AI Risk Management is the practice of systematically identifying and addressing the risks that artificial intelligence systems introduce to an organization. It covers technical risks (security vulnerabilities, model failures), operational risks (Shadow AI, unauthorized usage), compliance risks (regulatory violations, data protection), and strategic risks (vendor lock-in, reputational damage).
The NIST AI Risk Management Framework (AI RMF) provides a widely-adopted structure organized around four functions: Govern (establishing AI risk management policies and processes), Map (identifying and categorizing AI risks), Measure (analyzing and assessing identified risks), and Manage (implementing controls and monitoring effectiveness).
Key AI risk categories include: data privacy and protection risks, bias and fairness concerns, security vulnerabilities including adversarial attacks, intellectual property risks, regulatory compliance risks, operational reliability, supply chain risks from AI vendors, and ethical considerations.
Effective AI risk management requires cross-functional collaboration between security, legal, compliance, IT, and business teams, supported by both technical controls (monitoring, DLP, access management) and organizational controls (policies, training, governance committees).
