NIST AI RMF 101

The NIST AI Risk Management Framework (AI RMF) was developed to help organizations designing, developing, deploying, or using AI systems better identify and manage the risks associated with the technology. NIST AI RMF is a voluntary and high-level framework that is adaptable across many industries and types of AI systems.

In this article, we will break down the key elements of the framework, implementation steps organizations can take, and identify resources available to help execute those steps.

NIST AI RMF Core

The NIST AI RMF Core consists of four functions: Govern, Map, Measure, and Manage. Each function is comprised of categories and subcategories with specific actions and outcomes.

Govern

The Govern function focuses on a culture of AI risk management throughout an organization by outlining processes to identify and account for the risks an AI system can pose. The Govern categories are:

• Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively.
• Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained for mapping, measuring, and managing AI risks.
• Workforce diversity, equity, inclusion, and accessibility processes are prioritized in the mapping, measuring, and managing of AI risks throughout the lifecycle.
• Organizational teams are committed to a culture that considers and communicates AI risk.
• Processes are in place for robust engagement with relevant AI actors.
• Policies and procedures are in place to address AI risks and benefits arising from third-party software and data, and other supply chain issues.

Map

The Map function focuses on risk management in every phase of an AI system’s lifecycle and assessing potential risks to all stakeholders, including end-users. The Map categories are:

• Context is established and understood.
• Categorization of the AI system is performed.
• AI capabilities, targeted usage, goals, and expected benefits and costs compared with appropriate benchmarks are understood.
• Risks and benefits are mapped for all components of the AI system, including third-party software and data.
• Impacts to individuals, groups, communities, organizations, and society are characterized.

Measure

The Measure function focuses on developing quantitative and qualitative methods to assess AI risks and impacts. The Measure categories are:

• Appropriate methods and metrics are identified and applied.
• AI systems are evaluated for trustworthy characteristics.
• Mechanisms for tracking identified AI risks over time are in place.
• Feedback about the efficacy of measurement is gathered and assessed.

Manage

The Manage function focuses on dedicating resources to mitigate the identified risks and developing incident response plans. The Manage categories are:

• AI risks based on assessments and other analytical output from the Map and Measure functions are prioritized, responded to, and managed.
• Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by input from relevant AI actors.
• AI risks and benefits from third-party entities are managed.
• Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored regularly.

Other Elements in the Framework

Framing Risk

The Framing Risk section of the framework focuses on helping organizations understand and address the potential risks, impacts, and harms of AI systems. It also includes information about quantifying risk, risk tolerance, and risk prioritization.

Audience

The Audience section focuses on identifying the different actors who will interact with an AI system throughout its lifecycle, including end-users.

AI Risks and Trustworthiness

The AI Risks and Trustworthiness section articulates the characteristics of trustworthy AI and offers guidance for how they can be achieved.

Effectiveness of the AI RMF

The Effectiveness section enables organizations to assess whether implementing the AI RMF has enhanced their capacity to manage AI risks.

Getting Started with NIST AI RMF

Here are some steps to take for organizations that want to implement NIST AI RMF standards:

1. Establish AI Governance Roles & Responsibilities
Form an AI risk oversight group that includes technical, legal, compliance, and business stakeholders. Assign clear ownership for each AI RMF Core function (Govern, Map, Measure, Manage) so accountability is built in from the start.

2. Inventory & Classify AI Systems
Identify all AI systems in development or production, including those embedded in vendor products. Classify them based on criticality, intended use, and potential for harm to prioritize which systems to assess first.

3. Define Risk Appetite & Principles
Document your organization’s tolerance for AI risk in areas such as fairness, privacy, safety, and robustness. This will guide trade-offs between innovation and mitigation measures throughout the lifecycle.

4. Map AI Systems & Stakeholders
Map each system’s purpose, data sources, user groups, and potential non-user stakeholders who could be impacted. Identify where harm could occur in data handling, model outputs, or misuse scenarios.

5. Define Risk Metrics
Choose metrics that align with your risk priorities, such as bias detection, robustness testing, explainability, and privacy vulnerabilities. Integrate these into development workflows so measurement happens continuously.

6. Implement Risk Controls & Mitigation Strategies
Deploy technical and procedural safeguards like bias mitigation techniques, human review processes, and incident response triggers. Match each control to a specific identified risk.

7. Monitor, Audit, & Update
Set up monitoring to detect data drift, unusual input patterns, or performance degradation. Conduct periodic audits and retrain or adjust models as conditions change or new risks emerge.

8. Document, Communicate, & Review
Maintain documentation such as model cards, decision logs, and risk assessment reports. Share key findings with leadership and relevant teams, and schedule regular AI RMF reviews to refine your process as AI systems and risks evolve.

Resources

NIST has released several resources to help organizations successfully implement the AI RMF framework.

• The NIST AI RMF Playbook provides suggested steps organizations can take to achieve the outcomes laid out in the framework’s Core categories.
• Use cases document AI RMF implementations in government, industry, and academia.
• Crosswalk documents are available to provide a mapping of concepts and terms between the AI RMF and other guidelines and frameworks.
• The Roadmap outlines NIST’s priorities for updating the framework to account for gaps in knowledge, practice, or guidance as AI technology advances and the adoption of AI systems becomes more widespread.

Comparing ISO 42001 and NIST AI RMF

NIST AI RMF and ISO 42001 were two of the first AI-focused frameworks to emerge as the use of AI became more common. While both standards are dedicated to identifying, assessing, and managing AI risks, there are some differences between them, including:

• ISO 42001 is a certifiable standard. NIST AI RMF, like other NIST standards, has no formal certification.
• ISO 42001 uses the traditional ISO clause-based structure, opposed to the four Core functions found in NIST AI RMF. Here is the NIST AI RMF/ISO 42001 Crosswalk document aligning the Core subcategories and ISO controls.
• ISO is an international body, meaning ISO 42001 could carry more weight with customers and partners overseas.

CompliancePoint has a team dedicated to helping organizations achieve their NIST goals. To learn more about our services, reach out to us at connect@compliancepoint.com.