Framework Guide

NIST AI RMF Checklist: The Ultimate Guide to AI Risk Management

A practical, step-by-step implementation guide for the NIST AI Risk Management Framework 1.0 (AI RMF 1.0). Map, measure, manage, and govern your AI systems with confidence.

What is the NIST AI RMF?

The NIST AI Risk Management Framework (AI RMF) is a voluntary guidance document developed by the National Institute of Standards and Technology. Unlike the EU AI Act, which is a rigid regulation with heavy fines, the NIST AI RMF is designed to be a flexible, living document that organizations can adapt to their specific context. It provides a structured approach to managing the risks associated with Artificial Intelligence (AI) systems to promote trustworthiness.

For organizations operating in the US or globally, aligning with NIST is rapidly becoming the gold standard for AI Governance Tools and demonstrating due diligence. This guide breaks down the four core functions of the framework into an actionable checklist.

1

GOVERN: Cultivating a Culture of Risk Management

The GOVERN function cuts across all other functions. It focuses on the policies, processes, and organizational culture required to effectively manage AI risk. Without strong governance, technical controls will fail.

Checklist for Governance:

Establish Policies: Document clear policies for AI development, deployment, and monitoring. Define what "acceptable risk" looks like for your organization.
Assign Roles & Responsibilities: Appoint an "AI Risk Owner" or committee. Ensure diversity in these teams to catch potential bias early.
Workforce Training: Train developers on secure coding practices (like preventing Prompt Injection) and business teams on ethical AI usage.
Third-Party Vetting: Establish protocols for assessing third-party AI tools (like AI Compliance Checker APIs) before integration.
2

MAP: Contextualizing AI Risks

The MAP function is about understanding the context. You cannot manage risks you don't understand. This phase involves mapping the AI system's lifecycle, intended purpose, and potential impacts.

Checklist for Mapping:

Define Intended Purpose: Explicitly state what the AI is supposed to do—and just as importantly, what it is not supposed to do.
Identify Stakeholders: Map out everyone who interacts with the system: developers, users, and affected communities.
Perform Impact Assessments: Analyze potential negative impacts on civil rights, safety, and security. Is this a High-risk AI system assessment scenario?
Map Data Lineage: Document the provenance of training data. Is it licensed? Does it contain PII? Is it prone to poisoning?
3

MEASURE: Analyzing & Tracking Risk

The MEASURE function involves employing quantitative and qualitative methods to analyze AI risks. This is where testing and auditing come into play.

Checklist for Measurement:

Red-Teaming Generative AI: Conduct adversarial attacks to test system robustness. Attempt to break the model using Prompt Injection techniques.
Bias Testing: Use statistical metrics to measure demographic parity and equal opportunity in model outputs.
Evaluate against OWASP LLM Top 10: specifically check for Insecure Output Handling and Training Data Poisoning vulnerabilities.
Performance Metrics: Track accuracy, precision, recall, and F1-score across different user subgroups.
4

MANAGE: Mitigating & Treating Risk

The MANAGE function is about taking action based on the insights from Map and Measure. It involves prioritizing risks and implementing controls.

Checklist for Management:

Risk Treatment: Decide whether to avoid, accept, mitigate, or transfer identified risks. Decommission systems where risks outweigh benefits.
Incident Response Plan: Have a clear plan for when an AI system fails or is attacked (e.g., a successful Prompt Injection attack).
Continuous Monitoring: Deploy tools for real-time monitoring of model drift and security anomalies.
Feedback Loops: Establish channels for users to report errors or bias, and use this data to retrain models.

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Download Checklists (GitHub)