A comprehensive analysis of how global AI governance is emerging, fragmenting, and evolving, using regime complexity, sociotechnical change, and governance disruption as core analytical lenses.
AI agents are adaptive, interactive systems that must be continuously evaluated, secured, governed, and improved due to the nondeterministic nature of underlying LLMs.
AI capabilities are improving rapidly across all tested domains. Performance in some areas is doubling every eight months, and expert baselines are being surpassed rapidly.
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.
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.
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.
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.
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.
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.
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.
“Treat policy development as a living process… A static policy is a risk.” (see Exec. Summary key takeaway on p.7 and Chapter 3 on continuous review, pp.20–22)
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.
Curated Library of AI Governance Resources
