Risk Management
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The ongoing process of identifying, assessing, mitigating, and monitoring the risks an AI system creates, from design through deployment and beyond.
What is Risk Management?
Risk management for AI is the ongoing process of identifying, assessing, mitigating, and monitoring the risks an AI system creates, from design through deployment and beyond.
It treats AI risk not as something to eliminate, which is impossible, but as something to know about, reduce where the stakes demand it, and watch for the rest of the system’s life.
How Risk Management Works
The cycle has four steps. Identification maps what could go wrong at each stage of the lifecycle, from skewed training data to model drift in production. Assessment rates each risk by severity and likelihood, so a rare catastrophic failure and a frequent minor one both get the attention they deserve.
Mitigation reduces the worst risks: retraining on better data, adding a human reviewer, restricting the use case, or deciding not to deploy at all. Monitoring closes the loop, because a model that was safe at launch degrades as the world it models changes.
Two published frameworks give the practice a shared vocabulary. The NIST AI Risk Management Framework, released in January 2023, organizes the work into four functions: Govern, Map, Measure, and Manage. ISO/IEC 23894 adapts the general risk-management standard ISO 31000 to AI.
Neither is binding law, but regulators and customers increasingly expect organizations to follow one, and structured evaluations like an AI assessment supply the evidence the cycle runs on.
Example of Risk Management
A bank deploys a model to approve consumer loans, and its risk team runs the full cycle on it. Identification: the model could deny credit unfairly across demographic groups, drift as economic conditions shift, or be gamed by applicants who learn its patterns.
Assessment: discriminatory denial ranks highest, because it harms applicants and violates fair-lending law. Mitigation: the team compares approval rates across groups before launch, removes features that proxy for protected attributes, and routes borderline denials to a human underwriter.
Monitoring: a monthly report tracks live approval rates by group, and a drift alarm triggers re-assessment when the applicant population changes. A year later a recession shifts applicant profiles, the alarm fires, and the cycle starts again.
Related AI terms: AI Risk · AI Assessment · AI Audit · Impact Assessment · Mitigation
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About