IAPP State AI Governance Tracker
webUseful reference for AI safety practitioners and policy researchers tracking how U.S. state-level AI regulation is evolving, particularly regarding high-risk AI systems and private-sector compliance obligations.
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Summary
A continuously updated tracker from the International Association of Privacy Professionals (IAPP) monitoring AI-related legislation across U.S. states, covering enacted laws in California, Colorado, New York, Utah, Texas, and others. It compiles legislative activity into searchable charts, maps, and directories focused on private-sector obligations, helping organizations navigate the fragmented state-level AI regulatory landscape.
Key Points
- •Tracks state-level AI governance laws with a focus on private-sector obligations, excluding government-only bills and narrow sectoral regulations.
- •Covers major enacted laws including Colorado AI Act (Feb 2026), Texas RAIGA (Jan 2026), Utah AI Policy Act (May 2024), and NY RAISE Act (Jan 2027).
- •States are not waiting for federal action; legislative responses have accelerated dramatically since generative AI gained public prominence in 2022-2023.
- •Legislative themes include government AI safeguards, consumer protection guardrails, automated decision-making rules, and generative AI transparency requirements.
- •Complements IAPP's Global AI Law and Policy Tracker and US State Privacy Legislation Tracker for broader regulatory coverage.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| US State AI Legislation Landscape | Analysis | 70.0 |
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TOOLS AND TRACKERS MEMBER Published 23 May 2024 Last updated 28 April 2026 View chart View map Contributors:
David Botero
Westin Fellow
IAPP
Cobun Zweifel-Keegan
CIPP/US, CIPM
Managing Director, Washington D.C.
IAPP
As with seemingly every aspect of AI, legislative activity related to potential AI risks and harms has moved with unprecedented speed. Often it can take decades for policymakers to begin responding to new technologies with targeted laws. But after generative AI captured the world's attention, it took only a matter of months for U.S. state legislatures to consider responsive legislation. States are not waiting for federal action, instead adopting a remarkably active stance on regulations responding to concerns around many different types of AI systems and contexts.
Unpacking the themes of these legislative efforts is an ongoing undertaking. To help keep track of this rapidly changing landscape, the IAPP has published this tracker, which collates legislative activity and reflects emerging themes across AI governance policymaking at the U.S. state level. This information is compiled into a chart, map and directory with information specific to states with enacted laws.
Over the past few years, state responses have varied widely, reflecting the multifaceted challenges AI poses across legal domains. Initial legislative efforts focused on state government use of AI, with states creating new safeguards or outright bans on high-risk governmental AI applications. Other states have focused on implementing studies and task forces to assess AI’s risks and benefits before rushing to new regulations. The recent surge in generative AI has shifted legislative attention to commercial AI guardrails. These efforts typically fall within consumer protection law and aim to amend state legal codes accordingly. This tracker serves as a resource to monitor these developments.
If you are aware of a state AI governance bill absent from the tracker, please share it with us at research@iapp.org .
The IAPP additionally hosts a US State Privacy Legislation Tracker , which tracks comprehensive US state privacy bills, and Global AI Law and Policy Tracker , which identifies AI legislative and policy developments in a subset of jurisdictions.
The state AI governance law chart is curated to spotlight legislation directly impacting private sector organizations, excluding government-only bills. While such standards influence AI policy, they generally do not impose immediate obligations on private sector organizations. Sectoral AI activities, despite their significance, are also omitted from the chart due to their limited scope.
The chart instead focuses on broadly applicable laws affecting various types of AI systems. This approach acknowledges the complexity of crafting comprehensive AI governance legislation that addresses the spectrum of risks associated with diverse AI applications. The chart features a scope column to indicate
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