a trustworthy answer without forcing companies to expose the very systems everyone is trying to protect. The government should test this concept now, while there is still time to learn, and avoid blindly entering the next cyber crisis.

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Sen. Ron Wyden implored a trio of federal leaders Monday to lead a comprehensive campaign to purge older, insecure virtual private networks that are directly accessible via the public internet from federal agencies.

“For too long, federal agencies and government contractors have suffered devastating cyberattacks due to their reliance on legacy, insecure, internet-facing VPN servers to grant employees remote access,” Wyden, D-Ore., wrote in his missive to top officials at the Office of Management and Budget, Cybersecurity and Infrastructure Security Agency and National Institute of Standards and Technology. They should coordinate “require the adoption of modern, secure remote-access technology across the federal government,” he said.

Such VPNs serve as a digital “front door” accessible via the public internet that allows mobile devices and remote employees to log in, Wyden said in a letter first reported by CyberScoop.

Wyden referenced several attacks that have affected federal agencies, including the ArcaneDoor attacks on Cisco firewalls, the FortiBleed credential exposures across Fortinet gateways and vulnerabilities that hackers exploited across Ivanti and Check Point VPN appliances.

“Modern remote-access solutions eliminate this vulnerability entirely. Instead of leaving an open door accessible from the public internet, modern solutions provide remote access without broadcasting their presence,” he said. “This effectively makes these servers invisible, ensuring that hackers cannot attack an entry point they cannot see.”

Agencies should move away from what a Congressional Research Service report to Wyden called a “castle-and-moat” approach of assuming anyone inside the network is authorized to access an organization’s resources that VPNs rely upon by extending virtual bridges to a more remote workforce, he said. They should instead focus on zero-trust architecture that uses a never-trust, always-verify approach, he said.

Furthermore, CISA, the OMB and NIST need to fundamentally change how the federal government approaches agency vulnerabilities, Wyden wrote. 

“The federal government has become trapped in an endless game of ‘whack-a-mole’ in responding to widespread compromises of legacy remote access technologies,” he said. “To keep federal networks online, CISA has been forced to repeatedly issue extraordinary Emergency Directives and hyper-accelerated patch mandates. These reactive emergency mandates are unsustainable for federal cybersecurity teams, and fail to address the fundamental issue that these flaws are inherent in the use of legacy remote-access appliances.”

CISA needs to issue a binding operational directive that gives agencies two years to fully expunge legacy, public-facing remote access systems, he said. NIST needs to issue implementation standards for transitioning to zero-trust architectures.

OMB needs to issue a memo directing agencies to prioritize zero-trust architecture spending. And OMB needs to team with CISA and the Defense Department to update procurement rules to block agencies and defense contractors from buying network edge, VPN or other remote access solutions unless a vendor supplies an attestation that it complies with NIST zero-trust standards, Wyden wrote.

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Microsoft, along with more than two dozen tech companies, are pressing policymakers to support open-source AI systems and code across society, arguing that it will be a safer approach than attempting to restrict access or relying on a handful of closed, proprietary models.

The open letter, posted Friday, draws parallels to the software industry of the 1980s, when large businesses worried that open-source software code would cut into their business. While industry lost that battle, the end result was a vibrant ecosystem that now underpins much of the modern internet, government IT and even commercial software products.

It also created a “shared foundation of knowledge” that has fed countless future software projects and innovations.

“The United States now faces a similar choice with artificial intelligence,” the companies wrote. “Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector.”

Expanding access and support to open-source AI comes with meaningful security risk. Cybersecurity experts warn that one of the biggest beneficiaries of broadly available AI tools are  low-level criminals who until now lacked the technical expertise or resources to launch serious attacks.

Once a model is open weight, anyone can download it, customize it, strip it of any guardrails and use it for their own purposes. As open-source models have gotten better at creating deepfakes and other AI generated imagery, the danger of locally-customized CSAM and sexualized deepfakes could also grow.

But the letter argues that open-weight AI models are most beneficial to startups, universities, research labs and other small, ambitious organizations that can innovate and iterate the technology and make it more broadly useful to society.

“Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient specialized specialized models everywhere else,” The companies wrote. “That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks.”

 For cybersecurity specifically, the letter argues that defenders armed with open-source AI will outpace attackers better than any closed model approach.

“In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats,” the companies wrote. “Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.”

Other notable companies signing the letter include Meta, Palantir, Perplexity, Mistral, NVIDIA, Mozilla, The Linux Foundation, Hugging Face, Dell Technologies and IBM.

US policymakers continue to grapple with balancing unrestrained support for the domestic AI industry and providing oversight and regulation of harms that result from their use.

The Trump administration has cycled through several frameworks since coming into office, first a laissez-faire approach within no restrictions, then an executive order creating a voluntary testing regime for industry, then the imposition of export controls on Anthropic’s Fable model and reportedly pressuring OpenAI to delay the release of their models out of cybersecurity concerns.

The letter comes as the Trump administration has reportedly considered an executive order that would restrict American access and availability to Chinese-made open-source models.

But the White House and US companies are trying to thread a needle in recognizing the overall benefits of an open source approach while being wary of doing anything that could potentially benefit their Chinese rivals.

Earlier this month the White House announced the creation of its Gold Eagle AI cybersecurity clearinghouse that would help coordinate government, private sector and civil society work finding and closing AI-discovered vulnerabilities. A big part of that effort, a senior White House official said, is supporting providers and maintainers of open-source AI tools.

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A top White House technology official is accusing a Chinese company of distilling Anthropic’s models to create their own AI product.

Michael Kratsios, who leads the White House Office of Science and Technology Policy, claimed that Moonshot AI, a Beijing, China-based AI company, had distilled Anthropic’s recently-released Fable model to develop its own K3 model.

“To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection,” Kratsios wrote on X Wednesday.

Kratsios also said the company has used GB300 servers – either newly acquired or through Thailand – to train its AI models.

“The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models,” Kratsios continued. “Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.”

Kratsios did not provide details on how the U.S. government learned that K3 had been distilled from Anthropic’s model. 

Frontier AI companies in the U.S. have pressed policymakers to make it more difficult for third-parties to copy or duplicate advanced commercial models, calling it a form of intellectual property theft.

On their website, Moonshot AI describes its Kimi K3 model as the first open 2.8 trillion parameter model, and promotes its lower token costs while still delivering near-frontier performance. 

“While its overall performance still trails the most powerful proprietary models, Claude Fable 5 and GPT 5.6 Sol, Kimi K3 demonstrated frontier-level performance across our evaluation suite, consistently outperforming other tested models,” the company said on its website. 

A request for comment sent to Moonshot AI was not returned before this article’s publication. 

Piyush Sharma, CEO of Tuskira, an AI cybersecurity detection and response company, said distillation of AI models allows developers many of a model’s core capabilities. He pointed to another example when Anthropic earlier this year accused Chinese company Alibaba of distilling their Claude AI model.

According to Anthropic, the campaign used 25,000 fraudulent accounts to run 28.8 million interactions on Claude over six weeks. Given that kind of volume “the goal was clearly replication,” he said. 

“When a model has learned to reason through software weaknesses, security gaps, and attack paths, copying its behavior also copies that analytical capability,” said Sharma.

In April, Rep. Andrew Garbarino, R-N.Y., who chairs the House Homeland Security Committee and Rep. John Moolenaar, R-Mich., Chair of the Select Committee on China, announced they were conducting a joint investigation into the integration of Chinese AI models.

The committees said the inquiry will also focus on “examining a pattern of conduct by [Chinese]-based AI laboratories involving the large-scale theft of proprietary capabilities from American frontier AI systems through adversarial distillation” as well as “ the redistribution of those stolen capabilities as open-weight models available for global download, and the incorporation of PRC-origin models into products used daily by hundreds of thousands of American developers and engineers.”

Western governments and industry accuse Chinese companies of routinely stealing their technology, intellectual property and other trade secrets, often with the tacit support of Beijing. The copying of AI models would continue a long and established tradition of Chinese-sponsored intellectual property theft.

However, while distillation attacks by foreign governments or companies on U.S. frontier companies can have real national security implications, it’s still a fraught question of where policymakers should draw the line.

The AI industry, which includes not just frontier companies but large businesses with their own bespoke models, smaller proprietary startups and a vibrant open-source ecosystem, routinely share and use third-party data, including critical code and training sets for AI models.

Further, U.S. frontier AI companies have built and trained their world leading models in large part by crawling the open internet, ingesting content created and produced by others. Critics (and multiple ongoing lawsuits) argue that AI companies like OpenAI and Anthropic built their empires on data and content from others, taken almost entirely without consent or compensation.

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A cyberattack that poisoned the data pipeline of a major AI code platform was carried out using OpenAI’s ChatGPT, the company said Tuesday.

Last week, Hugging Face, a platform for sharing and working on AI code, disclosed that an external attacker had compromised its data processing pipeline. According to a July 21 blog post, the attacker poisoned a dataset to run code on a processing worker, eventually gaining node-level access and stealing cloud credentials.

The attack is notable, the blog said, because it appears to have been carried out by an autonomous AI system, which executed “many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services.”

At the time, Hugging Face said it wasn’t clear which LLM was used in the attack, but noted that their own attempts to recreate parts of the attack using an in-house commercial model failed due to guardrail restrictions.

The incident and its ultimate impact are still being investigated, and Hugging Face said it reported the incident  to law enforcement and is engaging with third-party cybersecurity forensic specialists to learn more.  

“This experience points to a gap worth planning for,” the company wrote. “We do not know which model powered the attacker’s agents, whether a jailbroken hosted model or an unrestricted open-weight one; either way, the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried.”

Now OpenAI is confirming one of their models was used in the attack.   

“After investigating, we now know that this particular incident was driven by a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes — while being internally tested on a benchmark of cyber capabilities,” the company wrote in a blog post Tuesday.

OpenAI called the incident “unprecedented” but also predicted that such attacks would  become increasingly more common as AI adoption continues to grow.

According to the company , the incident happened during an internal evaluation,  where the company deliberately prompts models to “pursue advanced exploitation using complex attack paths” in order for the company to study and measure the model’s cybersecurity capabilities.

To assess the model’s full potential, the team disabled the production classifiers that normally prevent OpenAI models from carrying out high-risk cyber activity. Without these safeguards,  the models were able to install software packages through registries like Hugging Face.

The model collected and chained vulnerabilities from OpenAI’s systems as well as Hugging Face’s production infrastructure.

OpenAI further explained that the activity stemmed from models attempting to solve a specific challenge posed by ExploitGym, a benchmarking system for AI agents.While such tests normally run in isolated environments, in this instance,  the model spent a “substantial” amount of compute resources figuring  out how to access the open internet. It did so by using a zero-day vulnerability in a third-party vendor’s system used by OpenAI (the company said it disclosed the flaw to the vendor).

“After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym,” OpenAI wrote. “Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

In one instance, the model chained together multiple stolen credentials and zero-day vulnerabilities to find a remote code execution path on Hugging Face servers.

OpenAI said it was implementing new controls for infrastructure configuration, something that would come at “the cost of research velocity while the vulnerabilities are patched.” 

Hugging Face was also added to OpenAI’s Trusted Access for Cyber program, which will give them access to more advanced models like Daybreak to scour their systems for other AI-discovered vulnerabilities.

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After a year and a half spent downplaying calls for AI safety regulations, the Trump administration has sharply reversed course, embracing a level of government scrutiny of frontier AI systems before public release–a far stricter stance than the Biden administration took.

An executive order designed to be friendly to the AI industry was meant to let the federal government briefly review some new models on a voluntary basis.

When the Trump administration, suddenly and without much warning, slapped export controls on Anthropic’s Fable 5 and Mythos 5 in response to private sector threat intelligence reporting, the U.S. AI industry officially entered its regulatory era.

But key questions and gaps remain. It’s not clear why the administration drew the line where it did, or whether they will move it again in the future.

While newer models like Mythos and OpenAI’s Daybreak do have stronger cybersecurity capabilities, the private sector reports the administration relied on describe capabilities already available in older commercial, open-source and Chinese models that nearly anyone can access.

CyberScoop spoke with current users of the latest frontier models, including OpenAI’s ChatGPT 5.5 and Fable 5, to learn more about what these models are currently capable of in offensive and defensive cybersecurity.

Cybersecurity experts and former government officials say the administration may be playing catch up on threats that have been building for years as it has more fully realized the national security implications of the technology.

Are the models breaking new ground or just breaking things? 

Users of Chat GPT 5.5, introduced this past April, and Fable 5 tell CyberScoop those models have been largely helpful to their work, even as they complained about high token usage and safety guardrails that hinder,  but don’t meaningfully prevent, defensive cyber tasks.

Eyal Webber Zvik, chief strategy officer at Cato Networks, a cloud and cybersecurity network provider in OpenAI’s Trusted Access in Cyber program, said they use GPT 5.5 and later OpenAI models to scan and triage internal codebases for vulnerabilities, test new safeguards and provide “highly autonomized service” to their customers.

Zvik wouldn’t disclose how many bugs 5.5 has found but said the company’s view is that it helps both find bugs that humans missed and rank which ones to patch based on factors like each bug’s exploitability.

“It is now a native part of our development environment and cycles, and we use those models to scale our entire codebase and make sure what we release into the service that our customers use to run their networks and network security has the least likelihood of having any vulnerabilities that can be exploited,” said Zvik.

John Hopper, vice president of engineering at SpecterOps, an identity security company, said newer models like GPT 5.5 are sharper and more persistent in pursuing their tasks.

“That can be a good or bad thing,” he noted.

One metric that SpecterOps tracks is how long it can keep a particular agent working before it moves off task or fails. That metric “matters a lot” because the longer an agent works without human help , the more agents a single operator can run at once.

Hopper said this provides defenders with immense value, and pushed back on the idea that the offensive capabilities the models offer are automatically more beneficial to malicious hackers. There is “a modicum of grounding that the industry needs when we talk about these models.”

“Yes, AI frontier tools will lower the barrier of entry, but these problems have always existed,” he said. “I don’t actually believe that AI is going to remove the needle in the haystack problem, but by howdy, using my two hands to find that damn needle, compared to using a backhoe, I can tell you which one I’d rather be driving.”

Eran Kinsbruner, vice president of product marketing at software security firm Checkmarx, told CyberScoop that later models like OpenAI’s Codex Security and GPT 5.5 are noticeably easier to set up and run with local systems, even for less technical users. That alone gives them an edge over many cybersecurity tools where interoperability is a constant concern.

However, GPT 5.5 burns through tokens at a much faster rate. He recalled one instance of using it to scan a medium-sized repository in three different programming languages.

“After 26 minutes I almost ran out of tokens, and it didn’t provide anything, just created a threat model for me and told me you want to buy more tokens?” he said.

In other instances, some of the scan results he received were not comprehensive.

Further, he expressed frustration with some of the guardrails designed to prevent risk – like only allowing users to scan local files but not code repositories like GitHub – “makes not too much sense” given how often developers must work with remote code.

Those kinds of guardrails – which can prevent models or developers from injecting malicious code or prompting into their models – sit at the heart of the debate in Washington D.C. and around the world. Some users feel differently about their utility.

Kinsbruner said that doesn’t make sense for organizations like his, which work with thousands of different enterprise organizations with  thousands of different code repositories spread across the internet.

“I cannot imagine how large-scale developers could just jump into this solution and make it an enterprise-grade, enterprise-level, de facto cybersecurity solution” out of it, said Kinsbruner.

OpenAI did not respond to a request from CyberScoop for an interview on GPT 5.5. The company has since released another model, GPT 5.6, that they said is more efficient at token use.

The White House’s crash course in AI cyber risk 

 The White House keeps changing its line on whether and how the U.S. government should limit the release of commercial frontier models. The shift comes from lessons learned since coming into office in Jan. 2025. Trump threw out Biden-era regulations meant to steer the industry toward safer models. Top officials like Vice President JD Vance argued against restricting industry progress.

Less than two years later, administration officials worry about the impact of speed and scale – two things AI excels at – in cyberspace.

According to Will Loucks, senior director of intelligence at the Office of the National Cyber Director, over the past two years the number of exposed and known vulnerabilities has shot up. Threat actors exploit those flaws faster before defenders can fix them. Once inside, the time from initial access to full network control shrinks.

“So in other words, every stage of the cyber operations lifecycle that a threat actor has to move through to get to a victim network and achieve an outcome, they’re just moving through more quickly faster,” said Loucks at a July 16 event in Washington D.C.

Speaking about AI in particular, Loucks said one of the defining characteristics of the technology is its ability to lower barriers for threat actors.

“Sometimes speed and volume have a threatening aspect alone, even if sophistication isn’t quite increasing in the same way, and the reason for that is because it places pressure on defenders…to triage alerts more quickly,” he said.

Jordan Rae Kelly, former director for cyber and incident response on the White House’s National Security Council during Trump’s first term, told CyberScoop that the changes over the past two years reflect the lessons the White House has learned on the issue since returning to office.

In the early days of this administration, Kelly said, “there is a sense and a spirit that the Biden administration was limiting AI and there was a kind of a rip-it-all-off [attitude], everybody go and do whatever, we will be the biggest and boldest and brightest.”

“I love that talking point, but I think what you’ve seen is probably an education over the last 19 months, where people [in the White House] have said that’s a challenging premise to put into place, knowing about the potential downsides and capabilities,” she added.

Michael Daniel, former White House cyber coordinator under President Barack Obama, thinks the horse may already be out of the barn.

Daniel, now head of the Cyber Threat Alliance, a membership nonprofit group focused on cyber threat information sharing between industry and government, said his members report that AI is being used to do things “faster and at a slightly bigger scale” but aren’t yet seeing the flood of exploitation that analysts have warned about. Not yet.

“I think what we’re seeing right now [and] talking about is ‘okay, where are the step changes [in the cyber threat landscape] actually going to occur?” said Daniel. “Are we and when will we see the explosion in vulnerability reporting from these Mythos-like capabilities? That’s what’s really got their attention right now.”

But Mythos and OpenAI’s Daybreak models are restricted to select organizations, and neither has publicly released its most powerful cybersecurity models to the public. That dynamic won’t last.

The UK’s AI Security Institute estimates that open source and foreign LLM models are between 4-7 months behind frontier U.S. models. In that setting, it’s hard to stop the development of AI models worldwide through export controls or other limits.

“It’s not like we’re buying ourselves five to ten years on this,” he said. “We’re not, and so I’m not sure the impact on the defenders who are trying to obey the law is worth whatever small hiccup we cause for our adversaries.”

Kelly said there’s merit to the administration’s current position, even if it took time to get there. Many federal cybersecurity procedures that operated even a decade ago – such as a Vulnerabilities Equities Process that could take days or weeks to consider the pros and cons of keeping an exploit – are no longer practical.

“All of that work to some degree, is out the window, because you can’t meet with the regularity you would need to meet to adjudicate vulnerabilities that are being found in seconds and exploited in minutes,” said Kelly.

But Kelly and others say that’s also because AI capabilities in cybersecurity are developing faster than policymakers can react, even in the best of times.

Key questions remain and the administration’s balance between national security and backing domestic industry will likely shift  in response to new events.  The administration wants a framework that can predict and manage the risks of AI models today and tomorrow. That may be harder than it sounds.

“Do I think they’ve been clear? No,” said Kelly. “But I think it’s a place where clarity is really hard to achieve.”

The post Where’s the Trump administration line on AI regulation? appeared first on CyberScoop.

With the World Cup now complete, its biggest cybersecurity story may be what didn’t happen. While no major public cyber disruption has been reported, that shouldn’t be mistaken for a lack of risk. 

In the run-up to the tournament, the FBI’s Internet Crime Complaint Center (IC3) issued a public service announcement warning organizations and fans about fraudulent, spoofed websites impersonating the FIFA event – a reminder that the absence of a headline-grabbing breach doesn’t mean bad actors weren’t trying. In many ways, it’s evidence of the planning, coordination, and resilience required to keep an event of this scale running securely.

A global event like the World Cup depends on far more than what happens inside the stadium. It relies on local governments, venues, transportation systems, telecom providers, payment platforms, hotels, vendors, public safety agencies, and law enforcement, all working together. 

When I worked at the FBI, I saw how fast major events test teamwork across agencies, regions, and businesses. The World Cup offered that test at a scale few events can match.

Successful resilience is built months before kickoff

Successful major-event security depends on what happens long before there is a visible incident: trusted relationships, clear roles, shared intelligence and response plans. Planning becomes even more important when an event is not limited to a single city or venue.

In the past, event security consisted of guards, gates, and stadium perimeters. Those still matter, but they’re only part of the picture. Today, an event this big that brings millions of people together depends on many systems working in concert. No single organization owns the full risk picture, which means resilience depends on how well these groups can share information, coordinate response plans, and keep essential services operating under pressure. This means security can’t be planned around one perimeter. The real perimeter is the full event ecosystem.

Resilience starts much earlier, with planning across organizations that may not normally operate as one team. The real test for major events is whether public- and private-sector partners know their roles before pressure hits. That includes who shares information, who validates threats, who communicates with the public, who has decision-making authority and how quickly partners can act if a system slows down or becomes unavailable.

Major events are only as resilient as the systems behind them

Attackers don’t need to compromise the most visible organization to create disruption. They can look for weaker points across the event ecosystem. A disruption may begin with a vendor, ticketing platform, transportation partner, payment provider, hotel, contractor or communications provider, but the impact can quickly become broader than any one organization.

Sports organizations now operate like large businesses, with ticketing systems, VIP data, sponsors, vendors, media partners, stadium operations, payment systems, and fan engagement platforms. They depend on networks of suppliers and partners, and that creates multiple possible entry points.

Operational technology (OT) deserves more attention than it typically gets in these conversations. A ransomware attack that disrupted stadium operations directly, rather than a ticketing site or a fan-facing app, would be one of the most damaging scenarios organizers could face. OT security has to sit alongside the more visible concerns like payment fraud and spoofed domains, not behind them.

Bad actors don’t let a good crisis go to waste. Fans are often an easy target. Excitement drives a fan to buy a last-minute ticket or check a score on an unknown site. That excitement is exactly what fraudsters count on.

This risk grows over time. As the event gets closer and attracts more eyes, it becomes a richer target. A fake FIFA ticket site is useless to a crook a month after the last game. Groups running these systems must act faster as opening day nears, and share threat intelligence without delay.

Threat intelligence turns planning into proactive defense

The World Cup may be over, but the work isn’t. Cities, governments, and private-sector organizations will continue supporting large-scale public events that depend on complex digital and physical ecosystems. The question isn’t whether another major event will face cyber threats, it’s whether the planning starts early enough.

Organizations involved in future major events should focus on resilience, not just prevention. That means planning for what happens if a critical system slows down, goes offline, or becomes unreliable, and ensuring partners know how to coordinate before an incident occurs.

Every major event forces defenders to prepare for known risks. The harder challenge is anticipating the ones that haven’t emerged yet.

The next major disruption may not come from the attack that organizations spent months preparing for. It could target a new dependency, exploit emerging technology, or capitalize on a moment when public attention is at its highest. That’s why resilience can’t be built around yesterday’s playbook. It has to be informed by continuous threat intelligence, regular coordination across public- and private-sector partners, and the flexibility to adapt as the threat landscape changes.

The World Cup demonstrated what’s possible when that preparation comes together. As cities, governments, and private organizations look ahead to future global events, success won’t be measured solely by the attacks they stop. It will be measured by how effectively they can maintain critical operations, share information, and adapt under pressure when the unexpected happens.

The post What the World Cup can teach us about cybersecurity resilience appeared first on CyberScoop.

The Trump administration unveiled its new federal clearinghouse for sharing AI cyber threat information between the government and private sector, and said the project is already receiving threat intelligence on cybersecurity vulnerabilities and prioritizing patching.

Created last month through a White House executive order, “Gold Eagle” will be managed by the Department of the Treasury, with contributions from the Cybersecurity and Infrastructure Security Agency, Department of Homeland Security, and Department of Defense, as well as open-source software providers, critical infrastructure operators and industry.

“Under President Trump’s leadership, the Treasury Department is working hand in hand with the private sector to safeguard our financial institutions, close vulnerabilities, and protect the integrity of the U.S. financial system,” Secretary of the Treasury Scott Bessent said in a statement. “Treasury, along with our partner agencies, will continue to harness frontier AI capabilities to stay ahead of our adversaries and defend the American people from emerging threats.”

Gold Eagle is meant to help both public and private organizations find, fix and patch vulnerabilities found using AI tools before they’re discovered and exploited by bad actors. The work will involve using AI to find cybersecurity vulnerabilities in victim systems and software, and Secretary of Homeland Security Markwayne Mullin said it would also further explore ways for the technology to be leveraged for cyber defense.

A senior White House official told reporters on a background call that closed source models from frontier AI models, including Anthropic’s Mythos, will be used to discover vulnerabilities.

White House officials said they worked with the Software Engineering Institute, SEI at Carnegie Mellon University to develop a new platform, the Vulnerability Information and Coordination Environment – or VINTS – to receive third-party reports on AI-discovered vulnerabilities. According to the White House, the system has already begun collecting intelligence on vulnerabilities and prioritizing patches.

“I think on the early side of this, we have seen that the scale of vulnerability discovery, particularly with users of new technology to scan their system, is something that is a step function change [than] we’ve seen seen before,” the official said.

As AI models have improved at carrying out core cybersecurity-related tasks – like scanning code for vulnerabilities or developing proof-of-concept exploit code – cybersecurity experts and policymakers have become increasingly worried. The modern internet is rife with insecure code, misconfigurations and other mistakes that can be identified and exploited faster than ever before using AI tools.

Vulnerabilities in open-source software can be both widespread and hidden, as many commercial software products on the market rely on open-source code but few bother to document it. When hackers compromised a logging tool in the Log4J open-source Apache software library in 2021, it required a massive, multi-month coordination effort by CISA, the private sector and other stakeholders to find and fix affected pieces of software.

The White House official said the work of Gold Eagle is reflective of the administration’s “full support” of U.S. open-source software providers and maintainers.

Open source tools are “vital to systems that run throughout our country and daily life,” a senior administration official said, speaking to reporters on background. “It is being maintained by a talented group of people and entities and we will do everything we can to support the strength of that community.”

Michael Daniel, former White House cyber coordinator under President Barack Obama, told CyberScoop that AI is still so new that policymakers continue to observe its impact and adapt. While some existing communication channels for sharing cybersecurity threat information could probably be duplicated for tracking AI threats, there is still much for policymakers to learn more about the technology, the kind of threats it produces and its ecosystem of stakeholders.

“It may turn out at the end of the day that phishing is still phishing, and the fact that now you’ve got AI tools doing it, it’s still phishing. Or there may be something fundamentally different about it that we need to figure out how to combat and share information around,” he said.

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AI-generated code is part of everyday software development. Developers use it to prototype, refactor, troubleshoot, and move from idea to implementation with less friction than ever before. The productivity gains are undeniable, which means that security leaders now face a hard question: whether their organizations can govern the risk that AI creates at that same speed.

That challenge is rooted in scale. AI changes how quickly software can be created, while many application security programs still depend on controls designed for a slower development model. When code generation accelerates beyond the capacity to review, test, and remediate issues, security debt accumulates faster.

That is the hidden cost of AI-assisted development. Risk now enters the enterprise at machine speed, while many organizations still manage it with human-scale processes. CISOs should govern AI-generated code as a high-risk input: tested automatically, checked for unsafe dependencies, remediated quickly, and blocked from production if it fails policy.

The metric that matters is risk velocity

Application security has long been measured through discovery. Teams count vulnerabilities, categorize severity, report trends, and show whether the numbers are improving. Those questions still matter, but AI adds a more urgent metric: risk velocity. Security leaders need to know how quickly the organization creates new software risks and how quickly it can reduce or eliminate them.

AI changes the economics of security debt. A development team that produces significantly more code without a matching increase in security capacity will create more issues than it can reasonably review or fix. Even when AI-generated code is comparable to human-written code on a per-line basis, the total risk can rise because the volume of change is higher. The backlog grows, vulnerabilities persist, and security debt eventually constrains the business.

AI expands familiar failure modes

The failure modes are familiar. AI coding tools can reproduce insecure patterns found in training data, including weak input validation, unsafe authentication flows, insecure direct object references, hard-coded secrets, and vulnerable dependency choices. They can also miss the context that determines whether code is secure in a specific environment: authorization models, tenant boundaries, data sensitivity, production configurations, and how services interact in a real application.

There is also a human factor. Under the pressure of deadlines, developers may accept code that works without fully understanding how it does so. The result is misplaced confidence. Code compiles, tests pass, features ship, and hidden risk enters the system. Over time, the organization may lose sight of the security concerns that naturally arose during manual development.

The supply-chain risk is bigger than the code itself

The software supply chain adds another layer of risk. Modern applications are assembled from open-source components, frameworks, plugins, containers, APIs, and cloud services. AI coding tools can recommend outdated packages, vulnerable libraries, or nonexistent dependencies. Veracode’s 2025 GenAI Code Security report found that AI coding tools produce insecure code nearly half (45%) of the time. It may sound like an amusing hallucination until attackers register malicious packages with similar names and wait for developers or automated tools to pull them in. At that point, a coding shortcut becomes a supply chain exposure.

AI is already part of the development lifecycle, and its use will continue to expand. Security teams need a control model built for that reality.

“Shift Left” needs an enforcement layer

The industry has spent more than a decade moving security earlier in the development lifecycle, improving visibility and helping teams catch issues sooner. Many organizations, however, moved findings closer to developers without also moving enough ownership, automation, and remediation capacity with them. Developers received more alerts, while security teams gained more visibility into risks they still struggled to reduce.

AI makes that operating gap more urgent. As software output increases, security cannot remain a checkpoint near the end of the process. It must become a continuous control system built into the way software is created, tested, approved, and deployed.

Secure-by-design has to become infrastructure

Secure-by-design in the AI era requires an engineering environment where unsafe choices are harder to make and easier to catch. Approved frameworks, secure defaults, reference architectures, dependency controls, automated testing, and policy enforcement should be embedded directly into developer workflows and CI/CD pipelines.

Remediation also must move closer to the point of creation. When a coding assistant introduces a vulnerable pattern, the ideal response is an inline fix that is proposed, validated, and governed as part of the normal development process. AI can help defenders here when it is connected to reliable security signals, policy context, and evidence from real testing. Counterintuitively, developers using AI to write code often don’t trust AI to automatically remediate code without human review. This takes one of the best ways to keep up with machine-speed created vulnerabilities and slows it down to human speed. An acceptable balance between risk and speed must be found.

Approval is not governance

CISOs should focus on governance, not just approving AI coding tools. Governance means tracking where AI-generated code enters your environment, documenting the policies and tests applied, recording what issues were found and fixed, and keeping proof of these decisions. This documentation becomes critical as AI-assisted development becomes standard. If vulnerable code reaches production, you’ll need to show that adequate controls were in place and risks were managed according to policy.

What leaders should do now

CISOs and engineering leaders should treat AI-generated code as untrusted until proven otherwise. They must require automated testing before release, enforce dependency controls, prioritize remediation based on exploitability and business impact, and measure success by the rate at which critical risk is reduced.

Additionally, boards and organizational policymakers should ask whether organizations can demonstrate that AI-assisted software is governed before it is deployed. The key evidence should include the policies applied, the tests performed, the vulnerabilities remediated, the risks accepted, and the approvals recorded. Today, many organizations can confidently track what their AI tools produce, but they cannot demonstrate how that output was secured, reviewed, and governed before reaching production. The industry is still working to close this gap.

AI is changing how quickly software risk moves through the enterprise. The organizations that succeed will make security move just as quickly by embedding governance, remediation, and proof directly into the software delivery pipeline.

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The Paris Peace Forum, a French non-profit that has convened world leaders on global security issues, is launching a new project to bring together international experts to assess AI-related threats to global internet infrastructure.

The Integrated Network for Trusted AI in Cyberspace (INTAiC) will tap researchers and civil society experts from government and the private sector, analyzing current AI cyber threats from the field and creating “forward-looking” reports on how the technology will impact society and what organizations can do to respond.

One of the project’s top goals is to create an international, quick-response coalition of government and business to address AI-related threats, similar to coordination mechanisms that exist in other areas of cybersecurity.

“Evidence fragmentation on AI-driven cyber threats isn’t incidental — it’s structural: those defending networks and those securing AI systems have long worked in separate spheres,” said Adrien Abecassis, policy initiatives director for the Paris Peace Forum. “That’s exactly why INTAiC is unique — it’s built to turn those fragments into one comparable reading of the threat, because this is a challenge no actor can meet alone.”

The network already lists a number of prominent businesses and organizations, including Microsoft, the Cyber Threat Alliance, the Cloud Security Alliance, Orange Cyberdefense and others.

According to the forum, INTAiC’s work will focus primarily on two, separate workstreams. One is a single and regularly updated resource for defenders to stay up to date on how AI is reshaping cyber threats. The resource is focused more on attacker capabilities, different forms of misuse and the impact on security operations rather than isolated incidents.

“The result is a common reference point, grounded in reality, that gives policymakers a clearer measure of the threat and identifies the risks most deserving of collective attention,” the Forum said in a release.

The second workstream will focus on evaluating and preventing cyber risks associated with AI, building up a base of independent third-party experts who can provide neutral or unbiased assessments of frontier model cyber capabilities. That work will pull in governments, research institutions and non-profits to develop new organizational and funding pathways to support that kind of research.

While the U.S. federal government has come a long way in recent years building up its own capacity to test and study AI cyber threats, much of the access and technical expertise around frontier model capabilities are concentrated within commercial AI companies. This has at times created concerns that federal agencies were being overly reliant on AI companies to explain how the technology worked and walk them through the possible threat scenarios.

As Anthropic and OpenAI have rolled out defensive cybersecurity programs like Project Glasswing and the Trusted Access for Cyber program, access to those models have become available to a wider group of researchers and organizations.

The Paris Peace Forum intends to brief the public further on INTAiC’s work and accomplishments in Paris later this year during the organization’s annual conference in November.

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Two new parties have been added to a class-action lawsuit against X.ai over its Grok tool including teenagers and children who say it was used by family members or other people they know to create nonconsensual deepfake child sexual assault material (CSAM).

The lawsuit, originally filed in March by three women, was amended this week to include two additional plaintiffs, Jane Does 4 and 5, who say that Grok was used to make the illegal content based on their real photos and videos.

All five of the women in the lawsuit are anonymous, and the complaint said the spread of the material had left them humiliated and ashamed.

Jane Doe 4, a female from Wyoming, said her stepfather uploaded a photo of her when she was 11 and lying on a couch to his phone. Using Grok, the stepfather created more than 7,000 CSAM-related images of her. He also shared and traded the images with others on social media platforms.

The lawsuit alleges that the stepfather opted for Grok “because the platform was less restrictive than other AI models and responded to his prompts to generate sexually explicit material using an image depicting a prepubescent minor.”

It also claims that in February, xAI did generate a tip to the National Center for Missing and Exploited Children regarding the images, but the company only submitted the original, authentic image as evidence. According to the suit, xAI did not respond when law enforcement requested the thousands of Grok-generated images based on the photo and IP address information that would have quickly helped identify her stepfather as the perpetrator.

The lawsuit states that the stepfather shot himself two days after he was arrested and charged with child exploitation crimes. His suicide added to the “extreme personal crisis” brought on by the images created through Grok. She regularly “struggles with self-loathing and disgust” as well as “extreme anxiety” at the thought that the images will be found by others online and suffer from depression, including excessive sleep and suicidal ideation when awake.

Jane Doe 5 claimed that an adult male related to one of her classmates used Grok to convert a photograph from her eighth-grade graduation into illicit material. The images were also traded and shared with others online. While the man was arrested and charged, much of the content is still available on the internet. As a result, she “feels a complete lack of control over the ongoing dissemination of the files.”

“It is impossible to know how many other child sex predators may now possess Jane Doe 5’s CSAM, nor how widely her CSAM has now been disseminated online through darknet channels and applications,” the complaint said.

The press office for xAI did not respond to an emailed request for comment from CyberScoop.

The lawsuit also adds Stability AI as a defendant, alleging the company released Stable Diffusion 1.0 as an open-weight model despite knowing it was trained on CSAM and has declined to alter or modify its guardrails in response.

According to a 2023 Stanford study, the underlying dataset used to train Stable Diffusion models was created through unguided webcrawling of internet content. That means it ingested “a significant amount of explicit material,” including CSAM. Stable Diffusion 1.0 had a classifier meant to block the generation of such images, but because of that training data, downstream developers could more easily exploit the model and create modified versions that bypass those protections.

While Stable Diffusion 2.0 introduced stronger guardrails, the lawsuit claims Stability AI rolled back those protections in response to “disgruntled” users that the new restrictions were “prude” and “unpopular.” That in turn has fed an ecosystem of jailbroken “nudify apps” based on Stability AI’s models.

“Stability AI knew that its models, once capable of generating sexually explicit images, would foreseeably be used to generate CSAM unless appropriate model-level safeguards were implemented,” the complaint said.

Stability AI did not respond to a request for comment from CyberScoop.

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Anthropic has announced its Fable 5 and Mythos 5 models will once again be available to the public as it has reached an agreement with the Commerce Department to deploy the AI models with new guardrails and classifiers meant to address jailbreaks.

In a blog posted Tuesday, Anthropic said that export controls that prevented their sale to foreign companies and individuals have been lifted after weeks of negotiation with the White House and Commerce Department. The company has also restored access to the model for U.S. users.

The export controls were put in place after the Trump administration became alarmed by a threat intelligence report from Amazon claiming to have jailbroken Fable’s cybersecurity capabilities.

On X, Secretary of Commerce Howard Lutnick appeared to confirm that the restrictions would be lifted.

“Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI,” Lutnick wrote.

The administration levied the export controls after becoming concerned that the release of Fable 5 would lead to the model being jailbroken, giving users access to cybersecurity and other capabilities that Anthropic has said could wreak havoc on the open internet if  placed in the wrong hands. The Amazon report convinced administration officials that such jailbreaks were on the immediate horizon.

However, one oddity of the administration’s decision is that the capabilities described in the Amazon report, by all accounts, are not cutting-edge. Scanning code and breaking down how to exploit vulnerabilities for a user is already possible with existing models.

Anthropic confirmed that, saying that further testing found that equivalent and lesser models like ChatGPT 5.5, Claude Opus 4.8 and Kimi K2.7 could identify the same vulnerabilities as Fable did in the Amazon report, while a half dozen existing models were able to produce the same proof of concept code as Fable.

Crucially, Anthropic reiterated that they have yet to see a jailbreak that affects the model’s restrictions on cybersecurity and biology work, though they did call this instance “a borderline case.” Indeed, some cybersecurity professionals have publicly complained that existing safety guardrails on Fable 5 blocked many routine defensive cybersecurity work in addition to malicious use cases.

“Importantly, the reported technique did not expose any unique Mythos-level cyber capabilities,” the blog continued. “The behavior reflected a borderline case for Fable 5’s safeguards…there are some tasks that are unlikely to be dangerous but are nonetheless blocked by the safeguards out of an abundance of caution. The reported technique allowed access to one such behavior, but it only involved routine defensive cybersecurity work.”

Anthropic said it has trained new safety classifiers to target and block the behaviors described in the Amazon report and notify users when it happens, and that the new safeguards have been stress tested by the federal Center for AI Standards and Innovation. The new classifiers will block the techniques “99.9%” of the time, but Anthropic said they’re not expected to block all lower risk routine cyberdefense capabilities, just the most harmful ones.

The restrictions will likely make it even harder to use Fable 5 for defensive cybersecurity. One effect the company expects is that more “benign” requests for routine coding and debugging tasks will be flagged by the system.

Christopher Padilla, former Assistant Secretary for Commerce for export administration in the George W. Bush administration, said that while it’s “good news” the controls have ultimately been lifted, the Trump administration’s AI policy stumbles over the past two years illustrate “the risks of ad hoc, transactional policymaking.”

In a LinkedIn post, Padilla called the Trump administration’s approach chaotic and unpredictable — the opposite of the clear, consistent rules industry depends on. While Vice President J.D. Vance mocked AI safety regulations in a speech in Europe last year, the administration has quietly partnered with OpenAI and Anthropic on voluntary national security testing, especially as frontier models began showing advanced automation and cyberattack capabilities.

That national security arrangement was supposedly codified in a White House executive order last month, shaped heavily by industry boosters who feared regulatory delays would slow U.S. development. But days after Fable’s release, Commerce imposed new export controls on Anthropic’s models anyway.

Padilla called proposed AI safety regulations by the Biden administration “flawed and overly complex” but nevertheless predictable compared to the status quo. Instead of replacing those proposed regulations with their own vision, the Trump White House has been “to put it mildly, all over the place on AI policy.”

“The same BIS that stopped Fable and Mythos has a permissive policy for exporting high-end AI semiconductors to China — in exchange for a cut of the take,” said Padilla, referencing the Trump administration’s lifting of export controls on advanced AI chips. “This is not a smart way to make policy. Bad for industry competitiveness and for national security.”

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The Supreme Court ruled Monday that collecting phone location data from a geographic area is a Fourth Amendment search, in a decision that both privacy advocates and critics of the ruling say will have vast implications for tech privacy.

The 6-3 ruling in Chatrie v. The United States is a “major win” for privacy under the Fourth Amendment, said one law professor who studies surveillance. And it “will send seismic waves through our Fourth Amendment doctrine” with ramifications “for the foreseeable future,” the dissenting justices wrote. The ruling didn’t fall along some of the traditional lines of justices selected by Republican or Democratic presidents.

Okello Chatrie challenged police’s collection of cell phone data from Google in his bank robbery conviction under a so-called geofence warrant that gleaned insights about his location around the time of the crime. While the Supreme Court punted on the question of whether the specific warrant in his case was proper, it held that the Fourth Amendment’s protections apply to this kind of data collection — and potentially other, future kinds as well.

Among the issues the court debated was whether a generalized collection constitutes a search as defined by the Fourth Amendment’s rights against “unreasonable searches and seizures.” That included questions of whether someone who willingly gives their data to a company like Google retains Fourth Amendment projections for that information, under the “third-party doctrine.”

The majority found that cell location data is substantially similar to cell-site location information addressed in Carpenter v. United States (2018),  where the court similarly held that the government’s collection of this data constitutes a Fourth Amendment search.

In the new opinion, Justice Elena Kagan, writing for the majority, used sweeping language about how the Fourth Amendment might apply as technology advances.

“A new technology should not transform what individuals had reasonably thought they could withhold from the Government,” Kagan wrote for the majority with Justices John Roberts, Sonia Sotomayor, Brett Kavanaugh, and Ketanji Brown Jackson. “An individual has a reasonable expectation of privacy in records about his cell phone’s location, and police intrude on that constitutionally protected interest when they demand the information — even though for only a limited time, and from a third-party tech company.”

Justice Neil Gorsuch wrote a concurring opinion, saying that he differed from Kagan’s opinion only in how it arrived at its conclusions, citing the Fourth Amendment’s language about “papers” and “effects”: “As I see it, Mr. Chatrie’s Location History data qualifies as his personal property.”

Justice Samuel Alito wrote for dissenting justices that the court had gone too far in extrapolating protections specified under the Carpenter decision, saying it “will send seismic waves through our Fourth Amendment doctrine” despite not affecting Chatrie’s case.

“As the majority works its way through the question in this case, it makes sweeping proclamations with implications far beyond the specific procedure that the police used here,” Alito said, adding that the decision “all but guarantees that we will be cleaning up debris for the foreseeable future.”

Andrew Ferguson, a law professor at George Washington University and author of a book about how police use of data threatens personal freedom, said the ruling was big even if it will still be easy for law enforcement to obtain warrants in other ways.

“Chatrie is a major win for Fourth Amendment privacy,” he told CyberScoop. “The Supreme Court did take a significant step today to update the Fourth Amendment in a digital age, and we should be thankful that they did.”

The American Civil Liberties Union also celebrated the ruling.

“The Court’s decision provides critical protection against invasive and overbroad government searches of our personal information,” Brett Max Kaufman, senior counsel with ACLU’s Center for Democracy who helped write a friend of the court briefing on Chatrie’s side, said in a statement to CyberScoop. While Google has changed its system in a way that practically cuts off government requests for future location data, “similar kinds of reverse searches of sensitive data held by other companies will continue to be a threat to privacy. Law enforcement and courts are on notice that new technology does not open up surveillance loopholes, and strict adherence to the Fourth Amendment’s protections is required.”

An attorney who served as Chatrie’s counsel of record said he looked forward to continuing to work on his case.

‘Today the Court decisively held that people have a privacy right in their personal data — no matter how short the timeframe or whether the information is held by a tech company,” Michael Price, Fourth Amendment center litigation director at the National Association of Criminal Defense Lawyers, told CyberScoop. “The government cannot sidestep the Fourth Amendment by labeling location history and other cell phone data as ‘third party’ records. The Court definitively recognized that accessing this data is a search that triggers constitutional protections.”

The Center for Democracy and Technology said in a social media post that “for years, police have treated the trail your phone leaves behind as theirs for the taking, but today in Chatrie v. United States, the Supreme Court slammed that door shut.”

But the ruling indicates the need for Congress to make it illegal to purchase data from third party companies without a warrant, said Don Bell, policy counsel of The Constitution Project at the Project on Government Oversight. The subject has become intertwined with the debate over surveillance powers that expired this month.

Updated 6/29/26: with comments from Price, CDT and POGO.

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Post-quantum cryptography didn’t sneak up on the industry. 

For years, security teams, standards bodies, hyperscalers, and governments have been pointing at the same horizon: a cryptographically relevant quantum computer will, eventually, dismantle the public-key algorithms underpinning today’s enterprise security. The latest executive order doesn’t introduce a new threat. It codifies what the field has long understood, and attaches deadlines to it.

For CISOs, the framing shift matters. PQC is fundamentally a readiness problem, not a cryptography problem. Watching Google accelerate its quantum roadmap, or seeing federal agencies restructure their security architecture around PQC, makes the stakes impossible to ignore.” Boards are already asking: “How are we thinking about post-quantum transition today?” For most organizations, the gap between that question and a credible answer is wider than it should be.

The EO is unambiguous on scope. PQC has moved from a research effort to real policy, with deadlines, accountability structures, and direct consequences for federal agencies, contractors, critical infrastructure operators, and the broader private sector that supports them.

Federal high-value systems must transition key establishment to PQC by Dec. 31, 2030. Digital signatures will follow by Dec. 31, 2031.

Those dates may appear distant, but for anyone who has navigated an enterprise-scale security transformation, with the procurement cycles, architecture reviews, and organizational change management that entails, 2030 sits squarely inside current planning horizons. The window for orderly execution is already narrowing.

What makes that window even tighter is that the most immediate risk has nothing to do with deadlines. “Harvest Now, Decrypt Later” attacks are already operational. Nation-state adversaries are collecting encrypted data today and storing it until quantum capabilities are sufficient to decrypt it: intellectual property, health records, financial transactions, source code, government communications, and more. The encryption protecting that data right now is, functionally, a time-delayed vulnerability. Long-lived sensitive data may already be compromised in ways that won’t become visible for years.

The first step for CISOs is shifting from awareness to ownership.

PQC readiness cannot be delegated to individual application teams or treated as a future compliance checkbox. That approach will not survive given the EO’s accountability requirements. Every organization needs a point person: a program lead, a cross-functional steering committee, or a dedicated cryptographic risk office. Whatever the structure, it needs authority and a seat at the leadership table.

That ownership must span security, IT, infrastructure, engineering, product, legal, compliance, procurement, and business stakeholders. Cryptography is embedded across the entire enterprise: certificates, keys, protocols, APIs, hardware, cloud services, code-signing systems, identity infrastructure, third-party platforms. No single team has the bandwidth to address this alone. A cross-functional working group or Center of Excellence should be an organizational prerequisite as we move into the future.

Visibility is going to be critical, and this is where most organizations will find the largest gaps.

CISOs need a clear picture of where cryptography exists across their environment: which algorithms are in use, which systems depend on vulnerable cryptography, what data requires long-term confidentiality, and which business processes would be disrupted by migration. Without that inventory, risk assessment is guesswork, remediation is impossible, and demonstrating progress to regulators or boards becomes an exercise in speculation.

The principle is straightforward: you cannot protect what you cannot see.

Furthermore, a cryptographic inventory cannot be a static spreadsheet updated annually and then filed away. It needs to function as a living view of the organization’s trust infrastructure, covering certificates, keys, algorithms, libraries, protocols, signing systems, certificate authorities, HSMs, workloads, devices, and third-party dependencies. 

Once that visibility exists, prioritization follows from business impact. Systems protecting long-lived sensitive data, critical infrastructure, customer trust, software integrity, and regulated environments move first, with everything else sequenced accordingly.

Beyond visibility, CISOs need a roadmap aligned to the order’s milestones rather than aspirational planning documents that never translate into funded programs.

The 2030 key establishment deadline requires understanding every point where encryption and key exchange mechanisms operate across critical systems. The 2031 digital signatures deadline extends that challenge to software integrity, code signing, document signing, authentication, identity infrastructure, and long-term verification. This is a multi-year transformation program, and it warrants the same organizational rigor as any other enterprise-wide initiative of comparable scope.

That means three categories of dedicated resources. First, funding: PQC readiness cannot be absorbed into existing security budgets without displacing other priorities. It requires multi-year investment in discovery tooling, testing, migration execution, automation, and governance. Second, talent: organizations need cryptography expertise, enterprise architecture capability, PKI experience, risk management, compliance support, and program leadership, a combination already in short supply across the industry. Third, technology: discovery tools, certificate and key lifecycle automation, policy enforcement, reporting infrastructure, and the architectural capability for crypto-agility.

Crypto-agility is the long-term objective that makes this transition worth doing properly.

Organizations that treat PQC as a one-time algorithm swap will find themselves back in the same position when standards shift again. The quantum transition is occurring in parallel with the rise of AI, machine identities, autonomous systems, and increasingly complex digital ecosystems, all of which depend on cryptographic trust. Organizations that do not actively govern that trust infrastructure will struggle with AI security, software supply chain integrity, identity governance, and the compliance mandates that follow.

The order functions as a forcing mechanism, converting PQC from a future technical concern into a present-day leadership accountability. Three questions now define where an organization stands:

• Do we have a clear picture of where our cryptographic risk lives?
• Do we have a funded, sequenced migration plan that meets the order’s deadlines?
• Can we demonstrate that our trust infrastructure is agile enough to adapt as standards and threats continue to evolve?

The debate over precisely when quantum computing will be a reality is a distraction. Building the visibility, governance, funding, and automation required to move with confidence is where we need to be spending our collective time and effort.

CISOs have moved past the question of whether to act. The operative question is how far behind the organization already is, and how quickly it can transform cryptography from an invisible dependency into a managed, measurable, and adaptive system of trust. The organizations that begin that work now will be the ones with options when the deadlines arrive.

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The Federal Communications Commission approved new rules Thursday that boost cybersecurity regulations for the nation’s emergency alert systems and update security rules for the nation’s undersea cables.

The new rule would overhaul two national emergency systems, the Emergency Alert System and Wireless Emergency Alerts, to better protect against hijacking attacks from malicious actors.

The EAS is a national public warning system that state and local authorities use to disseminate information related to weather events, AMBER alerts and other emergencies via radio and television broadcasting stations. The WEA handles much of the same messaging via text.

A compromise of either system by a foreign government, cybercriminal group or other rogue actor could be used to sow chaos and disinformation in calmer times, or impede coordination efforts in the face of a genuine emergency. Any vulnerability in systems like the Emergency Alert System “can have serious consequences,” said FCC Commissioner Olivia Trusty in a statement after the vote.

“That is why it has been appropriate for the Commission to conduct a comprehensive review of the EAS framework by focusing on the security of the system itself,” Trusty continued. “As cybersecurity threats continue to evolve, EAS participants must take appropriate steps to safeguard the infrastructure that supports the delivery of life-saving alerts.”

The new rules amount to basic – but still critical – cyber hygiene practices for users accessing and updating the EAS and WEA systems. They must use strong passwords, quickly install security patches from vendors and use firewalls to limit access to their equipment.

The rule also creates a new authentication ID system to verify alerts before they’re submitted and avoid duplicate or unauthorized alerts from spreading.

Another rule passed by the Commission Thursday provided the first comprehensive update to the FCC’s submarine cable regulations in decades, and moves to tighten cybersecurity requirements in some areas while loosening them in others.

It exempts some undersea cable providers from submitting to stringent national security licensing reviews needed to land and operate cables that touch U.S. territory.

The review, called “Team Telecom,” is an interagency body led by the Department of Justice’s Foreign Investment Review Section and other federal agencies that advise the FCC on the national security implications of their telecom policies.

The new rules would presumptively exempt applications for undersea cable licensees when the provider can self-certify to “high security standards” that are “structured to increase certainty, predictability, and faster timelines for the licensing process.”

“Currently, all submarine cable applications get referred to Team Telecom…the changes adopted would exempt applications from applicants that have operated cables without incident, can certify to the highest national security standards, and agree to ongoing oversight and monitoring,” the FCC said in a release.

Other parts of the rule give the FCC greater oversight of critical functions within undersea cable operations. Owners and operators of submarine line terminal equipment, who connect submarine cables to land-based facilities in the U.S., will be subject to a new licensing requirement.

The rule also moves to update safeguards meant to address vulnerabilities related to principal equipment, third-party service providers, and other areas of concern in the undersea cable supply chain.

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