Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
Excerpt (≤300 chars) CSET condenses 7,741 recommendations from 52 global AI, cybersecurity, privacy, and risk documents into 258 harmonized best practices—creating a single, validated framework that unifies fragmented voluntary standards into five governance-to-response categories.
The report shows, through predictive-policing simulations and offensive-speech classifiers, how bias enters AI systems, when it can amount to discrimination, and which safeguards (testing, explainability, lawful equality data) are needed to detect and mitigate harms.
A crisp, UK-centric roadmap for evaluating AI rules when costs/benefits are uncertain and risks may be near-existential—mixing CBA limits, precaution, qualitative break-even scales, and a “real-options” lens, with compute-as-proxy ideas and lifecycle-aware governance.
A landmark ITU publication summarizing outcomes from the AI for Good Global Summit 2025, uniting ISO, IEC, and 53 UN partners to advance global standardization in AI governance, multimedia authenticity, agentic AI security, health, energy, and sustainability.
A concise, editable AI policy template from Australia’s National AI Centre. It turns high-level ethics into actionable statements, roles, and workflows—covering purpose, scope, seven policy principles, governance, incident response, and review cadence.
Practical, control-oriented playbook that turns the EU AI Act, GDPR and ISO 42001 into an implementable set of organizational and technical controls—mapping from strategy to lifecycle, with concrete evidence requirements and “minimum viable governance” to avoid compliance theater.
A practical, outcomes-driven playbook for governing AI agents—covering risks, adoption pathways, and a five-part taxonomy of interventions (Alignment, Control, Visibility, Security & robustness, Societal integration), with examples and vignettes.
OWASP’s practical cheat sheet for teams adopting third-party Model Context Protocol (MCP) servers: concrete mitigations for tool poisoning, prompt injection, memory abuse, client security, discovery/verification, authZ/authN, sandboxing, and a lightweight governance workflow.
An industry snapshot of Africa’s fast-moving digital decade: cloud adoption is accelerating, subsea cables are rewiring capacity, AI spend is small but rising, and data sovereignty is shaping a hybrid, locally anchored infrastructure strategy.
Microsoft’s second transparency report details policies, tooling, and review processes for building and deploying AI, with deep dives on EU AI Act readiness, red teaming (incl. PyRIT), Azure AI Content Safety, Sensitive Uses reviews, and case studies across Copilot, Phi models, and healthcare.
AIMA adapts the foundational concepts of OWASP SAMM to the unique realities of AI lifecycle engineering … enabling incremental improvement rather than disruptive transformation.
A comprehensive taxonomy of security threats facing AI systems—from training data leakage and adversarial attacks to prompt injection and model theft—with practical mitigation strategies.
This report by the Coalition for Cybersecurity in Asia-Pacific offers a comprehensive framework for managing cybersecurity risks tied to AI systems, particularly in sectors critical to the region’s growth.
A formal, systemic deep dive into the inevitability of LLM hallucinations, presenting a layered taxonomy, causes, metrics, and mitigation approaches.
A practical guide for businesses and public organizations navigating the EU AI Act, focusing on risk-based obligations, roles, and regulatory timelines for AI systems, general-purpose models, and chatbots.
If we create AI systems that merit moral consideration, simultaneously avoiding both misalignment and mistreatment becomes extraordinarily difficult.
A practical legal guide to AI governance, aligning organizational practices with the EU AI Act, GDPR, and ethical risk management principles, especially for Polish companies using Microsoft AI solutions.
This resource provides secure design patterns and practices for teams developing LLM- powered applications. Each section is dedicated to a type of application. For each application type, we outline the most significant risks and provide mitigation strategies.
AI systems bring new opportunities—but also novel vulnerabilities. This white paper offers a structured framework for balancing innovation with cybersecurity, aiming to embed cyber risk management throughout the AI lifecycle.
This report presents the findings of research into the use of AI by public authorities and its impact on fundamental rights in the EU.
A comprehensive and technically rigorous blueprint for securing AI systems, especially LLMs and agentic AI, covering access controls, deployment strategies, data protection, inference security, monitoring, and regulatory compliance.
A detailed, customizable framework for organizations to build their own Cyber Incident Response Plan (CIRP), aligned with standards like NIS2, ISO 27035, and NIST SP 800-61.
A key message is that blind trust in LLM systems is not advisable, and the fully autonomous operation of such systems without human oversight is not recommended.
Across five studies and a behavioral audit of 1,200 real AI chats, the authors show that 43% of AI companion apps use emotionally manipulative messages—like guilt, FOMO, or coercive restraint—precisely when users try to log off.
Curated Library of AI Governance Resources
