A structured set of guidelines and processes for systematically addressing AI risks across the system lifecycle, from design through retirement.
A comprehensive framework - often based on industry standards (e.g., NIST AI RMF) - that defines risk principles, roles, processes (assessment, mitigation, monitoring), and governance artifacts (policies, templates). It ensures consistent risk handling by embedding risk activities (impact assessments, audits) into project gateways and by measuring organizational risk maturity over time.
A global insurer adopts the ISO/IEC TR 24028-based AI Risk Management Framework: it establishes risk categories, assigns risk owners, mandates risk checkpoints at key milestones (design, pilot, production), and uses an enterprise dashboard to track risk metrics and framework adoption across all AI projects.
