AI risk taxonomies provide structured frameworks for identifying and managing artificial intelligence risks systematically. Common categories include model performance risks (accuracy, robustness), data risks (quality, bias, privacy), deployment risks (integration, monitoring), ethical risks (fairness, transparency), and legal risks (liability, IP, compliance). Taxonomies enable consistent risk assessment, ownership assignment, and control mapping. A healthcare AI company adopted a taxonomy with 68 distinct AI risk types organized into six domains, allowing systematic review during model development and creating clear accountability for each risk category.