In this guide
- What is AI risk management?Definition, the risk lifecycle and how it maps to the EU AI Act.
Key facts
| Definition | Identifying, assessing, treating and monitoring the risks AI systems create across their lifecycle |
|---|---|
| EU AI Act link | A continuous risk-management system is mandatory for high-risk AI under Article 9 |
| Reference frameworks | ISO/IEC 42001, ISO/IEC 23894 and the NIST AI Risk Management Framework |
| Risk types | Safety, fundamental rights, bias, security, privacy, performance and reliability |
| Core artefact | An AI risk register linked to your AI inventory |
| Cadence | Continuous — risks are re-assessed as models, data and uses change |
Why AI needs its own risk management
AI behaves differently from ordinary software: it learns from data, can be opaque, and its outputs can shift as data and use change. That introduces risks — bias, inaccuracy, security weaknesses, privacy harms and effects on people's rights — that general IT risk processes were not designed to catch. AI risk management adapts familiar risk practice to those specifics. See what is AI risk management?
The AI risk management lifecycle
The process is a continuous loop: identify the risks each system poses; assess their likelihood and impact; treat them by avoiding, reducing, transferring or accepting; and monitor the residual risk over time. Because models, data and uses change, it never finishes — you re-assess on a set cadence and whenever something material changes.
What the EU AI Act requires (Article 9)
For high-risk AI, the EU AI Act makes a risk-management system mandatory under Article 9: a continuous, documented process running across the whole lifecycle that identifies and evaluates risks to health, safety and fundamental rights, adopts mitigations, and tests that they work. It sits alongside data governance, technical documentation, human oversight and logging.
Frameworks: ISO 42001, ISO 23894 and NIST AI RMF
You do not have to invent the method. ISO/IEC 42001 embeds risk management in a certifiable management system; ISO/IEC 23894 gives AI-specific risk guidance built on ISO 31000; and the NIST AI Risk Management Framework offers a widely used, voluntary structure (Govern, Map, Measure, Manage). Pick one as your spine and stay consistent.
How to build an AI risk register
Start from your AI inventory. For each system, record the risks, an owner, the likelihood and impact, the mitigations, and the residual risk after controls. Keep it live and reviewed. The register is what turns risk assessment into action and gives you the evidence buyers and auditors ask for.
Getting started proportionately
Scale effort to risk. Map your systems, prioritise the high-risk and customer-facing ones, run a first assessment, and stand up a register with named owners. Put deeper, documented controls around the systems the law treats as high-risk, and a lighter touch elsewhere. You can check your exposure in minutes.
Frequently asked questions
What is AI risk management?
The ongoing process of identifying, assessing, treating and monitoring the risks an organisation's AI systems create for people, the business and compliance.
Does the EU AI Act require AI risk management?
Yes — Article 9 requires a continuous, documented risk-management system for high-risk AI systems across their lifecycle.
What frameworks help with AI risk management?
ISO/IEC 42001, ISO/IEC 23894 and the NIST AI Risk Management Framework all provide structured, widely used approaches.
What is an AI risk register?
A live record of each AI system's risks, owners, likelihood, impact, mitigations and residual risk — the practical core of AI risk management.
Where do you start with AI risk management?
Build an AI inventory, then run a first risk assessment on your highest-risk and customer-facing systems.
Related guides
Sources
Last updated 19 June 2026.