This pre-release report compares AI governance capacity across 40 countries using 43 indicators in four pillars. It sets out findings, indicator definitions, data sources and methods for calculating the AGILE Index.
International guidance for providers developing machine-learning AI systems. It sets lifecycle practices for secure design, development, deployment and operation, addressing adversarial ML, supply chains, model protection, monitoring and incident response.
An independent assessment of seven leading AI companies’ practices for managing immediate harms and catastrophic risks from advanced AI. It grades 33 indicators across six domains and sets out company-specific improvement opportunities.
A voluntary resource for New Zealand businesses using or developing AI systems responsibly. It sets out a proportionate, risk-based approach spanning governance, data, procurement, security, stakeholder engagement and generative AI.
A Google Cloud report benchmarking reported generative AI value across global enterprises. It presents survey findings on agent adoption, returns, investment priorities, security concerns, and an AI agent ROI checklist.
A 2025 report presenting aggregated evaluation trends for frontier AI systems across autonomy, chemistry and biology, cyber, safeguards, loss-of-control risks, societal impacts and open-source models. It outlines methods, findings and limitations.
This issue brief consolidates recommendations from AI, cybersecurity, privacy and risk-management guidance into a single framework. It presents 258 harmonized recommendations across 34 topics and five categories, with AI Scores showing the contribution of AI-specific sources.
A CFTE whitepaper defining AI literacy through five core components and a scalable framework. It offers organisational training approaches for responsible AI use, critical evaluation and continuous workforce upskilling.
A HUX AI guide to implementing an AI Management System under ISO/IEC 42001:2023. It explains the standard’s Clauses, roles, risk processes and lifecycle controls through a fictional AI hiring-tool case study.
This white paper sets out a proposed techno-legal approach to AI governance in India, combining legal instruments, regulatory oversight and technical controls. It maps lifecycle risks, potential safeguards, enabling technologies and proposed institutional mechanisms.
This report presents a framework of seven AI Readiness Objectives for governments seeking to securely harness advanced AI. It groups AI-security interventions into three pillars and adds six preliminary AI Innovation Priorities.
"The 'right' level of maturity is not universal, it is determined by your organization’s specific AI adoption pattern, industry, regulations, and risk tolerance."
As businesses scale AI across functions and processes, the absence of clear policies, oversight mechanisms and accountability frameworks can lead to unpredictable agent behavior, especially when exposed to real-world data and complex business environments.
Governance of a multi-agent system is not just scaled-up single agent governance... Failures do not decompose across individual agents, which means the target of their governance cannot be individual agents, but rather has to be the system itself of interacting agents.
Across all model types and deployment settings, pre-deployment evaluations for dangerous capabilities and safeguard robustness are essential inputs for responsible release decisions. Neither proprietary nor open-weight models are inherently dangerous; risk depends on their capabilities.
The organization shall define and put in place a process to report concerns about the organization's role with respect to an AI system throughout its life cycle.
Building on CSET’s harmonized AI framework, this report collates actionable guidance from these resources into an easy-to-navigate format and connects the information back to core recommended practices, establishing a critical link between high-level principles and practical implementation details.
ISO 42001 establishes the formal policies and roles. NIST AI RMF provides the practical, risk-based steps to identify and measure harms in your specific operational context. Sequencing both creates a system that is certifiable and resilient.
We address this by creating the AI Risk Repository: a living database of 1,725 risks extracted from 74 existing taxonomies and frameworks. We organize these risks using two complementary classification systems.
Post-deployment measurement and monitoring is necessary (1) to validate that an AI system is operating reliably and as expected in real-world scenarios, (2) to track unforeseen outputs and drift, and (3) to identify unexpected consequences of integrating AI systems in new or changing contexts.
A practitioner's map of published international standards for AI systems, covering foundational concepts, governance, trustworthiness, data quality, lifecycle management, computational approaches, use cases, and conformity assessment.
No universally accepted definition of artificial intelligence exists — a gap that carries direct consequences for regulation, compliance obligations, and regulatory scope. This textbook proposes the AI Governance Stack as its central organizing framework: a five-layer operational model.
We may never fully understand how some AI systems arrive at their answers. Governance cannot wait for perfect explainability. The real test is whether organisations can define acceptable boundaries, detect when systems cross them, and respond when the machine surprises them.
AI risk management is the systematic and continuous application of management policies, processes and practices to the tasks of analysing, evaluating, controlling and monitoring risks throughout the entire lifecycle of an AI system.
Curated Library of AI Governance Resources