Structural Tests for AI Systems: 15 Checks for Regulators, Auditors and Compliance Officers
If a system fails even one, it’s structurally out of compliance and regulators can prove it – in minutes.
The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
An analysis of nearly 460,000 AI model cards shows developers overwhelmingly report technical risks—while real-world harms arise more from misuse and misinformation.
STATE OF AI IN BUSINESS 2025
Despite $30–40B invested in GenAI, 95% of businesses see no ROI. The divide is not in adoption—but in impact. This report maps the real reasons behind failure and what separates top performers from stalled efforts.
The ROI of AI 2025
“88% of agentic AI early adopters are now seeing a positive ROI on gen AI
Trusted Third-Party AI Assurance Roadmap (UK Government, DSIT, September 2025)
This roadmap sets out our ambitions for the third-party assurance market in the UK and the immediate actions that government will take to support this emerging sector.
What Even Superintelligent Computers Can’t Do
This RAND working paper proposes a framework for identifying tasks that even Artificial General Intelligence (AGI) would be fundamentally unable to perform, grounded in constraints from physics, information theory, and computational complexity.
AI Governance Library Newsletter #9:
✒️ Foreword While the U.S. frames AI as a tool for innovation, economic growth, and military advantage, China is building
THE LAW, ETHICS AND POLICY OF ARTIFICIAL INTELLIGENCE
An interdisciplinary legal-philosophical textbook covering AI’s ethical foundations, regulatory implications, and governance challenges through technical, philosophical, and legal lenses.
Responsible AI Guidance for Businesses: Investing with Confidence
A voluntary guide tailored to New Zealand businesses, showing how existing governance, legal, and operational practices can anchor AI risk management—without requiring a ground-up rebuild.
Solving the AI ROI Puzzle
Organizations with a Chief AI Officer (CAIO) see 10% higher ROI on AI—and up to 36% more when using centralized operating models. But only 1 in 4 organizations have a CAIO today.
AI Safety Index – Summer 2025 Edition
A detailed, scored assessment of how seven frontier AI developers approach safety—tracking real practices, not just promises, across 33 indicators in six domains.
Guidelines for secure AI system development
Security-by-default for AI: practical, lifecycle-wide guidance to help providers build and operate AI systems that resist misuse, protect data, and remain reliable—even under attack.
AI Governance International Evaluation Index (AGILE Index) – 2025 Edition
The AGILE Index ranks countries on their AI governance readiness—not by intent, but by measurable capability, institutional maturity, and actual regulatory power.
AIGN Agentic AI Governance Framework v1.0
A foundational governance model for AI systems that act autonomously, delegate tasks, or interface with external tools—built to handle autonomy, unpredictability, and systemic risk.
MCP 2025 Edition: The Illustrated Guidebook
A protocol to connect AI models with external tools and data sources through a shared interface, solving the M×N integration problem for developers.
The AI Policy Playbook: 2024 Edition
A modular toolkit for lawmakers, researchers, and advocates to shape effective, rights-respecting AI policy—built on global principles, practical levers, and tested language from 40+ jurisdictions.
The Mitigating ‘Hidden’ AI Risks Toolkit
A policy-first toolkit for identifying and mitigating overlooked or under-prioritized AI risks—especially those affecting marginalized groups, low-visibility use cases, and long-term governance gaps.
AIGN AI Governance Culture Framework
Culture is governance’s invisible backbone. This framework helps leaders identify, assess, and embed cultural levers—from values to incentives—that shape responsible AI behavior across organizations.
SAIL Secure AI Lifecycle Framework, Version 1.0 – June 2025
A lifecycle-driven framework offering 70+ mapped AI-specific risks, actionable safeguards, and crosswalks to ISO 42001, NIST AI RMF, OWASP Top 10 for LLMs, and more – aimed at embedding security into every phase of AI development and deployment
Databricks AI Governance Framework
A practical, product-integrated framework for managing AI risks across the ML lifecycle—rooted in Databricks’ tooling and aligned with enterprise data governance priorities.
Privacy and Data Protection Risks in Large Language Models (LLMs)
A structured guide to identifying and mitigating privacy risks in LLMs—covering data leakage, user inference, training data exposure, and strategies for auditability and control.
THE MODEL ARTIFICIAL INTELLIGENCE LAW MAIL v.3.0
A detailed legislative framework proposing oversight, obligations, and incentives for AI systems—covering everything from foundation models and open-source exemptions to risk-based licensing and whistleblower protection.
AI Governance Library Newsletter #8: Putting Dollars on the Ethical AI
Scaling governance means more than more rules—it means better tools. This issue explores AI-assisted GRC, ethics-as-ROI, and governance for agents. Five standout reviews show where oversight is finally catching up with the systems we build.
Three Lines of Defense Against Risks from AI
A strategic proposal to professionalize AI risk governance.
