AI risk management is the continuous process of identifying, assessing, treating, monitoring, and communicating risks created by an AI system and its use. It covers technical failures, human misuse, legal duties, security, operational impact, and affected people across design, procurement, deployment, and retirement.
Teams establish context, identify harms and failure modes, assess likelihood and impact, then choose controls and owners. AI governance makes those decisions repeatable, while responsible AI provides the broader operating principle. The NIST AI Risk Management Framework gives the loop its standard shape: govern, map, measure, manage. Risk management continues after deployment.
When does AI risk management matter?
Prioritize risks tied to business use, not abstract model capability. AI risk classification can determine regulatory duties, and AI assurance tests whether controls support the claims. Financial loss from it can be transferred, which is what AI liability insurance is for, though the accountability cannot. Residual risk needs a named owner who accepts it.
This entry was drafted with AI assistance.
Frequently asked questions
What is AI risk management used for?
Prioritize risks tied to business use, not abstract model capability. AI risk classification can determine regulatory duties, and AI assurance tests whether controls support the claims.
Is AI risk management only for high-risk AI?
No. The depth should match the impact, but every production system needs proportionate ownership and monitoring.
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