What Is the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework (AI RMF 1.0), published by the National Institute of Standards and Technology on January 26, 2023, is the de facto standard for managing risks associated with artificial intelligence systems in the United States. Developed through extensive multi-stakeholder consultation, the framework provides organizations with a structured, flexible, and technology-neutral approach to identifying, assessing, and mitigating AI-related risks throughout the AI lifecycle.
While the AI RMF is voluntary for private-sector organizations, it has rapidly become a baseline expectation for enterprises seeking to demonstrate responsible AI practices. For federal agencies, adoption is effectively mandatory under Executive Order 14110 (October 2023) and subsequent OMB guidance (M-24-10), which direct agencies to implement the AI RMF for all AI systems that impact rights or safety.
The framework is designed to be used alongside existing organizational risk management processes - it does not replace enterprise risk management (ERM) or cybersecurity frameworks like NIST CSF, but rather extends them to address the unique characteristics of AI systems, including opacity, non-determinism, data dependency, and emergent behavior.
For organizations deploying enterprise AI platforms, the AI RMF provides the authoritative structure for building governance programs that satisfy both regulatory expectations and stakeholder trust. Areebi maps directly to each of the framework's four core functions, enabling automated compliance with AI RMF requirements.
The Four Core Functions of the AI RMF
The AI RMF is organized around four core functions that provide a lifecycle-based approach to AI risk management. Each function contains categories and subcategories that define specific outcomes organizations should achieve.
1. GOVERN: Cultivating a Culture of AI Risk Management
The GOVERN function is the foundational cross-cutting function that establishes the organizational structures, policies, and processes necessary for effective AI risk management. Unlike the other three functions, GOVERN applies across the entire AI lifecycle and informs how MAP, MEASURE, and MANAGE are executed.
Key GOVERN outcomes include:
- GOVERN 1: Policies, processes, procedures, and practices are in place and enforced to map, measure, and manage AI risks
- GOVERN 2: Accountability structures are established with clear roles and responsibilities
- GOVERN 3: Workforce diversity, equity, inclusion, and accessibility are prioritized
- GOVERN 4: Organizational teams are committed to a culture of risk management
- GOVERN 5: Processes for engagement with relevant AI actors and stakeholders are established
- GOVERN 6: Policies and procedures are in place to address AI risks from third-party entities
Areebi's policy engine directly implements GOVERN outcomes by providing enforceable, machine-readable policies that apply automatically across every AI interaction. Role-based access controls, approval workflows, and audit trails establish the accountability structures GOVERN requires.
2. MAP: Contextualizing AI Risks
The MAP function establishes the context for framing AI risks. It helps organizations understand the conditions under which AI systems operate, who is affected, and what benefits and risks are associated with their use.
Key MAP outcomes include:
- MAP 1: Context is established and understood, including intended purpose, deployment conditions, and stakeholder impacts
- MAP 2: Categorization of AI systems and their risk profiles
- MAP 3: AI capabilities, targeted usage, goals, and expected benefits are understood
- MAP 4: Risks and benefits are mapped for all components of the AI system
- MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized
With Areebi, organizations can classify AI systems by risk level, map data flows, identify sensitive data exposure points, and maintain a comprehensive inventory of all AI usage across the enterprise - satisfying MAP requirements through continuous visibility rather than periodic assessments.
3. MEASURE: Quantifying and Tracking AI Risks
The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniques, and methodologies to analyze, assess, benchmark, and monitor AI risk and related impacts.
Key MEASURE outcomes include:
- MEASURE 1: Appropriate methods and metrics are identified and applied
- MEASURE 2: AI systems are evaluated for trustworthy characteristics (validity, reliability, safety, fairness, transparency, explainability, privacy, security)
- MEASURE 3: Mechanisms for tracking identified AI risks over time are in place
- MEASURE 4: Feedback about efficacy of measurement is collected and integrated
Areebi's compliance dashboards provide real-time metrics on AI usage, data exposure, policy violations, and security events. These dashboards enable continuous measurement rather than point-in-time assessments, directly satisfying MEASURE requirements with quantifiable, auditable data.
4. MANAGE: Allocating Resources to Mapped and Measured Risks
The MANAGE function is where organizations take action on the risks identified through MAP and quantified through MEASURE. It involves prioritizing, responding to, and monitoring AI risks on a regular basis.
Key MANAGE outcomes include:
- MANAGE 1: AI risks based on assessments and other analytical output from MAP and MEASURE are prioritized, responded to, and managed
- MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by input from relevant AI actors
- MANAGE 3: AI risks and benefits from third-party resources are managed
- MANAGE 4: Risk treatments, including response and recovery, and communication plans are documented and monitored
Areebi's data loss prevention controls, guardrails, and real-time intervention capabilities enable organizations to actively manage identified risks. When a policy violation is detected - such as an employee attempting to paste proprietary source code into an AI prompt - Areebi automatically blocks the action, logs the event, and alerts the security team.
AI RMF Profiles and Use Cases
NIST has developed companion resources to help organizations implement the AI RMF in specific contexts. The most significant of these is the AI RMF Generative AI Profile (NIST AI 600-1), released in July 2024, which addresses the unique risks posed by generative AI and large language models.
The Generative AI Profile identifies 12 risks specific to GAI systems:
- CBRN (Chemical, Biological, Radiological, Nuclear) information
- Confabulation (hallucination)
- Data privacy
- Environmental impacts
- GAI-generated content misuse (deepfakes)
- Homogenization of outputs
- Human-AI configuration
- Information integrity
- Information security
- Intellectual property
- Obscene, degrading, or abusive content
- Value chain and component integration
Each of these risk areas maps to specific controls that enterprise AI platforms must implement. Areebi addresses all 12 GAI risk areas through its integrated policy engine, DLP controls, content filtering, and comprehensive audit logging.
Organizations can use the Areebi AI Governance Assessment to evaluate their current maturity against AI RMF profiles and identify gaps requiring remediation.
Implementing the AI RMF in Your Organization
Successful AI RMF implementation requires a phased approach that integrates AI risk management into existing enterprise processes. Based on our work with enterprises across financial services, healthcare, and government, we recommend the following approach:
Phase 1: Establish Governance (GOVERN)
Begin by establishing your AI governance committee, defining roles and responsibilities, and creating an initial set of AI use policies. This phase typically takes 4-8 weeks and should involve legal, compliance, IT security, and business stakeholders.
Phase 2: Discover and Classify (MAP)
Conduct a comprehensive inventory of all AI systems in use across the organization - including shadow AI tools that employees may be using without IT approval. Classify each system by risk level based on its intended use, data exposure, and impact on decisions.
Phase 3: Instrument and Measure (MEASURE)
Deploy monitoring and measurement capabilities across all identified AI systems. This includes usage analytics, data flow analysis, policy violation tracking, and security event monitoring. Areebi's dashboards provide these capabilities out of the box.
Phase 4: Enforce and Manage (MANAGE)
Activate enforcement controls including DLP scanning, content guardrails, and automated policy enforcement. Establish incident response procedures for AI-related security events and compliance violations.
Organizations seeking to accelerate their AI RMF implementation can request a demo to see how Areebi automates compliance across all four core functions.
Federal Agency Requirements and Executive Orders
While the AI RMF itself is voluntary for private-sector organizations, several federal directives have made it the required standard for government AI systems:
- Executive Order 14110 (October 30, 2023): "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence" - directs federal agencies to implement AI risk management consistent with the NIST AI RMF
- OMB Memorandum M-24-10 (March 2024): Requires federal agencies to designate Chief AI Officers, maintain AI use case inventories, and implement minimum risk management practices aligned with the AI RMF
- OMB Memorandum M-24-18 (October 2024): Expands requirements for AI governance in federal acquisition, directing agencies to include AI RMF compliance requirements in contracts with AI vendors
For technology vendors seeking to sell AI solutions to the federal government, AI RMF compliance is increasingly a procurement prerequisite - often alongside FedRAMP authorization. Areebi's architecture is designed to satisfy both frameworks simultaneously, providing a unified compliance posture for organizations serving government and commercial customers.
Learn more about how Areebi supports government AI governance on our Government Solutions page, or explore our Trust Center for detailed security documentation.
Relationship to Other AI Governance Frameworks
The NIST AI RMF does not exist in isolation. It is designed to be interoperable with other domestic and international AI governance frameworks:
- ISO/IEC 42001: The international standard for AI management systems. Organizations pursuing ISO 42001 certification will find significant overlap with AI RMF requirements, particularly in governance structures and risk assessment methodologies.
- OECD AI Principles: The AI RMF's trustworthiness characteristics (valid, reliable, safe, fair, transparent, explainable, privacy-enhanced, secure) align closely with the OECD's five AI principles.
- EU AI Act: While the EU AI Act takes a regulatory approach compared to the AI RMF's voluntary framework, many of the risk management practices overlap - particularly for high-risk AI systems.
- Colorado AI Act: Colorado's algorithmic discrimination law draws heavily on NIST concepts of AI risk and trustworthiness, and explicitly references the AI RMF in its regulatory guidance.
Organizations using Areebi can manage compliance across multiple frameworks from a single platform, reducing duplication of effort and ensuring consistent risk management practices. Visit our Compliance Hub to explore all supported frameworks.