The ISO/IEC AI Standards Map
ISO/IEC JTC 1/SC 42 has published 41 standards for artificial intelligence — spanning governance, risk management, data quality, and the AI life cycle. This map shows how they fit together, and which are mandatory, recommended, or reference standards for ISO/IEC 42001 certification. It is maintained by a certified ISO/IEC 42001 Lead Auditor and updated as the standards evolve.
ISO/IEC AI standards by category
Grouped the way SC 42 organizes them. Colour shows each standard's role in ISO/IEC 42001 certification.
ISO/IEC 42001
Required for 42001
Recommended
Referenced
Foundational Concepts & Architecture
ISO/IEC TR 24372:2021
Overview of computational approaches for AI systems.
ReferencedISO/IEC 22989:2022
AI concepts and terminology — the vocabulary baseline for an AIMS.
Required
ISO/IEC 23053:2022
Framework for AI systems using machine learning.
Required
ISO/IEC 5392:2024
Reference architecture of knowledge engineering.
RecommendedISO/IEC TR 17903:2024
Overview of ML computing devices.
ReferencedISO/IEC TS 42112:2026
Guidance on ML model training efficiency optimization.
ReferencedGovernance & Management
ISO/IEC 42001:2023
The certifiable AI management system (AIMS) standard — the anchor of the whole framework.
ISO/IEC 42001
ISO/IEC 38507:2022
Governance implications of the use of AI by organizations (for governing bodies).
Recommended
ISO/IEC 42005:2025
AI system impact assessment.
Required
ISO/IEC 42006:2025
Requirements for bodies providing audit and certification of AIMS.
Recommended
Ethics & Trustworthiness
ISO/IEC TR 24028:2020
Overview of trustworthiness in AI.
ReferencedISO/IEC TS 8200:2024
Controllability of automated AI systems.
RecommendedISO/IEC TR 24027:2021
Bias in AI systems and AI-aided decision making.
RecommendedISO/IEC TS 12791:2024
Treatment of unwanted bias in classification and regression ML tasks.
RecommendedISO/IEC TR 24368:2022
Overview of ethical and societal concerns.
RecommendedISO/IEC TR 5469:2024
Functional safety and AI systems.
ReferencedISO/IEC TS 6254:2025
Objectives and approaches for explainability and interpretability of ML models and AI systems.
RecommendedISO/IEC 12792:2025
Transparency taxonomy of AI systems.
RecommendedISO/IEC TR 20226:2025
Environmental sustainability aspects of AI systems.
ReferencedISO/IEC TR 21221:2025
Beneficial AI systems.
ReferencedRisk Management, Security & Privacy
ISO/IEC 23894:2023
Guidance on AI risk management.
Required
ISO/IEC 27001:2022
Information security management systems — requirements.
RecommendedISO/IEC 27701:2025
Privacy information management systems — requirements and guidance.
RecommendedISO/IEC FDIS 27090
Guidance for addressing security threats and compromises to AI systems.
ReferencedISO/IEC DIS 27091
AI privacy protection.
ReferencedISO/IEC 20547-4:2020
Big data reference architecture — Part 4: Security and privacy.
ReferencedData & Data-Quality Management
ISO/IEC 5259-1:2024
Data quality for analytics and ML — Part 1: Overview, terminology, examples.
RecommendedISO/IEC 5259-2:2024
Part 2: Data quality measures.
RecommendedISO/IEC 5259-3:2024
Part 3: Data quality management requirements and guidelines.
RecommendedISO/IEC 5259-4:2024
Part 4: Data quality process framework.
RecommendedISO/IEC 5259-5:2025
Part 5: Data quality governance framework.
RecommendedISO/IEC TR 5259-6:2026
Part 6: Visualization framework for data quality.
RecommendedISO/IEC 20546:2019
Big data — overview and vocabulary.
ReferencedISO/IEC TR 20547-1:2020
Big data reference architecture — Part 1: Framework and application process.
ReferencedISO/IEC TR 20547-2:2018
Part 2: Use cases and derived requirements.
ReferencedISO/IEC 20547-3:2020
Part 3: Reference architecture.
ReferencedISO/IEC TR 20547-5:2018
Part 5: Standards roadmap.
ReferencedISO/IEC 24668:2022
Process management framework for big data analytics.
ReferencedEvaluation & Quality Assurance
ISO/IEC TR 24029-1:2021
Assessment of robustness of neural networks — Part 1: Overview.
RecommendedISO/IEC 24029-2:2023
Part 2: Methodology for the use of formal methods.
ReferencedISO/IEC TS 4213:2022
Assessment of ML classification performance.
RecommendedISO/IEC TS 42119-2:2025
Testing of AI — Part 2: Overview of testing AI systems.
RecommendedISO/IEC TS 25058:2024
SQuaRE — Guidance for quality evaluation of AI systems.
RecommendedISO/IEC 25059:2023
SQuaRE — Quality model for AI systems.
RecommendedLife Cycle
ISO/IEC 5338:2023
AI system life cycle processes.
Required
ISO/IEC 8183:2023
AI data life cycle framework.
RecommendedApplications & Use Cases
ISO/IEC TR 24030:2024
AI use cases.
ReferencedISO/IEC 5339:2024
Guidance for AI applications.
ReferencedWhere does your organization stand against these standards?
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