What is the NIST AI Risk Management Framework?

As artificial intelligence becomes increasingly integrated into business operations, organizations face mounting pressure to implement robust governance. The National Institute of Standards and Technology (NIST) AI Risk Management Framework is quickly becoming the gold standard for AI risk management. Here’s everything you need to know about getting started.

What is the NIST AI Risk Management Framework?

The NIST AI Risk Management Framework (AI RMF) is a voluntary standard published in January 2023 by NIST, an agency of the U.S. Department of Commerce dedicated to promoting innovation and developing standards across the economy. The framework provides organizations with a structured approach to building and improving their AI risk management programs.NIST provides three key documents to support implementation:

• NIST AI RMF Framework: Defines the outcomes organizations should seek from their risk management programs
• NIST AI RMF Playbook: Offers specific suggestions for actions, documentation, and references to achieve framework outcomes
• NIST AI 600-1 Generative Artificial Intelligence Profile: Provides guidance for adapting the framework to address the unique risks posed by generative AI systems

The current administration’s recent AI Action Plan has further emphasized NIST’s role in supporting AI evaluation methods and building an ecosystem to advance responsible AI development.

The four core functions and seven trustworthy AI principles

The NIST AI RMF is built around four essential tasks that create a comprehensive risk management cycle. These functions translate into 72 sub-categories, each of which outlines an outcome to achieve that function’s overall goal:

1. Map: Recognize context and identify risks related to that context
2. Measure: Assess, analyze, and track identified risks
3. Manage: Prioritize and act upon risks based on projected impact
4. Govern: Cultivate and maintain a culture of risk management

Also central to the NIST framework are seven characteristics of trustworthy AI systems. Weave these principles throughout your organization’s AI objectives and policies:

• Valid and reliable: The foundational requirement for all other characteristics.
• Safe: Systems operate without causing harm.
• Secure and resilient: Protected against threats and adaptable to changes.
• Explainable and interpretable: Decisions can be understood and explained.
• Privacy-enhanced: Protects individual privacy and data rights.
• Fair, with harmful bias managed: Prevents discriminatory outcomes.
• Accountable and transparent: Clear responsibility and openness about operations.

Getting started: Company-level implementation

Other risk areas, such as data governance or cybersecurity, have universal principles and a limited number of “correct” options for implementing controls. AI governance is relatively new, and AI systems vary widely across use cases, data, end users, and other factors. That means AI governance requires a more flexible approach.

As a result, the NIST AI RMF’s Govern function essentially outlines a set of activities to establish and standardize across the entire organization. Then, the organization can consistently pursue the outcomes outlined in Map, Measure, and Manage.

Begin by reviewing your existing controls and comparing them with the NIST outcomes to understand what you already have in place and identify gaps. This assessment should include reviewing parallel governance processes and documentation related to security, data privacy, and other areas. If possible, it is even better to add AI governance to existing workflows or processes rather than building new ones.

Then, you’ll want to begin building the foundations of your AI governance program, starting with five critical elements:

1. Set out stakeholders, roles, and governance structures: Identify everyone involved in AI development and oversight, both at the company level and for individual applications. Define clear roles, responsibilities, decision-making authority, and escalation paths.
2. Define responsible AI objectives: Set out what your overall, measurable objectives are to govern the development and use of AI systems across the organization. Align your objectives with NIST’s Trustworthy AI Principles.
3. Build an AI policy: Work with stakeholders to create comprehensive guidance on how your organization develops, acquires, deploys, and governs AI. Ensure this policy reflects your business strategy, values, risk appetite, and legal obligations, while aligning with existing security and data privacy policies.
4. Establish an organizational risk assessment process: Create a systematic approach to define and categorize AI risks. Set clear criteria for acceptable and unacceptable risk levels, and establish escalation procedures for each risk tier. The risk assessment process must be repeatable but can evolve as needs change.
5. Set out your responsible AI development and use process: Once other governance pieces are in place, develop a repeatable process covering the entire AI lifecycle: design, build, test, deploy, and monitor. Include specific checkpoints, role definitions, minimum requirements, and escalation paths.

Application-level preparation

Once you’ve established company-level processes and you’re ready to start implementation at the AI application or system-level, four key activities can help, especially as you’re refining the responsible AI development and use process above.

1. Review existing processes: Examine how you currently handle AI-related questions across vendor evaluation and procurement. Find out how you’re already asking about AI in vendor software, or related concerns such as data ownership, security, and governance.
2. Understand stakeholder concerns: Identify what questions customers and partners are asking about your AI systems. Are they concerned about fairness, explainability, or other specific risks? This insight will help you create scalable documentation that meets the needs of your organization, customers, and partners.
3. Review existing feedback mechanisms: Having feedback mechanisms from all end users, internal and external, is a key part of NIST’s governance structure. Identify all current channels for feedback, as well as other individuals impacted by your products or systems. Current channels may include customer support and incident reporting systems. Consider how these can be adapted or expanded to cover AI-specific concerns.
4. Assess current testing and monitoring: Evaluate existing capabilities for cybersecurity, performance, and reliability testing. Document both the tools you use and who owns responsibility for this work. Could testing for AI-specific risks and concerns be easily added to existing workflows?

Moving forward

Implementing the NIST AI Risk Management Framework is not just about compliance — it’s about building sustainable, trustworthy AI capabilities that drive business value while effectively managing risks. Start with the company-level foundations, gradually expand to application-specific controls, and remember that this is an iterative process that will evolve with your AI capabilities and the broader regulatory landscape.

The framework’s voluntary nature makes it an ideal starting point for organizations serious about responsible AI development. By beginning now, you’ll not only protect your organization from AI-related risks but also position yourself as a leader in the rapidly evolving field of AI governance. Whether you’re just beginning your AI journey or looking to formalize existing practices, the NIST AI RMF provides a proven roadmap for building confidence in your AI systems while enabling innovation and growth.

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