Regulators are no longer watching from the sidelines when it comes to enterprise AI.
The Federal Trade Commission (FTC) and the Securities and Exchange Commission (SEC) have started cracking down on companies that misstate, exaggerate, or fabricate claims about their AI capabilities. From misleading ads to unsubstantiated performance claims, the bar has been raised—and the consequences are real.
This shift marks a new phase in AI governance. What a company says about its AI tools—on its website, in its documentation, or in sales decks—is now a matter of legal risk and public trust.
Why AI Claims Are Under the Microscope
In 2024, the FTC launched Operation AI Comply, a multi-agency effort to hold businesses accountable for deceptive AI claims. Their focus spans industries like healthcare, finance, and cybersecurity. At the same time, the SEC has started investigating firms for “AI-washing”—a term for overstating the use or impact of AI in investor-facing materials.
Both agencies have made their message clear: AI isn’t exempt from existing law. If a company markets its AI model as “fully autonomous” or “guaranteed to deliver results,” it must have the evidence to back it up.
These new guidelines put AI security teams in a new position. They’re not just protecting models from attack—they’re now part of ensuring the claims made about those models don’t create exposure.
What Regulators Are Targeting
The FTC and SEC have released clear examples of claims they consider deceptive. These fall into a few core categories:
- Exaggeration or fabrication: Statements like “100% accurate,” “human-level intelligence,” or “fully autonomous” without proof.
- Omission: Leaving out known limitations, risks, or the fact that humans remain in the loop.
- Fake content: Using AI to generate reviews, testimonials, legal docs, or robocalls with misleading intent.
- AI-washing: Suggesting tools are powered by AI when they’re not or leaning on the AI label for marketing without real functionality.
These claims are misleading and violate LLM security principles by creating false confidence in a system’s capabilities.
Enforcement Is Already Happening
Recent enforcement actions highlight how quickly things are changing:
- DoNotPay paid $193,000 in a settlement for advertising a “robot lawyer” it couldn’t support with evidence.
- Rytr, an AI writing platform, was forced to shut down after it was found to be generating fake reviews.
- Ecom automation companies like Ascend Ecom were ordered to cease operations over get-rich-quick AI claims.
- SEC investigations into investment advisors have revealed inflated claims about AI-driven portfolio tools, triggering settlements and revisions to disclosure.
These aren’t outliers. They show a pattern of increased scrutiny. In each case, it wasn’t just the model’s performance that triggered a response—it was the mismatch between what was claimed and what could be proven.
Why Documentation Is Now a Risk Surface
Most enterprises know to vet their public marketing language. Fewer realize that internal documentation—model cards, spec sheets, training guides—can also expose them to risk.
Regulators now look at AI documentation as part of a company’s external posture. If the model documentation is inconsistent with public claims or lacks details around limitations, risks, or human oversight, that disconnect could be seen as deceptive.
Common red flags include:
- Missing disclaimers or risk summaries
- Ambiguous use cases
- Outdated or unsubstantiated accuracy metrics
- Documentation created after launch
These issues compound when teams work in silos. Developers might test one thing. marketing might write another, and legal might review too late. Inconsistent language across departments can lead to regulatory trouble.
What “Regulatory-Ready” Documentation Looks Like
To reduce legal and operational exposure, companies need clear, specific, and structured documentation.
A minimum viable model doc in 2025 should include:
- Intended use cases: Clear definition of what the tool is built to do
- Known limitations: Real talk about where the model may fail or misfire
- Testing conditions: Datasets, edge cases, and validation setups
- Performance metrics: Actual accuracy/error rates, with dates
- Risk disclaimers: Clear articulation of bias, misuse, or context limits
- Human oversight: What tasks require a human and how they step in
- Data governance: Where the training data came from and how it’s managed
- Version control: Logs of what changed, when, and why
- Review schedule: When the doc will be audited or updated next
Documentation isn’t just a dev artifact anymore. It’s a legal artifact. The more precise it is, the less room there is for misunderstanding or overstatement.
Best Practices for Reducing AI Claim Risk
To stay ahead of scrutiny, enterprises need to treat documentation like a living system. That means:
1. Align Legal, Product, and Marketing Early
- Use shared language across documentation, ads, investor materials, and training guides.
- Ensure all external claims match what can be proven internally.
2. Avoid Trigger Phrases
- Don’t use terms like “guaranteed,” “fully autonomous,” or “human-level” unless they are provably accurate.
- Replace them with qualified, forward-looking language:
- “We aim to automate…”
- “Our system supports decision-making…”
- “Potential to reduce time spent…”
3. Standardize With Templates
- Use structured templates like model cards or datasheets to align across teams.
- Create a centralized documentation repository accessible to legal, risk, product, and sales.
4. Embed Documentation in the Product Lifecycle
- Don’t wait until just before launch.
- Update documents at each major milestone—training, QA, integration, and release.
5. Train Teams on Regulatory Triggers
- Teach what the FTC and SEC look for.
- Review past enforcement examples to highlight real-world stakes.
Final Thoughts
The era of loose language around AI is over. Claims that were once treated as hype now carry legal risks. The FTC and SEC aren’t trying to stop innovation—they’re trying to prevent deception.
Enterprises must adapt by building a documentation culture supporting transparency, precision, and internal alignment. That doesn’t mean silencing your messaging but strengthening it with clarity and evidence.
Specific, well-supported claims won’t just reduce your risk—they’ll build long-term trust with customers, investors, and regulators.
