AI Risk Management
AI risk management is the discipline of identifying, assessing, mitigating, and monitoring the harms an AI system can cause across its lifecycle.
What is AI Risk Management?
AI risk management borrows from enterprise risk management but adds AI-specific concerns: bias, hallucination, drift, opacity, automation bias, security against prompt injection, and unintended scale of harm. Frameworks include the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act risk classification, and sectoral guidance from EIOPA, EBA, and the FCA. A working programme has a register, risk owners, control mappings, and review cadence.
How does AI Risk Management apply to enterprise AI?
Enterprises deploying AI in customer-facing or decision-impacting workflows need a documented AI risk management programme. Insurance, banking, and healthcare buyers will not sign without one.
Related terms
- ISO 42001 - ISO/IEC 42001 is the international standard for AI management systems, specifying requirements for establishing, implementing, maintaining, and continually improving an AI governance programme.
- NIST AI RMF - The NIST AI Risk Management Framework is a voluntary US framework for managing risks of AI systems across the lifecycle, organised around the functions Govern, Map, Measure, and Manage.
- EU AI Act - The EU AI Act (Regulation (EU) 2024/1689) is the European Union's horizontal regulation for AI, classifying systems by risk and imposing obligations on providers, deployers, importers, and distributors.
- AI Audit Trail - An AI audit trail is the persistent, tamper-evident record of every input, output, tool call, model version, and decision an AI system has made, sufficient to reconstruct any past interaction.
External references
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