NIST AI Risk Management Framework

Overview

The National Institute of Standards and Technology AI Risk Management Framework provides a voluntary, consensus-driven framework for organizations designing, developing, deploying, and using artificial intelligence systems. Published in January 2023, the NIST AI RMF represents a coordinated effort to establish standardized approaches to AI risk management that can be adopted across sectors and organizational contexts.

The Framework is structured around four core functions: Govern, Map, Measure, and Manage. These functions are designed to be implemented concurrently and continuously throughout the AI system lifecycle. The Framework does not prescribe specific technical solutions or regulatory requirements, instead offering flexible guidance that organizations can tailor to their unique risk profiles, operational contexts, and applicable legal obligations.

The NIST AI RMF is increasingly referenced in regulatory guidance, procurement requirements, and industry standards. Federal agencies are directed to consider the Framework when developing AI governance structures. Private sector organizations adopt the Framework to demonstrate mature risk management practices to stakeholders, customers, and regulators. Understanding and implementing the Framework positions organizations to meet evolving compliance expectations and operational best practices.

Core Functions

Govern

The Govern function establishes and nurtures a culture of risk management within the organization. This function encompasses policies, procedures, and practices that integrate AI risk management into broader enterprise risk management and governance structures. Organizations must cultivate organizational processes and approaches that address risks associated with AI systems throughout their lifecycles.

Effective governance requires clear accountability structures that assign responsibility for AI risk management to specific individuals or teams with appropriate authority and resources. Senior leadership must demonstrate commitment to responsible AI practices through resource allocation, policy development, and oversight mechanisms. Organizations should establish cross-functional teams that include technical personnel, legal counsel, compliance officers, and business stakeholders to ensure comprehensive risk assessment and management.

The Govern function also addresses external engagement with stakeholders, including affected individuals, communities, and civil society organizations. Organizations should establish mechanisms for meaningful stakeholder input during AI system design, development, and deployment. Transparency regarding AI system capabilities, limitations, and risks enables informed stakeholder engagement and builds trust.

Map

The Map function establishes the context for AI risk management by identifying characteristics, categorizing risks, and documenting impacts associated with AI systems. Organizations must understand the intended purpose and expected use of AI systems, the context in which systems will operate, and the potential impacts on individuals, groups, organizations, and society.

Mapping activities include identifying and documenting AI system dependencies, data sources, and integration points with other systems and processes. Organizations must assess the context of use, including the setting in which the AI system will operate, the characteristics of the individuals or entities affected, and environmental factors that may influence system performance. Understanding context enables accurate risk identification and appropriate risk mitigation strategies.

Risk categorization under the Map function considers various dimensions of AI risk, including risks to individual rights and safety, organizational risks such as operational failures or reputational harm, and societal risks such as amplification of biases or erosion of public trust. Organizations should map both intended and potential unintended consequences of AI system deployment, recognizing that AI systems may be used in ways not anticipated during design.

Measure

The Measure function employs quantitative and qualitative methods to analyze, assess, benchmark, and monitor AI risks and impacts. Measurement activities provide empirical foundations for risk management decisions and enable organizations to track effectiveness of risk mitigation measures over time.

Organizations must establish metrics appropriate to the AI system, its context of use, and identified risks. Metrics should address multiple dimensions of AI system performance, including accuracy, reliability, safety, security, resilience, fairness, privacy, transparency, and accountability. Measurement approaches should be documented and validated to ensure they provide meaningful indicators of risk levels and system performance.

Testing and evaluation under the Measure function should occur throughout the AI system lifecycle, from initial development through deployment and ongoing operation. Pre-deployment testing validates that systems meet performance requirements under expected operating conditions. Post-deployment monitoring detects performance degradation, identifies emerging risks, and verifies continued effectiveness of risk controls. Organizations should establish thresholds and triggers that prompt investigation and remediation when metrics indicate unacceptable risk levels.

Manage

The Manage function allocates resources to mapped and measured risks on a regular basis and implements appropriate risk treatment strategies. Management activities translate risk assessments into concrete actions that reduce likelihood or impact of adverse outcomes while enabling beneficial uses of AI systems.

Risk treatment strategies include risk avoidance, risk mitigation, risk transfer, and risk acceptance. Organizations must select and implement controls appropriate to identified risks and organizational risk tolerance. Controls may include technical measures such as input validation, output filtering, or human oversight mechanisms, as well as procedural controls such as training requirements, access restrictions, or use limitations.

The Manage function requires documented decision-making processes that consider risk assessment findings, stakeholder input, and organizational values. Decisions regarding risk treatment should be traceable, with clear rationales for accepting, mitigating, or avoiding specific risks. Organizations must establish feedback loops that enable continuous improvement of risk management practices based on operational experience, stakeholder feedback, and evolving understanding of AI risks.

Risk Assessment and Scoring

Risk assessment under the NIST AI RMF requires systematic evaluation of likelihood and impact of potential adverse events associated with AI systems. Organizations must consider multiple risk dimensions, including technical risks arising from system failures or limitations, human risks related to misuse or misunderstanding, and systemic risks that emerge from widespread deployment or interconnected systems.

Effective risk assessment incorporates diverse perspectives and expertise. Technical assessments evaluate model architecture, training data, performance metrics, and operational constraints. Legal assessments identify applicable regulatory requirements and potential liability exposures. Ethical assessments consider impacts on fundamental rights, fairness, and social values. Business assessments evaluate operational, financial, and reputational risks.

Organizations should develop risk scoring methodologies that enable comparison and prioritization of risks across AI systems and use cases. Scoring approaches should consider both quantitative factors, such as measured error rates or affected population sizes, and qualitative factors, such as severity of potential harms or availability of alternative solutions. Risk scores inform resource allocation decisions and determine appropriate levels of oversight and control.

Risk assessments should be documented in detail sufficient to enable review and validation by internal stakeholders and external auditors. Documentation should describe the assessment methodology, data sources, assumptions, limitations, and conclusions. Risk assessments must be updated periodically and whenever material changes occur to the AI system, its operating environment, or understanding of relevant risks.

Controls and Safeguards

The NIST AI RMF emphasizes implementation of layered controls and safeguards to mitigate identified risks. Effective control strategies combine preventive controls that reduce likelihood of adverse events, detective controls that identify when issues occur, and corrective controls that remediate problems and prevent recurrence. Key control categories include:

• •Data quality controls that ensure training and operational data meet requirements for accuracy, completeness, representativeness, and relevance. Organizations must implement data validation, cleaning, and curation processes, as well as ongoing monitoring to detect data drift or degradation.
• •Model validation controls that verify AI system performance through rigorous testing using diverse test datasets, adversarial inputs, and edge cases. Validation should assess performance across relevant demographic groups and use contexts to identify potential disparate impacts.
• •Human oversight mechanisms that enable qualified personnel to review, override, or intervene in AI system operations. Effective oversight requires clear procedures, appropriate training, and system designs that facilitate meaningful human review and control.
• •Transparency and explainability measures that enable users and affected parties to understand how AI systems operate, what factors influence system outputs, and what limitations and uncertainties exist. Documentation should be tailored to different audiences, including technical users, operational personnel, and affected individuals.
• •Security controls that protect AI systems against unauthorized access, manipulation, or exploitation. Security measures should address threats to training data, model parameters, inference infrastructure, and system outputs.
• •Privacy controls that minimize collection and retention of personal information, implement appropriate access restrictions, and enable individual rights such as access, correction, and deletion where applicable.

Organizations must document implemented controls, verify their effectiveness through testing and monitoring, and maintain evidence of control operation for compliance and audit purposes. Controls should be reviewed and updated based on operational experience, changing risk profiles, and technological developments.

Documentation Expectations

Comprehensive documentation is essential to effective implementation of the NIST AI RMF. Documentation serves multiple purposes: enabling internal oversight and quality assurance, facilitating external audit and regulatory review, supporting transparency and accountability, and preserving institutional knowledge as personnel change over time.

Organizations should maintain AI system inventories that identify and describe all AI systems in development or deployment. Inventory records should include system purpose, classification, risk level, responsible parties, and current lifecycle stage. Inventories enable enterprise-wide risk visibility and inform resource allocation decisions.

System-specific documentation should include design specifications, development methodologies, data sources and characteristics, model architecture and parameters, performance metrics and validation results, identified risks and mitigation measures, deployment conditions and constraints, and monitoring and maintenance procedures. Documentation should be maintained in version-controlled repositories with clear ownership and update responsibilities.

Operational documentation captures evidence of ongoing risk management activities, including risk assessment updates, control testing results, incident reports and investigations, stakeholder feedback, and management decisions regarding risk treatment. Maintaining detailed operational records demonstrates commitment to continuous risk management and provides evidence of responsible practices.

Model Oversight

Effective model oversight requires establishing governance structures with clear roles and responsibilities for AI risk management. Organizations should designate individuals or teams responsible for oversight of AI systems throughout their lifecycles. Oversight responsibilities include reviewing and approving system designs, validating risk assessments, verifying implementation of required controls, monitoring system performance, and authorizing significant changes or deployments.

Oversight mechanisms should be proportionate to system risk levels. High-risk systems require more intensive oversight, potentially including dedicated review boards, independent validation, and ongoing monitoring by specialized personnel. Lower-risk systems may be subject to streamlined oversight processes while maintaining fundamental accountability structures.

Organizations should establish escalation procedures that ensure significant issues, unexpected behaviors, or changing risk profiles receive appropriate management attention. Escalation triggers should be clearly defined and regularly reviewed to ensure they capture material concerns. Management should receive regular reports on AI system performance, risk management activities, and emerging issues.

Integration with Enterprise Risk Management

The NIST AI RMF is designed to integrate with existing enterprise risk management frameworks and processes. Organizations should not treat AI risk management as separate or isolated from other risk domains. Rather, AI risks should be evaluated and managed within the broader context of operational, strategic, financial, legal, and reputational risks facing the organization.

Integration requires alignment of AI risk management policies, procedures, and controls with established enterprise risk management frameworks. Organizations using ISO 31000, COSO ERM, or similar frameworks should map AI risk management activities to existing risk management processes. This alignment ensures consistency of approach, avoids duplication of effort, and facilitates communication of AI risks to senior management and boards of directors.

Organizations should establish linkages between AI risk management and related disciplines including cybersecurity, privacy, compliance, and internal audit. AI systems implicate concerns across multiple domains, and effective risk management requires coordination among specialized teams. Regular communication and collaboration mechanisms ensure that relevant expertise is applied to AI risk issues and that risk management activities are appropriately coordinated.

Continuous Monitoring

Continuous monitoring is essential to managing AI risks throughout system lifecycles. AI systems may degrade in performance over time due to data drift, environmental changes, or adversarial activities. New risks may emerge as systems are used in unanticipated ways or as understanding of AI risks evolves. Continuous monitoring enables early detection of issues and timely implementation of corrective measures.

Organizations should implement automated monitoring systems that track key performance indicators, error rates, and other metrics relevant to system performance and risk management. Monitoring systems should generate alerts when metrics exceed established thresholds or exhibit unexpected patterns. Human review of monitoring data provides context and judgment necessary to interpret automated alerts and determine appropriate responses.

Monitoring should extend beyond technical performance metrics to include operational factors such as user feedback, stakeholder concerns, and changes in regulatory or policy environments. Organizations should establish channels for users and affected parties to report concerns or unexpected behaviors. Reported issues should be investigated promptly and addressed appropriately.

Monitoring findings should inform periodic reviews of risk assessments and risk management strategies. Organizations should establish review cycles that examine whether risk profiles have changed, whether implemented controls remain effective, and whether new risks or risk mitigation approaches should be considered. Review findings should be documented and used to update risk management plans and operational procedures.

Preparing for Assessments

Organizations implementing the NIST AI RMF should anticipate internal and external assessments of their AI risk management practices. Preparation for assessments includes:

• •Maintaining complete and current documentation of AI systems, risk assessments, controls, and monitoring activities. Documentation should be organized systematically to enable efficient review and should clearly demonstrate implementation of Framework functions and categories.
• •Conducting internal assessments to identify gaps or weaknesses in risk management practices. Internal assessments enable proactive remediation before external review and provide assurance that implemented practices align with Framework guidance.
• •Training personnel who will interact with assessors to ensure they understand Framework concepts, organizational implementation approaches, and their specific roles in risk management activities. Knowledgeable personnel facilitate efficient assessments and demonstrate organizational commitment to responsible AI practices.
• •Establishing procedures for responding to assessment findings and implementing corrective actions. Organizations should have clear processes for evaluating findings, prioritizing remediation activities, and verifying that corrective measures are effective.
• •Engaging with industry peers, professional associations, and standards bodies to understand evolving practices and expectations regarding AI risk management. Awareness of industry standards and emerging best practices positions organizations to meet or exceed assessment expectations.

Organizations should view assessments as opportunities to validate and improve risk management practices. Assessment findings provide valuable feedback that can strengthen governance structures, enhance operational processes, and demonstrate accountability to stakeholders. A culture that welcomes scrutiny and continuous improvement enables organizations to build and maintain mature AI risk management capabilities.