The Complete Guide to AI Risk Assessment

Understanding AI Risk

AI systems introduce new categories of risk that traditional risk frameworks may not adequately address. From model hallucinations to prompt injection attacks, understanding these risks is critical.

Risk Categories

• Model Risk: Accuracy, bias, and reliability issues.
• Security Risk: Vulnerabilities to attacks and unauthorized access.
• Data Risk: Privacy violations and data leakage.
• Operational Risk: System failures and incorrect decisions.
• Compliance Risk: Regulatory violations.

Assessment Methodology

A comprehensive AI risk assessment follows a structured approach: identify, analyze, evaluate, and remediate. This guide walks through each step with real-world examples.