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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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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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.”

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