The machine-readable AI-governance catalog.

Risk-based framework. Prohibited practices (Art. 5) effective 2 February 2025; general-purpose AI obligations (Arts. 51-55) 2 August 2025; high-risk system obligations (Title III) 2 August 2026. Staggered 6/12/24-month application timeline from 1 August 2024 entry-into-force per Regulation (EU) 2024/1689 Art. 113.

Foundational EU personal-data protection regulation. Most-cited European instrument PW catalogues at the AI-governance boundary — every CNIL / Garante / AEPD / BfDI / DPC enforcement action against an AI system (Clearview, ChatGPT, Replika, automated-hiring complaints) invokes GDPR Arts. 5/6/9/22/35. Art. 22 (automated individual decision-making + profiling) is the load-bearing provision that interacts with EU AIA Art. 26(11) deployer use of AI-system output for decisions concerning natural persons. Art. 35 (DPIA) partially overlaps EU AIA Art. 27 FRIA; the EDPB is finalising a joint EDPB-AI-Office guideline on the AIA-FRIA / GDPR-DPIA interplay through 2026. Art. 9 (special-category processing) interacts with EU AIA Art. 5(1)(c)(d)(g) prohibitions on social scoring + emotion recognition in workplace + untargeted facial-image scraping. Enforced by national Data Protection Authorities; the European Data Protection Board (EDPB, formerly Art. 29 Working Party) coordinates one-stop-shop + Article 65 binding-decision procedures across DPAs.

Operational bridge between EU AIA Arts. 53-55 (general-purpose AI obligations) and provider compliance. Art. 56(8) AIA gives adherent providers a presumption of compliance with the substantive obligations — distinct from industry self-pledges (Anthropic RSP, OpenAI Preparedness, DeepMind FSF) and from intergovernmental voluntary codes (Seoul, G7 Hiroshima). Chapter 1 (Transparency) operationalises Art. 53(1)(a)-(c) model documentation + training-data summary obligations; Chapter 2 (Copyright) operationalises Art. 53(1)(c) opt-out compliance + Art. 53(1)(d) text-and-data-mining respect; Chapter 3 (Safety & Security) operationalises Art. 55 systemic-risk-tier obligations including capability evaluations + serious-incident reporting + cybersecurity protections + model-weight access controls. AI Office monitors implementation; Article 65 binding-decision procedure routes cross-DPA disputes. Not a binding regulation in itself — providers may choose alternative means to demonstrate compliance — but the Code is the AI Office's canonical reference and the operational rulebook national-competent-authorities consult during inspections.

Partially rescinded by EO 14179 (Jan 2025). Some §4 reporting persists via Defense Production Act + BIS interim rule.

Rescinds EO 14110's regulatory-burden provisions. Directs OMB / OSTP / NSC to remove barriers to AI development. Does NOT itself impose new substantive obligations — coverage is mostly silent. The DPA-grounded compute-reporting interim rule (BIS, Jan 2025) and Defense Production Act §708 reporting persist independently.

Voluntary. Four functions (Govern / Map / Measure / Manage). GenAI Profile (NIST AI 600-1) added 2024 for GPAI-specific guidance.

Companion to NIST AI 100-1 covering GenAI-specific risks: CBRN information uplift, confabulation, data privacy, environmental impacts, harmful bias, dangerous information, IP misuse, obscene/abusive/violent content, information security, information integrity, human-AI configuration, value chain and component integration. Voluntary.

First US state-level model-testing mandate. Passed CA legislature Sep 2024; vetoed by Gov. Newsom Sep 29, 2024. Re-introduction expected 2025-2026 with amendments. Would have required pre-deployment third-party testing for models above 10^26 FLOPs OR $100M+ training cost. Cited in every 2024-2025 AI governance literature review as the most impactful US state intervention.

First-mover industry safety framework. Introduces the AI Safety Level (ASL) capability-tier vocabulary subsequently adapted by OpenAI Preparedness + DeepMind FSF. v2 (Oct 2024) refines ASL-3/ASL-4 capability thresholds, mandates pre-deployment capability evaluations, and commits to a Frontier Red Team. Seoul Frontier AI Safety Commitments signatory; cited by name in EU AI Office GPAI Code of Practice drafts. NOTE (iter-314): the RSP is a versioned-evolving artefact; this row pins v2 (Oct 2024) as the load-bearing reference, but Anthropic publishes incremental updates on the policy page. Citers tracking specific ASL-4 threshold language should confirm against the current version on anthropic.com — the catalog re-pins on the next Coverage Games event.

Capability-tier risk evaluation regime with four categorical levels (Low / Medium / High / Critical) across four risk categories (cybersecurity, CBRN, persuasion, model autonomy). Pre-deployment evaluation against the framework gates release decisions; Safety Advisory Group + board-level Safety & Security Committee govern threshold determinations. Seoul Frontier AI Safety Commitments signatory. NOTE (iter-314): the Preparedness Framework is a versioned-evolving artefact; this row pins the originally-published Dec 2023 version, but OpenAI publishes updates on the safety/preparedness page. Citers tracking specific risk-category language or threshold definitions should confirm against the current published version — the catalog re-pins on the next Coverage Games event.

Critical Capability Levels (CCL) regime spanning autonomy, biosecurity, cybersecurity, and persuasion domains. Distinct vocabulary from Anthropic ASL + OpenAI Preparedness — designed for cross-domain elicitation; each CCL triggers domain-specific mitigations including model-weight access controls + enhanced red-teaming. Seoul Frontier AI Safety Commitments signatory. Alphabet-published; effective across Google DeepMind frontier-model releases. NOTE (iter-314): the FSF is a versioned-evolving artefact; this row pins v1 (May 2024) as the load-bearing reference, but DeepMind publishes incremental updates on the deepmind.google blog. Citers tracking specific CCL definitions or mitigation requirements should confirm against the current published version — the catalog re-pins on the next Coverage Games event.

Meta's open-weight-frontier governance posture. Categorises frontier models into 'high risk' + 'critical risk' tiers; the framework's distinctive feature is its explicit defence of open-weight release as a governance posture (vs. the closed-model stance of Anthropic / OpenAI / DeepMind). Pre-release threat modelling + post-release monitoring; commits to halt training if critical-risk threshold reached without mitigations. Seoul Frontier AI Safety Commitments signatory.

First broad-spectrum US industry commitments; precursor to EO 14110 §4.2(a) reporting + the Seoul Frontier AI Safety Commitments. 15 signatories across two tranches (Jul + Sep 2023): Anthropic, OpenAI, Google DeepMind, Microsoft, Meta, Inflection, Amazon (Jul); Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, Stability AI (Sep). Eight commitment areas: internal + external security testing, info sharing, cybersecurity investment, third-party vuln disclosure, watermarking, public reporting, prioritising research on societal risks, deploying AI to address societal challenges.

Binding on covered federal agencies. Three pillars: (I) strengthen AI governance through agency Chief AI Officers + AI Governance Boards; (II) advance responsible AI innovation including authorized use, talent, and infrastructure; (III) manage risks from agency AI use with mandatory minimum practices for safety- and rights-impacting AI by December 1, 2024. Agencies must publicly inventory their AI uses annually (continuing the EO 13960 + EO 14110 inventory tradition) and report AI procurements quarterly. Attachment 1 sets the operative risk-management minimum practices (AI impact assessment, real-world performance testing, independent evaluation, ongoing monitoring, public notice + plain-language explanation, human oversight + opt-out for rights-impacting uses).

Procurement-focused operational guide accompanying OMB M-24-10 and the broader EO 14110 / EO 14179 federal-AI policy stack. Provides agencies with: (1) market intelligence on the GSA Multiple Award Schedule special item numbers covering AI services (54151S IT Professional Services + the newer AI / Generative AI SINs); (2) sample acquisition language for responsible-AI requirements (bias-testing, transparency, evaluation, security); (3) supply-chain risk-management considerations including model-provenance and dependency disclosure; (4) requirements derivation guidance for safety- and rights-impacting AI per OMB M-24-10 Attachment 1. The guide is non-binding on its own but agencies typically incorporate its language into solicitation packages.

DoD-wide operational pathway implementing the five Ethical Principles for AI (Responsible, Equitable, Traceable, Reliable, Governable; adopted Feb 24, 2020). Six foundational tenets: (1) RAI Governance — clarifies roles between OUSD(R&E), OUSD(A&S), DoD CIO, CDAO; (2) Warfighter Trust — calibrated reliance, T&E, V&V; (3) AI Product and Acquisition Lifecycle — RAI integrated into requirements, contracting, sustainment; (4) Requirements Validation — JCIDS gating; (5) Responsible AI Ecosystem — supply chain, data sourcing, vendor disclosure; (6) AI Workforce — RAI training across acquisition workforce. The S&IP is paired with a DoD RAI Toolkit (CDAO-maintained) of templates + sample contract language. Distinct from DoDD 3000.09 (Autonomy in Weapon Systems) which governs LAWS-specific decisions and was separately updated Jan 2023.

Operational PMO guidance for agencies acquiring AI / generative-AI cloud services within the existing FedRAMP authorisation framework. Key operational themes that recur across the published surface: (1) AI cloud services that process federal data require a FedRAMP ATO (Low / Moderate / High baseline) per the standard FedRAMP scope; (2) GenAI-specific control tailoring — agencies + JAB consider model-specific risks (training-data exposure, prompt-injection, output disclosure) when scoping the SSP + selecting NIST SP 800-53 control overlays; (3) cross-walk to OMB M-24-10 minimum practices for safety- + rights-impacting AI; (4) supply-chain risk-management considerations for model + dataset provenance; (5) agency authorising-official discretion remains the operative gate — FedRAMP authorisation enables but does not by itself approve a specific AI use case (M-24-10 governance applies separately). Editorial note: limited public detail on this row reflects the PMO's web-page-plus-memo distribution pattern; a consolidated GenAI baseline document is the natural next milestone and would refresh this row.

Defense-acquisition-specific information-security regulation. Core clauses: (1) DFARS 252.204-7012 (adopted 2015, current consolidated 2020) — requires contractors handling Covered Defense Information (CDI) on covered contractor information systems to implement NIST SP 800-171 r2 security controls + report cyber incidents to DoD within 72 hours; (2) DFARS 252.204-7019 / -7020 / -7021 (CMMC interim rule Nov 2020) — implements the Cybersecurity Maturity Model Certification framework requiring increasingly stringent third-party attestation of NIST 800-171 implementation by contract tier. AI relevance: (a) AI-system source code, model weights, training data, and architecture documentation produced or stored on contractor systems fall within CDI when the underlying contract is so designated; (b) cyber-incident reporting in 252.204-7012(c) applies equally to AI-system compromise events (training-data exfiltration, model-weight theft, prompt-injection-based credential exposure); (c) supply-chain risk-management linkages with FAR Part 4 Subpart 4.21 + the DoD RAI S&IP supply-chain tenet. Distinct from AI-specific DFARS clauses under consideration as part of DoD Acquisition Innovation initiatives — none of which have been finalised at the catalog-write date.

Principles-based, regulator-led approach (no statutory AI law). Cross-sectoral principles delegated to existing regulators. AISI established Nov 2023 for evaluation/safety research.

Joint CAC/MIIT/MPS measures. Registration + safety assessment for public-facing generative AI. Aligns with Algorithm Recommendation Rules (2022) and Deep Synthesis Rules (2022).

Voluntary commitments by frontier AI developers. 11-point code covering risk identification, deployment, content provenance, security investment, info sharing.

First intergovernmental standard. Updated 2024 to clarify GPAI scope. Foundation referenced by G7, GPAI, and many national frameworks.

First legally-binding international treaty on AI. Opened for signature Sep 2024. Enters into force three months after five ratifications including three CoE members.

Non-binding. Calls on member states to bridge digital divides and develop national strategies. China + US co-sponsored; passed by consensus.

First multilateral consensus on frontier-AI safety risks. 28 signatories including US, EU, China. Introduced the policy vocabulary of 'frontier AI' that later instruments adopted. Non-binding but precedent-setting; spawned the AI Safety Institute network.

Bletchley follow-up. 16 frontier-AI-developer companies signed Frontier AI Safety Commitments alongside. Introduces measurable capability-evaluation expectations and pre-deployment thresholds; first instrument to formalise frontier-lab voluntary commitments as a governance category.

First binding bilateral on frontier-AI safety. Commits both AISIs to coordinated pre-deployment evaluations, red-team data sharing, methodological alignment on capability elicitation, and joint exercises across at least one major frontier-model release. Precedent for the broader AISI network (US, UK, JP, SG, CA, FR, KR) consolidated at the Seoul Summit; cited in Seoul Declaration §5-7 operationalising international coordination.

India's primary AI-adjacent statute is the DPDPA + MEITY's binding AI advisories (Mar 2024 + Apr 2024 walked-back versions). No dedicated AI law yet; the proposed Digital India Act was paused 2024-2025. Affects 1.4B people — the single largest population under any AI-governance regime tracked here.

Risk-based framework structurally similar to EU AIA but with distinct development-rights framing rooted in Brazil's Marco Civil tradition. Senate-approved Dec 2024; Chamber of Deputies vote pending 2025. Notable for explicit human-dignity + collective-rights provisions absent from EU AIA. Sets a precedent for Latin American AI regulation if enacted.

Non-binding voluntary guide for 10 ASEAN member states (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, Myanmar, Cambodia, Laos, Brunei). Adopts a cross-cutting risk + values framework intentionally distinct from the EU AIA's prescriptive model — emphasises 'pragmatic + flexible' implementation reflecting member-state capacity diversity. Pairs with Singapore AI Verify Foundation's technical toolkit.

Continental-level non-binding strategy for 55 AU member states. Frames AI through development-rights / digital-sovereignty / capacity-building lens. Explicitly references unequal compute access + dataset coloniality as governance concerns absent from OECD-bloc instruments. Operationalisation via national strategies (e.g., Egypt 2030, Kenya AI Roadmap, South Africa NAIPF).

Update to the 2020 Model AI Governance Framework (v2), expanding scope to generative AI. Nine dimensions: accountability, data, trusted development + deployment, incident reporting, testing + assurance, security, content provenance, safety + alignment R&D, AI for public good. Pairs with the AI Verify Foundation's open-source technical-testing toolkit. Voluntary; cited as the ASEAN-aligned reference for technically-grounded governance and influential beyond ASEAN-10.

Joint METI + MIC issuance consolidating prior AI Utilization Guidelines (2019) + AI R&D Principles (2017) into a single business-facing framework. Voluntary; explicitly aligned with G7 Hiroshima AI Process Code of Conduct + OECD AI Principles. Ten core principles spanning fair competition, accountability, transparency, education, AI safety. Companion of the Hiroshima AI Process Reporting Framework Japan operationalises; reflects Japan's preferred soft-law posture vs. the EU AIA's prescriptive model.