An AI risk management framework addresses unique challenges of artificial intelligence including bias, explainability, robustness, data quality, model drift, and societal impacts. It integrates technical validation, ethical review, governance oversight, and continuous monitoring. Frameworks align with standards like NIST AI RMF or ISO 42001. A financial services firm develops an AI governance framework requiring fairness testing for lending models, explainability documentation for regulatory review, adversarial robustness testing, quarterly model performance monitoring, and ethics board approval before deploying any customer-facing AI applications.