Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
A comprehensive framework by the Cloud Security Alliance proposing four integrated pillars—Model Cards, Data Sheets, Risk Cards, and Scenario Planning—to proactively identify, assess, and mitigate risks across the AI/ML lifecycle.
AI literacy is the ability to understand, evaluate, and confidently use AI technologies, recognising both their capabilities and limitations in personal, professional, and societal contexts.
This white paper presents a comprehensive framework for AI incident response, adapting proven practices from complex systems domains to address non-deterministic, context-dependent, and interconnected AI failures.
AI is increasingly used by foresight practitioners to accelerate horizon scanning, trend analysis and scenario development, but it raises significant challenges around trust, bias, transparency and governance.
An analytical framework for understanding AI governance proposals by surfacing their shared and divergent assumptions, helping policymakers act under uncertainty while preserving future decision-making flexibility.
This whitepaper explores how organizations can secure generative AI systems, use AI to enhance cybersecurity operations, and defend against AI-powered threats through layered controls, automated reasoning, and responsible AI governance.
Agentic AI systems go beyond automation by acting autonomously, learning from interactions, and orchestrating multiple agents to achieve complex goals—raising both transformative opportunities and amplified governance challenges.
This whitepaper provides a comprehensive overview of strategies and tools for securing and governing agents within Microsoft 365, focusing on data access control, compliance, and scalable agent management across organizations.
With the rapid acceleration of AI adoption, organizations must move beyond legal compliance and embed ethical principles, governance processes, and risk ownership to avoid real-world harm and reputational damage.
This guide supports organisations in conducting Fundamental Rights Impact Assessments under the EU AI Act, outlining criteria, methodologies, and a phased process to identify, assess, mitigate, and monitor AI-related risks to fundamental rights.
Voluntary, risk-based guidance from NIST that helps organizations govern, map, measure, and manage AI risks so that AI systems are not only high-performing, but also safe, fair, accountable, and aligned with societal values.
An empirically grounded taxonomy based on 202 real-world AI incidents (2023–2024) maps types, causes, responsible entities, disclosure sources, and impacts—showing that most harms stem from organizational decisions and legal non-compliance, while formal reporting and remediation remain rare.
A 2025 snapshot of U.S. state AI lawmaking: 210 bills in 42 states, few enacted, and a clear pivot from sweeping “high-risk AI” frameworks toward narrower, transparency-first rules, with growing attention to chatbots, health uses, and liability levers.
Draft European Standard setting baseline, lifecycle-wide security requirements for AI models and systems—spanning secure design, development, deployment, maintenance and end-of-life—with clear provisions for developers, system operators and data custodians.
Comparative memo mapping Frontier Safety Frameworks to classic risk-management standards, extracting convergences, gaps, and concrete recommendations to harden frontier AI governance with clearer criteria, processes, and external assurance.
A field-tested playbook for turning Responsible AI principles into day-to-day practice. It distills workshops with 30+ practitioners into five building blocks—People, Priorities, Processes, Platforms, and Progress—with concrete trade-offs, patterns, and pitfalls.
A crisp research agenda for policymakers: define agent tech and use cases, quantify risks/benefits, and test interventions (sandboxes, monitoring, assurance) before regulation hardens—so rules arrive with evidence, not guesswork.
A framework for keeping AI systems “legible” by preserving human-readable chain-of-thought, weighing the competitive “monitorability tax,” and mapping voluntary, domestic, and international policy tools—plus how to verify compliance without leaking IP.
Pilot from MIT AI Risk Initiative, FutureTech, MIT & CSET uses LLMs to classify 950+ AGORA governance docs against MIT AI risk/mitigation taxonomies, compares models vs. human raters, and surfaces coverage gaps by risk domain and sector.
A concise, policy-ready map of how ISO/IEC standards turn AI principles into practice—spanning risk, data quality, transparency, sustainability, and conformity assessment—so governments and industries can align fast without fragmenting global markets.
A practical, visual walkthrough of ISO/IEC 42001 that turns clauses into checklists, roles, and a full HR hiring use case—bridging policy and day-to-day execution for an AI Management System.
A concise “key update” on fast-moving frontier AI: reasoning models, agentic capabilities, and what they mean for biosecurity, cyber, labour, and oversight—plus early evidence on deceptive behaviors in evals and why developers shipped stronger safeguards.
“The shift from scaling up pre-training compute to scaling up inference compute may have profound effects on AI governance… potentially undermining measures that rely on training-compute thresholds.”
A seven-page, form-style reporting template (v1.0.0, SB-10407) for notifying market surveillance authorities of serious incidents involving high-risk AI systems. It structures admin data, system details, incident description, provider analysis, and general statements.
Curated Library of AI Governance Resources
