As artificial intelligence becomes increasingly integrated into business operations, organizations face mounting pressure to implement robust AI governance. The National Institute of Standards and Technology (NIST) AI Risk Management Framework offers a comprehensive, voluntary standard that’s 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 Trump 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
The NIST AI RMF is built around four essential functions, categories that create a comprehensive risk management cycle:
- Map: Recognize context and identify risks related to that context
- Measure: Assess, analyze, and track identified risks
- Manage: Prioritize and act upon risks based on projected impact
- Govern: Cultivate and maintain a culture of risk management
These functions translate into 72 sub-categories, each of which outlines an outcome to achieve that function’s overall goal.
Trustworthy AI Principles
Central to the NIST framework are seven characteristics of trustworthy AI systems:
- Valid & Reliable: The foundational requirement for all other characteristics
- Safe: Systems operate without causing harm
- Secure & Resilient: Protected against threats and adaptable to changes
- Explainable & Interpretable: Decisions can be understood and explained
- Privacy-Enhanced: Protects individual privacy and data rights
- Fair – With Harmful Bias Managed: Prevents discriminatory outcomes
- Accountable & Transparent: Clear responsibility and openness about operations
According to NIST, these principles should be woven throughout your organization’s AI objectives and policies.
Discover how FairNow’s AI Governance Software streamlines NIST AI RMF alignment.
Learn how FairNow’s AI Governance Software centralizes risk management and streamlines NIST AI RMF compliance.
Learn how FairNow’s AI Governance Software centralizes risk management and streamlines NIST AI RMF compliance.
Getting Started: Company-Level Implementation
Other risk areas, such as data governance or cybersecurity, have developed universal principles and a limited number of “correct” options for implementing controls. AI governance is far newer, and AI systems vary widely in use case, data, end user, and other factors. That means AI governance requires a more flexible approach.
Because of that, the NIST AI RMF’s Govern function essentially lays out a set of activities that need to be set and standardized for the entire organization or company. Then, the organization can pursue the outcomes laid out in Map, Measure, and Manage in a consistent manner.
Begin by reviewing your existing controls and compare them with the NIST outcomes, so you can understand what you already have in place and identify gaps. This assessment should include reviewing parallel governance processes and documentation for security, data privacy, and other related areas. If it’s possible to add AI governance into existing workflows or processes rather than building new ones, so much the better.
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 versus unacceptable risk levels, and establish escalation procedures for different risk tiers. 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.
Followed By Application-Level Preparation
Because of the way that the choices about principles, values and processes impact how you govern AI risk for individual systems, we recommend starting at the company-level. But once those processes have begun and you’re ready to start implementation at the AI application- or system-level, four key activities can help get started, 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 in existing processes, especially 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 meet the needs of your own organization, your customers, and your partners.
3. Review Existing Feedback Mechanisms
Having a feedback mechanism from all end users, internal or 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 into existing workflows?
Why Start Now?
The voluntary nature of the NIST AI RMF might tempt organizations to delay implementation, but early adoption offers significant advantages:
- Competitive Differentiation: Demonstrating robust AI governance can be a market differentiator
- Risk Mitigation: Proactive risk management prevents costly incidents and regulatory issues
- Stakeholder Confidence: Customers, partners, and investors increasingly expect responsible AI practices
- Regulatory Readiness: As AI regulations evolve, having NIST-aligned practices positions you for compliance
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 managing risks effectively. 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.
Related Articles
Discover how FairNow’s AI Governance Software streamlines NIST AI RMF alignment.
Learn how FairNow’s AI Governance Software centralizes risk management and streamlines NIST AI RMF compliance.
Learn how FairNow’s AI Governance Software centralizes risk management and streamlines NIST AI RMF compliance.
FAQs On NIST AI Risk Management Framework (RMF)
We’ve already got a cybersecurity program aligned with the NIST CSF. Is the AI RMF still necessary, or mostly redundant?
There are many areas of overlap between the CSF and the AI RMF, but cybersecurity is complementary to AI risk management, not a replacement. Both frameworks have a Govern function and your documents for one, such as an AI policy or trainings, may overlap or simply be combined. But the day-to-day work of identifying, quantifying, and mitigating risks in AI systems will require new workflows not covered in the NIST CSF.
Is it ever too early to establish an AI governance framework?
No. In fact, building your governance framework as you begin adopting AI is far more effective than trying to bolt one on after dozens of models are already in use. You don’t need a massive, complex system on day one. Start by defining clear policies for a single use case. Establish who is accountable for the model’s performance and fairness. By integrating governance from the start, you create a culture of responsibility and build a scalable structure that grows with your AI initiatives, preventing costly and complex clean-up projects down the road.
What's the most critical first step to building a unified governance framework?
Your first and most important step is to establish clear ownership. Before you write a single policy, you must define who is accountable for what. This means creating a cross-functional committee with leaders from legal, IT, compliance, and the business units using the AI. Then, for each AI system, assign specific individuals who are responsible for its entire lifecycle, from data inputs to performance monitoring. Without clear lines of accountability, even the best-written policies will fail to be enforced.
