A policy report arguing that AI governance must move beyond fragmented national approaches toward a UN-centered global framework built around four pillars: safety, inclusiveness, clarified responsibilities, and multilateral coordination.
This issue highlights the newest and best in AI governance — the ideas shaping oversight, risk, and policy today. Future editions will explore new lenses: practical tools, policy guides, and training resources. Each one will bring a fresh perspective — and your voice is welcome.
Many vendors overclaim… focusing on superficial jailbreak demonstrations… while ignoring systemic vulnerabilities, workflow bypasses, misuse risks, and failures in agentic or tool-enabled systems.
AI is increasingly being used to augment and automate cyber operations… raising urgent questions about when AI systems introduce unacceptable cyber risk, and how risk thresholds should be identified before harms materialize at scale.
The OECD.AI Index… is a composite measurement framework designed to assess countries’ implementation of the OECD Recommendation on Artificial Intelligence.
An ability of individuals, organisations, and systems to perform AI regulatory functions effectively, efficiently, and sustainably across the lifecycle...
Governance is not merely a set of external constraints… but an intrinsic feature of any AI system, adaptable to evolving risks and contexts.
Frontier AI both amplifies existing risks and introduces qualitatively novel challenges… emerging safety practices are often misaligned with established risk management frameworks.
AI actors should carry out risk-based due diligence to identify, prevent, mitigate and account for adverse impacts of AI systems across their lifecycle.
Model Risk Management (MRM) must evolve from periodic validation to continuous governance as Generative and Agentic AI systems introduce opacity, stochasticity, and autonomy that break traditional supervisory assumptions.
The AICM provides a foundational security and governance framework… establishing a clear and actionable set of controls… tailored to the unique risks and responsibilities inherent in AI development, deployment, and consumption.
This Report assesses what general-purpose AI systems can do, what risks they pose, and how those risks can be managed… focusing on emerging risks at the frontier of AI capabilities.
Insurance can help ensure that AI-related harms are mitigated, and that AI’s risks and benefits are fairly shared… by offering risk-based premiums tied to safety standards.
Agents don't just generate content; they act, adapt and influence digital and physical environments across extended time horizons, with compounding and potentially irreversible consequences.
AI is rapidly becoming deeply embedded in contemporary societies… reshaping industries, economies, and societies… governments are establishing policies, legal frameworks and ethical guidelines to ensure responsible use.
This framework aims to help organizations harness the power of generative artificial intelligence (GenAI) while appropriately managing its risks.
A Fundamental Rights Impact Assessment (FRIA) supports identifying, assessing and mitigating risks AI systems may pose to individuals’ rights, ensuring compliance with the EU AI Act and broader human rights standards.
The United States and its partners must get ready for what the worst people in the world might do with the most advanced AI models.
AI vendor contracts are not just SaaS agreements… they raise issues… who owns the output, whether your data trains someone else’s model… and what happens when the AI gets it wrong.
ISO 42001 describes how to assess those risks, and how to introduce the right AI controls — in other words, it describes how to use AI governance to build trustworthy AI systems.
Cloud-based AI gives affordable access to advanced tools… Small businesses must take proactive steps to protect data, customer privacy and business systems.
AI Verify testing framework aims to help companies assess their AI systems against 11 internationally recognised AI governance principles… Each item consists of outcomes, processes, and evidence to validate implementation.
This checklist is designed to be a definitive, testable reference for IT auditors and AI security professionals… structured into ten key domains, with each control mapped to mandatory global standards and frameworks.
The MEASURE Function provides the empirical evidence base for AI risk management by defining metrics, assessing data quality, evaluating system performance, and implementing continuous monitoring.
Curated Library of AI Governance Resources