⚡ Quick Summary
Published by OWASP, this Japanese-language toolkit is a cybersecurity and governance checklist for organisations adopting large language model (LLM) applications that automatically generate content. It describes generative AI as both an opportunity and a source of new attack and governance challenges, and frames trustworthy AI around reliability, resilience and responsibility. Its listed conditions include robustness, accountability, monitoring, transparency, explainability, safety, security, privacy, fairness and sustainability.
The document’s central approach is to incorporate LLM security and governance into established privacy, security, legal and organisational practices, while creating AI-specific measures where necessary. It sets out six adoption steps: establish resilience through threat modelling, revise existing policies, train staff, involve organisational leaders, revise third-party risk management, and develop an adoption strategy. The main deliverable is a detailed checklist covering adversarial risk, threat modelling, AI asset inventories, training, business cases, governance, legal issues, regulation, technical implementation, TEVV, model and risk cards, RAG, and AI red teaming. It also reproduces the OWASP Top 10 LLM application risks and directs readers to related OWASP and MITRE resources.
🧩 What’s Covered
The material proceeds from concepts and strategy to operational checklist questions and supporting resources.
- Scope and trustworthy AI: Defines AI, machine learning, generative AI, LLMs and models, then presents conditions for trustworthy AI. The conditions group reliability-related attributes such as accountability and explainability; resilience-related attributes such as safety, security and privacy; and responsible attributes including fairness, ethics, inclusion, sustainability and a clear purpose.
- Threats and foundational controls: Explains LLM-specific challenges, including the difficulty of separating control flow from data, non-deterministic outputs and hallucinations. It classifies threats arising from not using AI, using AI, attacks on AI models, harms produced by AI models, and AI laws and regulation. It calls for role-specific training and integration with current privacy, governance and security measures.
- Adoption strategy: Identifies shadow AI as use of unapproved online tools, insecure browser plug-ins, or third-party applications that acquire LLM capabilities outside approval processes. Six adoption steps cover threat modelling, policy revision, education, leadership engagement, third-party risk management and strategy development.
- Deployment approaches: Describes six types of implementation, from general consumer use and vendor APIs to licensing enterprise models, fine-tuning pre-trained or already fine-tuned models with organisational data, and developing a model internally. The comparison relates control, transparency, customisation, cost and employee training to the approach selected.
- Core implementation checklist: Supplies questions and actions for adversarial risks, trust boundaries, malicious inputs, internal misuse, intellectual-property protection and content filtering. It calls for an AI asset inventory, inclusion of AI components in an SBOM, recording data sources and sensitivity, penetration testing or red teaming, and an onboarding process for AI solutions.
- Governance, legal and regulatory issues: Recommends an AI RACI chart, documented risk ownership, data classification and use restrictions, AI policy, and an approved-tools list. Legal prompts cover terms, EULAs, output rights, copyright, liability, insurance, sensitive information and employment-related discrimination. A dedicated section highlights US and Canadian considerations and notes potential EU AI Act and GDPR relevance.
- Assurance and optimisation: Covers lifecycle TEVV, including continuous testing, evaluation, verification, validation, monitoring and reporting. It explains model cards and risk cards, describes RAG as retrieval from current knowledge sources, and recommends red teaming alongside other evaluation methods.
- Risk catalogue and references: Defines the OWASP Top 10 LLM application risks, including prompt injection, insecure output handling, training-data poisoning, model denial of service, supply-chain vulnerabilities, sensitive-information disclosure, insecure plug-ins, excessive agency, overreliance and model theft. It then lists OWASP, MITRE, vulnerability-repository and procurement resources.
💡 Why it matters?
The toolkit translates LLM security and governance into tasks that can be assigned to business, technical, security, privacy, legal and operations teams. It helps teams identify shadow AI, map trust boundaries, catalogue models and data, update incident procedures, and set limits on access and data use before or during deployment.
Its checklist also connects technical assurance to organisational accountability: RACI assignments, AI policies, model and risk documentation, lifecycle TEVV, supplier review and red-team testing all appear alongside input/output security and training-pipeline controls. The document explicitly positions these activities as extensions of existing cybersecurity, privacy and governance practices rather than a wholly separate discipline.
❓ What’s Missing
The document explicitly says it is not exhaustive and that organisations may need to extend assessments and practices for their use cases and jurisdictions. It offers high-level questions and recommended activities rather than completed templates, detailed scoring methods, control test procedures, or a prescriptive implementation sequence for every deployment type. Its regulation section is titled for the United States and Canada, although it also discusses prospective EU AI Act applicability and GDPR implications; it does not provide a comparably detailed survey of other legal systems. The resource list names many external projects and tools, but does not reproduce their underlying requirements. The text also introduces the deployment framework as five methods while presenting six types.
👥 Best For
Security and privacy leaders, AI governance owners, legal and compliance teams, and technical managers preparing an organisational LLM adoption programme. It is particularly suited to teams that need to extend existing security, asset-management, supplier-review, incident-response and training practices to cover LLM applications and generative-AI-enabled threats.
📄 Source Details
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 〜 is a 31-page Japanese PDF published by OWASP. The cover identifies version 1.1 and dates the Japanese edition 10 April 2024. It credits 18 checklist contributors and two Japanese-version contributors. The project is licensed under CC BY-SA 4.0: https://creativecommons.org/licenses/by-sa/4.0/deed.ja
