In Depth
AI risk management applies structured risk management principles to the unique challenges of AI systems. The NIST AI Risk Management Framework (AI RMF), released in 2023, is the most widely referenced standard. It organizes AI risk management into four functions: Govern (establishing context and culture), Map (understanding and framing risks), Measure (analyzing and assessing risks), and Manage (prioritizing and addressing risks).
AI systems present distinct risk categories: performance risks (inaccurate predictions, failure modes), fairness risks (discriminatory outcomes), security risks (adversarial attacks, data poisoning), privacy risks (data exposure, inference attacks), safety risks (physical harm from autonomous systems), and societal risks (misinformation, job displacement). Each category requires specific identification, measurement, and mitigation approaches.
For organizations deploying AI, a structured risk management program is essential. This includes risk assessments before deployment, continuous monitoring of deployed systems, incident response procedures, and regular reviews of risk tolerance. Organizations in regulated industries (financial services, healthcare, government) often face explicit requirements for AI risk management. The NIST AI RMF, ISO 42001, and Singapore's Model AI Governance Framework provide practical implementation guidance.