AI-RMF-MAP-3: Categorize AI risks based on likelihood, severity, and reversibility
Vallum evidence mapping for NIST AI RMF AI-RMF-MAP-3 — categorize ai risks based on likelihood, severity, and reversibility. Documents evidence produced, gaps, and customer responsibilities.
Source Attribution
- Framework
- NIST AI RMF 1.0
- Control ID
- AI-RMF-MAP-3
- Source Organization
- NIST
- Official Source
- AI Risk Management Framework ↗
- Control Objective
- Categorize AI risks based on likelihood, severity, and reversibility
Evidence Vallum Produces
Partial evidence through operation receipts and evidence-package metadata relevant to this control's objectives
Evidence Vallum Does NOT Produce
Model training documentation, bias testing results, stakeholder impact assessments, or organizational governance records
Known Limitations
Vallum provides evidence relevant to this NIST AI RMF requirement but does not constitute complete control implementation. Full compliance requires complementary controls at infrastructure, organizational, and procedural levels. Evidence covers Vallum-managed operations only and does not extend to customer applications, third-party systems, or physical infrastructure.
Your Responsibilities
Establish AI governance structure, conduct organizational risk assessments, maintain AI system documentation, and implement stakeholder engagement processes
External Controls Required
Identity provider, infrastructure security controls, organizational policies and procedures, third-party assessments, and legal/regulatory counsel
Residual Risk
Evidence gaps exist between Vallum-managed operations and full control implementation. Time-of-check to time-of-use windows, infrastructure-level vulnerabilities, and organizational process failures remain outside Vallum's evidence boundary.
Relevant Titan Components
Need a detailed evidence mapping for your specific compliance program?
Request a Control Evidence Mapping