Overview
The NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0) is the foundational U.S. standard for managing risks associated with AI systems throughout their lifecycle. Published in January 2023, the framework provides a flexible, voluntary approach that organizations can adapt to their specific contexts, use cases, and risk tolerances.
The AI RMF has become the de facto standard for AI governance in the United States—cited by SEC, CFPB, federal agencies, and state regulators. Referenced by major consulting frameworks and corporate AI policies. Crosswalks available to EU AI Act, ISO 42001, and Singapore AI Verify.
Core Functions
The AI RMF organizes risk management into four interconnected functions:
GOVERN
Cultivate a culture of risk management. Establish policies, roles, accountability.
MAP
Understand context and identify AI system characteristics. Categorize by risk.
MEASURE
Analyze risks using quantitative and qualitative methods. Assess trustworthiness.
MANAGE
Address identified risks. Implement mitigation, monitoring, incident response.
Seven Characteristics of Trustworthy AI
| Characteristic | Description |
|---|---|
| Valid and Reliable | System performs as intended under expected conditions |
| Safe | Does not endanger human life, health, property, or environment |
| Secure and Resilient | Maintains confidentiality, integrity, and availability |
| Accountable and Transparent | Clear accountability and openness about operation |
| Explainable and Interpretable | Outputs can be understood by stakeholders |
| Privacy-Enhanced | Protects personal information appropriately |
| Fair with Bias Managed | Treats individuals and groups equitably |
Generative AI Profile (AI 600-1)
Released July 2024, the GenAI Profile addresses risks unique to large language models and generative systems:
| GAI Risk | Description |
|---|---|
| Confabulation | Generation of false or misleading information |
| Dangerous Content | CBRN, weapons, or harmful instruction generation |
| Data Privacy | Training data memorization and exposure |
| Information Integrity | Misinformation and manipulation |
| Information Security | Model extraction and adversarial attacks |
| Intellectual Property | Copyright and licensing concerns |
| Value Chain | Third-party model and component risks |
Industry-Specific Considerations
Financial Services
- Integration with SR 11-7 model risk management
- Fair lending and ECOA compliance
- Explainability for adverse action notices
Insurance
- Colorado SB 21-169 alignment
- NAIC Model Bulletin compliance
- Actuarial standards (ASOP 56)
Utilities
- Critical infrastructure protection
- NERC CIP integration
- Grid reliability considerations