The Framework
NIST AI Risk Management Framework
The NIST AI RMF is the most comprehensive, vendor-neutral framework for managing AI risk. Here's how we implement each of its four core functions.
NIST AI RMF Official DocumentationEstablish the foundation for AI governance
GOVERN establishes the organizational structures, policies, and accountability needed to manage AI risk at scale. Without governance, the other three functions have no teeth.
Policies, Processes, Procedures & Practices
Define organizational policies that set boundaries for acceptable AI use, mandate risk management processes, and establish escalation paths.
Accountability
Assign clear ownership for AI risk management — from the C-suite to individual model owners. Ambiguity in accountability is a governance failure.
Workforce & Diversity
Ensure teams building and deploying AI systems understand risks, biases, and governance requirements through training and diverse representation.
Organizational Teams
Cross-functional teams — legal, security, data science, risk — must coordinate AI governance. Siloed approaches fail.
Policies for Third-Party Risks
Vendor AI and foundation models carry inherited risk. Third-party AI governance policies define how to evaluate, onboard, and monitor external AI.
Policies for AI Risk Management
Risk tolerance, appetite, and treatment policies must be explicitly defined for AI — distinct from general enterprise risk policies.
Understand the AI systems you operate
MAP focuses on understanding the context in which AI systems operate — what they're used for, who is affected, and what failure looks like. Risk cannot be managed that hasn't been mapped.
Context Established
Define the intended purpose, operational context, and scope for each AI system. Understand the business goals and constraints it must operate within.
Categorization
Classify AI systems by risk level based on potential harms, affected populations, and reversibility of decisions. High-risk AI requires more rigorous treatment.
AI Risk & Benefits
For each AI use case, enumerate both the anticipated benefits and the potential negative impacts — including impacts on individuals, groups, and society.
Risk Tolerance
Align AI system risk profiles to organizational risk tolerance. Some use cases are acceptable with low-maturity governance; others require comprehensive controls before deployment.
Risks & Impacts
Document and communicate risk and impact assessments to relevant stakeholders — including business owners, affected communities, and oversight bodies.
Quantify and track AI risk
MEASURE gives organizations the tools and metrics to understand how AI systems are actually performing against their risk profiles — not just at deployment, but continuously.
AI Risk Analysis
Apply structured risk analysis methods to AI — including bias testing, adversarial robustness, and failure mode analysis.
AI Risk Assessment
Evaluate and prioritize identified risks using consistent scoring methods. Risk registers should track likelihood, impact, and current control effectiveness.
Internal Experts & Mechanisms
Use internal red teams, bias audits, and explainability tools to surface risks that automated testing won't catch.
Risk of Using External AI
Third-party models introduce risks that are difficult to directly measure. Establish monitoring, SLAs, and contractual requirements with vendors.
Risks from Dependent Systems
AI systems often depend on data pipelines, APIs, and other AI models. Dependencies must be mapped and their risk contributions measured.
Respond to and treat AI risk
MANAGE is where governance becomes operational — prioritizing risks, implementing controls, and maintaining continuous visibility into the AI risk posture of the organization.
Risk Treatment
For each identified risk, define a treatment: accept, mitigate, transfer, or avoid. Document the rationale and the owner responsible for execution.
Strategies for Risk Treatment
Implement the controls, safeguards, and operational changes required to reduce risk to acceptable levels. This includes technical controls and policy enforcement.
Risk & Benefits Over Deployment Lifecycle
AI risk is not static. Continuously monitor deployed systems for drift, changing use patterns, and emerging risks — and update risk treatment accordingly.
Risk Based on Impacts
Prioritize risk treatment based on the magnitude of potential harm. High-impact, high-likelihood risks demand immediate attention and robust controls.
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