ISO/IEC 42001 — The International Standard for AI Management Systems

What is AIMS — AI Management System

Analogous to an ISMS (information security) or QMS (quality), an AIMS is a management system — processes, role owners, documents, and controls whose purpose is to ensure the organization uses AI responsibly. It is not a questionnaire or a software tool; it is a governance framework that integrates with how the organization operates day to day.

An AIMS answers questions such as: who approves the procurement of a new AI tool? What is the process when a system produces an erroneous output? Who is the designated role owner responsible for transparency in AI-assisted decisions? What data is permitted to be sent to an external AI service? How does the organization document that an AI vendor processes personal data?

Standard Structure — 10 Chapters, 4 Annexes

The standard follows the same High-Level Structure (HLS) familiar from ISO 27001 and ISO 9001:

• Chapters 4–10 (normative body) — Organizational context, leadership, planning, support, operations, performance evaluation, and improvement. These are the clauses examined in every certification audit.
• Annex A — A catalogue of 38 recommended controls organized across 9 categories. This is where the majority of practical implementation work takes place.
• Annex B — Implementation guidance for every control in Annex A.
• Annexes C and D — Guidance on addressing specific AI risks and sector-specific considerations.

Key Controls Every Organization Must Address

AI Asset Management (Annex A.6)

Map every AI system in use: name, vendor, intended purpose, user population, data inputs, data outputs, and risk level. Without this inventory there is no realistic path to compliance — and this exercise is typically where organizations discover they have two to three times more AI tools in active use than they believed (shadow AI).

Supplier Management (Annex A.10)

AI-adapted data processing agreements, verification of data usage rights for inputs the organization provides, and change tracking when a vendor updates its underlying model. A supplier that changes model behaviour without notice — potentially destabilizing output consistency — must be covered by a formal change management process.

Human Oversight and Incident Reporting (Annex A.7, A.8)

Every AI system that influences a decision affecting a person requires a defined human oversight pathway. The organization must specify what constitutes an AI incident (erroneous output, detected bias, data exposure), how it is reported, who investigates, and how learning is captured.

Transparency and Explainability (Annex A.7.4, A.9)

When an AI system makes or influences a decision that affects an individual — CV screening, credit approval, service prioritization — the organization must be able to explain that decision, at minimum to the level of which features or inputs drove the outcome.

Readiness Roadmap in Four Steps

Regulatory Intersection

An organization implementing ISO 42001 already covers a significant portion of requirements from other frameworks:

• EU AI Act — Risk management, technical documentation, incident monitoring, and transparency obligations in Articles 9–15 map directly onto the AIMS controls. See our EU AI Act compliance guide.
• NIST AI RMF — The US AI Risk Management Framework shares substantial structural alignment with ISO 42001. Organizations serving US markets gain dual coverage from a single implementation effort.
• ISO 27001 — Organizations with an existing ISMS save an estimated 40–50% of implementation effort, since supplier management, asset documentation, and incident management clauses overlap directly. See our ISO 27001 ↔ ISO 42001 mapping.

The Alice GRC portal marks every identified gap with the standard or regulation it touches, so a single compliance effort advances readiness across multiple frameworks simultaneously.