ISO 42001:2023

Build Trustworthy, Responsible, and Transparent AI

Artificial Intelligence is transforming industries — but with innovation comes responsibility. Organisations are now expected to demonstrate that their AI systems are safe, ethical, transparent, and well‑governed.

ISO 42001:2023 is the world’s first international standard for Artificial Intelligence Management Systems (AIMS). It provides a structured framework for managing AI risks, ensuring responsible development, and building trust with customers, regulators, and stakeholders.

If your organisation uses or develops AI, ISO 42001 helps you operate with confidence and accountability.

What is ISO 42001:2023?

ISO 42001:2023 defines the requirements for establishing, implementing, maintaining, and continually improving an AI Management System. It applies to any organisation that designs, develops, deploys, or uses AI systems — regardless of size or sector.

It aligns with global AI regulations and frameworks, including the EU AI Act, OECD AI Principles, and NIST AI Risk Management Framework.

  • Responsible and ethical AI

  • Transparency and explainability

  • Risk management and governance

  • Data quality and lifecycle controls

  • Human oversight and accountability

  • Monitoring, validation, and continual

Why ISO 42001:2023 matters

  • Build Trust and Credibility

  • Support Regulatory Compliance

  • Reduce AI‑Related Risks

  • Enable Responsible Innovation

  • Integrates with Existing Standards

Key Elements of ISO 42001:2023

  • Context of the Organisation – Understanding how AI impacts your organisation, stakeholders, and risk environment.

  • Leadership & Governance – Top management must define AI responsibilities, oversight mechanisms, and ethical.

  • AI Risk Management – A structured approach to identifying, assessing, and treating AI‑specific risks.

  • Operational Controls – Processes to ensure AI systems operate safely and consistently.

  • Change management – Documentation and traceability and human oversight mechanisms.

  • Performance Evaluation – Monitoring, auditing, and reviewing the AIMS to ensure effectiveness.

  • Continual Improvement – Ongoing refinement of AI processes, controls, and governance.

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