Mapping NIST AI RMF to Controls and Evidence
Explore how to translate the NIST AI Risk Management Framework into actionable controls and evidence within AI system architecture. Understand the mapping process from governance requirements to specific enforcement points and evidence types, ensuring auditability and risk management. This lesson helps you design measurable, testable controls that align with regulatory frameworks and operational needs, preparing you to bridge governance and engineering effectively.
Risk classification determines the required level of oversight. It doesn’t tell you how to map that classification to a governance framework’s terminology and requirements when a regulator or auditor expects the risk to be expressed in framework-specific terms. The NIST AI Risk Management Framework (AI RMF) functions provide a common vocabulary for risk governance. They do not constitute compliance certification.
Architecturally, the RMF provides a translation layer between governance requirements and engineering controls. A framework statement typically defines an outcome, such as identifying risks or monitoring system performance. The architecture package must identify where each control is enforced and the evidence that demonstrates the control ...