AI Risk Management

AI risk management is a structured process for identifying what could go wrong with an AI system, assessing how likely and how serious those failures would be, putting controls in place to reduce them, and monitoring whether those controls are working. It covers a wide range of failure types, including inaccurate outputs, biased decisions, privacy violations, security vulnerabilities, regulatory gaps, and the erosion of human accountability when automation takes over decisions that should have human review. The discipline borrows from established enterprise risk management practice but adapts it to the specific characteristics of AI: systems that change over time, make probabilistic judgments, and can fail in ways that are harder to detect than a system outage.

AI systems can cause harm quite subtly. A model producing biased hiring recommendations, a fraud detection system flagging legitimate customers, an automated pricing tool creating compliance exposure. These failures often accumulate before anyone notices, and by then the harm is already done. Without a risk management process, the organization has no consistent basis for deciding which AI uses need scrutiny and which don't, no named owner when something goes wrong, and no evidence of due diligence when a regulator or board asks what controls were in place. The cost of that gap tends to arrive at the worst possible moment.