How do you implement AI risk management using the NIST AI RMF?
The NIST AI Risk Management Framework provides a structured approach to identifying, assessing, and mitigating AI risks. Core functions: (1) Govern: establish organizational policies, roles, and processes for AI risk management. Define risk tolerance, create oversight committees, and integrate AI risk into enterprise risk management. (2) Map: contextualize AI systems within their operational environment. Identify intended uses and users, assess potential impacts (positive and negative), understand limitations, and categorize risk level. (3) Measure: assess risks using quantitative and qualitative methods. Evaluate model performance, fairness, security, privacy, transparency, and robustness through testing and monitoring. (4) Manage: implement risk treatments (mitigate, accept, transfer, or avoid), monitor residual risks, and communicate risk status to stakeholders. Trustworthy AI characteristics: valid and reliable (performs consistently as intended), safe (does not endanger life, health, property, or environment), secure and resilient (withstands adversarial conditions), accountable and transparent (responsible parties identified, decisions explainable), explainable and interpretable (mechanisms understood, outputs meaningful), privacy-enhanced (data collection and use respect privacy), and fair with managed bias. Implementation: create an AI inventory (catalog all AI systems in use), risk-classify each system (critical, high, medium, low), conduct risk assessments proportional to risk level, implement controls and monitoring, and review regularly. Integration with existing frameworks: NIST AI RMF complements ISO 42001 (AI management system standard), NIST Cybersecurity Framework (for security-specific controls), and sector-specific regulations (healthcare, finance). Documentation: maintain risk assessment records, control implementation evidence, monitoring results, and incident reports. Continuous improvement: review and update risk assessments when AI systems change, when new threats emerge, or when regulations evolve.