Enterprise AI Risk Management: Identification, Assessment & Mitigation

AI risk management is a comprehensive process designed to identify, assess, treat, and continuously monitor risks associated with AI systems throughout their lifecycle, addressing model-level, data-level, and operational risks. This approach extends classical enterprise risk management principles, such as those outlined in ISO 31000, by incorporating AI-specific identification methods and scoring criteria, often operationalized through frameworks like NIST AI RMF. Unlike traditional IT risk management, AI systems introduce unique failure modes that can manifest without code changes, necessitating ongoing risk assessment rather than static evaluations. The process involves a tailored risk scoring model, Likelihood × Impact × Exploitability, to prioritize resource allocation and treatment, which can follow the paths of acceptance, mitigation, transfer, or avoidance. Continuous monitoring is essential, as AI systems can change behavior post-deployment, and tools like NeuralTrust's TrustGuard and TrustLens are employed to ensure effective operational risk management.

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