Governance & Compliance · Software component
Control Effectiveness Evaluator
Software componentGovernance & ComplianceSafety, Security & Governancearc:ControlEffectivenessEvaluator
A governance component that verifies whether implemented AI controls actually reduce their target risks, measuring coverage, design gaps and implementation gaps.
Responsibility. Measures whether deployed controls achieve their intended risk reduction.
Also known as: Control testing, Control assurance
Relationships
reads dependency
writes dependency
sends data to dynamic
evaluates assurance
Design guidance
- MUST verify that fairness mitigations actually reduce measured bias.
- SHOULD validate that monitoring systems detect actual problems and that incident procedures can be executed.
- SHOULD treat guardrails as one control whose effectiveness is measured, not as comprehensive risk management.
Classification
- Patterns
- Assess stage of staged NIST AI RMF implementation
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Transparency and accountability (NIST AI RMF: accountable and transparent)
- Risks mitigated
- Security theater: controls deployed without evidence they workMonitoring that fails to detect real problems
- Frameworks & regulations
- NIST AI RMF: MEASUREISO/IEC 42001 §9 Performance evaluation
Sources
- Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
- Ref9.07: "Risk Assessment and Management for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/07-Risk-Assessment-Management.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note