LLM AI サイバーセキュリティとガバナンスのチェックリスト 〜 失敗しない大規模言語モデル導入のために 〜
A Japanese-language OWASP checklist for governing and securing LLM applications. It sets out adoption steps, implementation questions, LLM risks, and references to OWASP and MITRE resources.
AI Governance Library: all resources
Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
Artificial Intelligence and Cybersecurity Research
This ENISA Research and Innovation Brief examines AI for cybersecurity and the security of AI systems. It maps methods, use cases, threats, research gaps and five proposed research needs.
CYBERSECURITY OF AI AND STANDARDISATION
This ENISA report maps standards and standardisation activity relevant to the cybersecurity of AI. It assesses coverage and gaps across the CIA model, AI trustworthiness and the draft AI Act.
AI Cybersecurity Challenges: Threat Landscape for Artificial Intelligence
This report maps the AI cybersecurity ecosystem through an AI lifecycle, asset taxonomy and threat taxonomy. It identifies 74 threats, their affected assets and potential impacts on security properties.
A multilayer framework for good cybersecurity practices for AI
This ENISA report presents a three-layer framework for securing AI systems through ICT cybersecurity foundations, AI-specific measures and sectoral practices. It also reports findings from a survey of EU national competent authorities.
AI governance around the world: Country profile: Singapore
This country profile describes Singapore’s approach to AI regulation, voluntary governance tools, assurance initiatives and AI standardisation. It maps key policies, institutions and national and international standards activity using primary sources.
Securing Machine Learning Algorithms
This ENISA report maps machine-learning algorithms, threats, vulnerabilities and security controls across the ML lifecycle. It provides operational implementation examples drawing on ISO 27001/2, NIST 800-53 and ML-security literature.
Technical Implementation Guidance: On Commission Implementing Regulation (EU) 2024/2690 of 17 October 2024
ENISA provides non-binding technical implementation guidance for cybersecurity risk-management requirements under Commission Implementing Regulation (EU) 2024/2690. It offers implementation advice, indicative evidence and mappings to standards and national frameworks.
Guide to Evaluation Perspectives on AI Safety
This guide presents evaluation perspectives, example risks and example evaluation items for AI safety in LLM systems. It also covers evaluation roles, lifecycle timing, technical and managerial methods, and considerations for multimodal systems.
International AI Safety Report 2026
This international scientific assessment synthesises evidence on general-purpose AI capabilities, emerging risks, and risk-management approaches. It focuses on frontier risks from misuse, malfunctions, and systemic effects.
Report on the Specific Influence on AI Safety
An 81-page report examining the socio-technical influences of generative AI through literature research and interviews. It organises 12 topics across ethics and law, economic activities, information space and environment, and outlines possible responses.
Artificial Intelligence Index Report 2026
An annual report that compiles global indicators on AI research, technical performance, responsible AI, economic and social effects, and governance. It provides data, charts, and chapter-level findings intended to inform decisions about AI.
Model Artificial Intelligence Governance Framework Second Edition
A voluntary framework for organisations deploying AI at scale. It sets out governance, human-oversight, operational and stakeholder-communication measures for responsible AI use.
Policy and Governance
This chapter of the AI Index Report 2026 examines global AI policy developments, national strategies, sovereignty, legislation, US regulation, and public investment. It uses comparative datasets and a timeline of developments through 2025.
Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations
A practical self-assessment guide for organizations deploying AI, developed to complement Singapore’s voluntary Model AI Governance Framework. It provides questions, implementation considerations and industry examples across governance, oversight, operations and stakeholder communication.
Four Principles of Explainable Artificial Intelligence
This NIST report defines four principles for explainable AI systems: Explanation, Meaningful, Explanation Accuracy, and Knowledge Limits. It reviews explanation purposes and styles, risks, algorithm categories, evaluation approaches, and human explanations as a comparison group.
The UK AI Security Institute’s Research Agenda
This research agenda sets out the AI Security Institute’s current priorities for understanding, evaluating and mitigating risks from advanced AI. It describes risk domains, evaluation methods, safeguards, control and alignment research directions.
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence
An NIST Special Publication that frames AI bias as a socio-technical risk. It defines systemic, statistical and computational, and human bias, and offers preliminary guidance on datasets, TEVV, human factors, and governance.
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
A voluntary, use-case-agnostic framework for organisations managing risks from the design, development, deployment and use of AI systems. It sets out trustworthiness characteristics and four functions—GOVERN, MAP, MEASURE and MANAGE—for continuous AI risk management.
AI Security Institute Frontier AI Trends Report
This report synthesises AI Security Institute evaluations of frontier AI systems across autonomy, cyber, chemistry and biology, safeguards, loss of control, societal impacts and open-source models. It presents trends, methods and limitations from its testing.
Assuring a responsible future for AI: Accelerating the growth of the UK’s AI assurance market
This report surveys the UK AI assurance market, its barriers to growth and its future potential. It sets out government actions to increase demand, strengthen third-party assurance supply and improve interoperability.
AI and cyber security: what you need to know
This NCSC guidance explains the benefits and cyber security risks of AI tools for non-technical managers, board members and senior executives. It outlines AI-specific vulnerabilities and provides questions for organisational discussions about AI/ML security.
The Model for Responsible Innovation: A practical tool for trustworthy adoption of AI in the public sector
A practical framework for public-sector teams to identify and mitigate ethical risks in data and AI projects. It sets out eight Fundamentals, six Conditions and an ethical red-teaming workshop process.
AI Governance around the world: Country Profile: United Kingdom
Country profile surveying the UK’s AI regulatory approach, policy initiatives, sectoral oversight and standardisation system. It maps voluntary principles, assurance activity, AI safety efforts, and engagement with international standards.
