Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
The Safety and Security Chapter of the EU Code of Practice for General-Purpose AI Models, setting out ten Commitments and Measures for systemic risk assessment, mitigation, reporting and governance by providers of models with systemic risk.
OECD Artificial Intelligence Papers No. 27: a review of research and expert perspectives on potential future AI benefits, risks and policy actions, drawing on the OECD Expert Group on AI Futures and ranking ten priority items in each category.
European Commission guidelines interpreting the Article 50 transparency obligations of Regulation (EU) 2024/1689 for interactive AI systems, synthetic content marking, emotion recognition and deep fakes, with conditions, exceptions and worked examples.
A model state bill from the Future of Life Institute that designates frontier AI models as products and their developers as manufacturers, authorises joint and several liability across the AI value chain and lists factors for judging defectiveness.
A crosswalk mapping the subcategories of the NIST AI Risk Management Framework's GOVERN, MAP, MEASURE and MANAGE functions to the clauses and Annex B controls of ISO/IEC FDIS 42001, the AI management system standard.
A model state bill from the Future of Life Institute that would impose strict liability on frontier AI developers for catastrophic harms, treating frontier model development as an ultra-hazardous activity.
A model state bill from the Future of Life Institute that would require independent pre-deployment safety testing of conversational AI systems used by minors, impose a duty of care, set operational and data safeguards for minors, and create strict liability and a private right of action.
An IAPP one-page explainer on the EU NIS2 Directive: which organisations and sectors are in scope, the Article 21 cybersecurity requirements, Article 23 incident-reporting obligations and timeline, enforcement powers, penalties and key dates.
Feedback submission from the Future of Life Institute on the Commission's draft implementing regulation for GPAI model evaluations and enforcement proceedings under Articles 92 and 101 of the EU AI Act, supporting key provisions and setting out seven recommendations.
Supplementary material to the article Managing extreme AI risks amid rapid progress, credited to Yoshua Bengio and co-authors. It reproduces the article text with 73 additional citations and adds an extended reference list on extreme AI risks, technical safety R&D and frontier AI governance.
A best-practices slide deck setting out six clauses for an AI addendum to a vendor agreement, each with sample Pro-Customer and Pro-Vendor wording on consent, data ownership, model training, legal compliance, ethical use and liability.
World Economic Forum insight report, published with PwC, on how generative AI can augment jobs and raise workforce productivity, offering four scenarios, interview insights from more than 20 early adopters and a framework for action.
Microsoft's 2026 Responsible AI Transparency Report describes how the company governs the AI it develops, releases, operates and uses, covering its re-engineered Responsible AI Standard, NIST AI RMF-aligned risk management, agent governance tooling and ecosystem engagement.
A paper from the Responsible AI Institute presenting the TrustX Agent Risk Classification Framework, a twelve-dimension rubric, GPA + IAT checklist and five-level autonomy scale that tier agentic AI systems into three risk levels with mapped controls, plus a coding assistant extension.
Part 2 of a four-part LinkedIn series on ISO/IEC 42001:2023, summarising clauses 4 to 10 of the AI management system standard as tables of requirement areas and organisational actions, with a closing map of clauses to templates and tools.
A systematic literature review of evaluation criteria and metrics for Trustworthy AI, mapped to the seven EU principles, with a proposed classification per principle and a mortgage lending case study.
SaferAI paper proposing a four-part frontier AI risk management framework — risk identification, analysis and evaluation, treatment and governance — built around an explicit risk tolerance operationalized into KRI and KCI thresholds linked by if-then rules.
Published by Deloitte India, this guide helps Global Capability Centre leaders assess readiness for generative AI, identify, qualify and prioritise use cases, run proofs of concept, scale to production and apply trustworthiness and governance checks.
Synthesis of the scientific evidence on the safety of general-purpose AI: capabilities, risks from malicious use, malfunctions and systemic effects, and technical risk management. Produced by 96 experts ahead of the AI Action Summit.
A sponsored MIT Technology Review Insights report, based on a March 2024 survey of 205 executives and expert interviews, on moving enterprise AI from pilots to scale: data foundations, costs and ROI, vendor partnerships, risk, security and regulation.
Analysis of the acceptable use policies of 30 foundation model developers, coding 127 prohibited use categories and examining enforcement, fragmentation across the AI supply chain and effects on the foundation model market.
Kodex AI white paper on adopting generative AI in banking: a three-stage use-case roadmap, quality determinants and benchmarks, GenAI risks with mitigants, and implementation recommendations for regulated financial firms.
The second Public Sector Tech Watch report from the European Commission analyses 1,617 cases of AI, blockchain, virtual worlds, AR/VR and quantum computing in European public administrations, and closes with six policy recommendations.
A PwC Middle East executive playbook on agentic AI, covering definitions, the evolution to multimodal agents, service-as-a-software and copilot-to-autopilot models, cross-industry case studies, agent tool comparisons, a six-step adoption roadmap and do's and don'ts.
Curated Library of AI Governance Resources
