AI Regulation Tracker
Every AI bill, rule, and enforcement action. Tracked.
Real-time legislative intelligence across 1 jurisdictions and 0 US states. See live source coverage →
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FTC proposed an IntelliVision facial-recognition settlement addressing unsupported accuracy, bias, and anti-spoofing claims.
The FTC published a proposed consent order concerning IntelliVision's alleged unsupported claims about facial-recognition accuracy, bias, and anti-spoofing performance. The proposed terms would restrict specified claims, require competent and reliable testing and documentation, and remain in effect for 20 years if finalized; the source does not establish that the order became final.
FTC proposed a consent order addressing deceptive claims about accessiBe's automated accessibility software.
The FTC published a proposed consent order concerning accessiBe's marketing of automated accessibility software. The proposal would restrict unsupported WCAG-compliance claims, require disclosures about endorsements and product limitations, and include monetary relief and compliance provisions, but it was subject to final approval and does not establish a current binding duty in the supplied record.
FTC proposed a consent order restricting Rytr's AI-generated review and testimonial services, subject to final approval.
The FTC published a proposed consent agreement concerning Rytr's generative AI review-writing functionality. The proposed order would ban specified review- and testimonial-generation services and impose reporting and recordkeeping provisions, but the supplied text shows that final approval had not yet occurred. The commissioners' dissents disputed the sufficiency of the allegations and warned against liability based only on potential misuse.
FTC proposed a consent order addressing allegedly deceptive marketing of DoNotPay's AI legal service.
The Federal Trade Commission published a proposed consent agreement concerning DoNotPay's allegedly deceptive marketing of an AI-powered legal service. The proposal was subject to final approval and public comment, with comments due October 30, 2024; the described provisions should not be treated as binding duties without evidence of finalization.
FTC proposed policy statement would treat undisclosed AI output steering contrary to user expectations as potentially deceptive.
The FTC proposed a policy statement applying section 5 deception principles to companies marketing AI systems. It says undisclosed steering of outputs away from users' stated or reasonably expected objectives may be deceptive, while ordinary hallucinations caused by technological limitations are treated differently. The document is proposed and non-final; comments were due July 31, 2026.
FTC proposed a consent order addressing unsupported accuracy claims for Workado's AI-content detector.
The FTC published a proposed consent agreement concerning Workado's claimed accuracy for an AI text detector. The proposed order would impose substantiation, disclosure, recordkeeping, reporting, and monitoring requirements if finalized; enforcement risk is assessed at 75 because the document concerns a named company and a proposed settlement, but the order was not yet final.
NIST NCCoE invites public input on the usability of the NIST Cybersecurity Framework (CSF) Cyber Artificial Intelligence (AI) Profile during a virtual working session on May 12, 2026.
NIST's National Cybersecurity Center of Excellence (NCCoE) is hosting the third virtual working session on May 12, 2026. This session aims to gather public feedback on the usability of the NIST Cybersecurity Framework (CSF) Cyber Artificial Intelligence (AI) Profile, contributing to the development of this important guidance.
NIST NCCoE announces a virtual working session series on April 28, 2026, to gather input for its Cybersecurity Framework (CSF) Cyber AI Profile.
NIST's National Cybersecurity Center of Excellence (NCCoE) is hosting a virtual working series to solicit input for the development of its Cyber Artificial Intelligence (AI) Profile. The first session is scheduled for April 28, 2026, and aims to update elements and content related to applying the NIST Cybersecurity Framework to AI systems.
FTC proposed an Everalbum order addressing alleged deceptive facial-recognition and biometric-data practices.
The FTC published a proposed consent agreement concerning Everalbum's alleged deceptive facial-recognition and content-deletion practices. The proposed order would require disclosures, affirmative express consent, deletion of specified data and derived models, and compliance monitoring, but the notice states that final approval remained pending.
NIST NCCoE announces a virtual working session on May 5, 2026, to solicit public input for the development of its Cybersecurity Framework Cyber Artificial Intelligence Profile.
NIST's National Cybersecurity Center of Excellence (NCCoE) is hosting a virtual working session on May 5, 2026, to gather stakeholder input for its Cybersecurity Framework Cyber Artificial Intelligence (AI) Profile. This session is part of a series aimed at extending the technical content of the profile, which will offer guidance on managing AI-related cybersecurity risks and applying AI in cybersecurity.
NIST's preliminary Cyber AI Profile proposes voluntary CSF-based guidance for managing cybersecurity risks from and involving AI.
NIST's NIST IR 8596 initial preliminary draft proposes a voluntary Cyber AI Profile based on CSF 2.0. It covers securing AI components, using AI for cyber defense, and countering AI-enabled attacks, but remains under development and does not create binding legal duties.
NIST research evaluates statistical methods for estimating uncertainty and generalized accuracy in LLM benchmark results.
This NIST report studies statistical methods for interpreting LLM benchmark performance, including generalized linear mixed models and uncertainty estimation. Findings are conditional on specified simulations, benchmarks, model configurations, and statistical assumptions; the report does not establish legal duties or production guarantees.
NIST summarizes 600+ comments supporting standards-based identity, authorization, and governance for AI agents.
This NIST summary reports more than 600 responses to a concept paper on software and agentic AI identity and authorization. Commenters generally favored extending existing standards, with emphasis on verifiable identities, scoped credentials, delegation traceability, continuous authorization, auditability, privacy, and human oversight. The document reports stakeholder feedback and planned NCCoE work; it creates no legal duties.
NIST's draft guide illustrates cautious, human-reviewed use of generative AI for CSF 2.0 analysis and reporting.
NIST's Initial Public Draft provides three illustrative use cases for generative AI supporting CSF 2.0 governance and profile development. It emphasizes approved tools, privacy and security review, provenance, current source data, and qualified human validation; it does not create enforceable duties or demonstrate validated performance.
NIST NCCoE consultation material proposes Cyber AI Profile formats and records unresolved scope, adoption, and delivery questions.
This NIST NCCoE consultation document discusses the development and possible delivery formats of the Cyber AI Profile, including a proposed Excel workbook alongside a final PDF. It records feedback, unresolved questions, and working-session dates, but does not establish a final profile, legal duty, certification status, implementation deadline, or enforceable requirement.
NIST discusses proposed ways to extend its Cyber AI Profile for agentic AI and Zero Trust; the material is preliminary and nonbinding.
This NIST NCCoE discussion essay supports factual context about proposed extensions to the Cyber AI Profile, including agentic-AI risks, Zero Trust, community feedback, and three integration options. NIST expresses a preference for targeted Cybersecurity Framework Subcategory revisions, but the material is preliminary and does not establish binding requirements, legal duties, implementation deadlines, certification criteria, or measured effectiveness.
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