What is AI Risk Management?

AI risk management is the ongoing process of identifying, assessing, treating, and monitoring risks that arise from developing or using AI systems — including bias, inaccuracy, security, privacy, transparency, and safety risks. It is most often framed using the NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001, the AI management system standard.

AI introduces risks that traditional IT risk management does not fully capture: models can be biased, produce confidently wrong outputs, leak training data, or behave unpredictably as inputs drift. The NIST AI RMF organizes the response into four functions — Govern, Map, Measure, and Manage — while ISO/IEC 42001:2023 provides a certifiable AI management system (AIMS), and ISO/IEC 23894:2023 offers dedicated AI risk-management guidance.

For example, a company rolling out an internal generative-AI assistant would map where it is used, measure risks like data leakage and hallucination, and manage them with acceptable-use rules, human review, and vendor assessments — recording each decision in an AI risk register. That register becomes the backbone of evidence when a customer's security questionnaire or an auditor asks how you control AI.

Documented AI policies, a risk register, and assessment procedures let you stand up this program quickly instead of building from a blank page. Templates accelerate the documentation, but they do not by themselves make you compliant or certified — you still have to run the assessments, apply the controls, and demonstrate the program is operating, especially if you pursue ISO 42001 certification.

Related terms: AI Management System (AIMS) · High-Risk AI System · Algorithmic Impact Assessment · Risk Register

Frequently asked questions

What is the difference between the NIST AI RMF and ISO/IEC 42001?

The NIST AI RMF is a voluntary, non-certifiable framework (functions: Govern, Map, Measure, Manage) for managing AI risk. ISO/IEC 42001 is a certifiable management-system standard against which an organization can be audited and certified. Many organizations use the AI RMF for practice and pursue ISO 42001 for formal assurance.

Can I reuse my existing ISO 27001 risk process for AI?

Partly. The core method (identify, assess, treat, monitor) carries over, but AI adds risk types — bias, explainability, model drift, training-data provenance — that an information-security risk assessment usually does not cover. AI-specific frameworks and an AI risk register are designed to fill those gaps.

Do small companies need a formal AI risk management program?

Increasingly, yes — not always for certification, but because enterprise customers and regulators now ask how you govern AI. A lightweight documented program (policy, acceptable-use rules, risk register) is usually enough to answer those questions credibly.

Toolkits that cover AI Risk Management

Learn more in our AI Governance (EU AI Act & NIST AI RMF) guide, explore the editable policy templates, or browse the full compliance glossary.

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