AI Risk Management

AI systems introduce a category of risk that differs fundamentally from the operational risks of conventional software. Traditional software fails in deterministic, diagnosable ways: a function returns the wrong value, a service becomes unavailable, a transaction rolls back. AI systems fail probabilistically and often invisibly — a model's accuracy degrades gradually as data distributions shift, outputs become systematically biased against a protected class without triggering any alert, or a model confidently generates plausible but incorrect information that propagates through downstream business decisions. These failure modes require risk management frameworks specifically designed for AI's stochastic and adaptive nature.

Enterprise AI risk taxonomies typically span several categories. Technical risks include model drift, hallucination, adversarial vulnerability, and infrastructure failure. Data risks cover training data poisoning, personally identifiable information leakage, and consent violations. Operational risks include over-reliance on AI outputs by human operators, lack of fallback procedures when models are unavailable, and inadequate documentation for model behavior. Ethical and compliance risks encompass discriminatory outcomes, unexplainable decisions in regulated contexts, and violations of sector-specific AI regulations such as the EU AI Act. Each category requires distinct assessment methods, mitigation controls, and monitoring mechanisms.

Integrating AI risk management into the existing enterprise risk framework — typically owned by a Chief Risk Officer or equivalent function — is more effective than creating a siloed AI risk process. This integration ensures that AI risks are evaluated against the same materiality thresholds, reported through the same governance structures, and resourced through the same risk budget processes as other enterprise risks. It also ensures that the organization's risk appetite — its tolerance for the possibility of AI-related harm — is set by appropriate executives rather than by AI development teams with inherent conflicts of interest.