AI risk assessment systematically identifies and analyzes risks specific to AI systems, including algorithmic bias, performance failures, privacy violations, security vulnerabilities, and unintended consequences. The assessment considers data quality, model design, deployment context, human oversight, and evolving regulatory requirements. Risk profiles vary significantly based on AI use case criticality and decision autonomy. A healthcare provider deploying AI for cancer diagnosis must assess risks of false negatives causing delayed treatment, algorithmic bias affecting certain demographic groups, data privacy breaches, and liability implications, implementing rigorous validation and human review protocols.