Operationalize AI risk management
Enable structured AI governance, risk visibility, and continuous oversight aligned with the NIST AI Risk Management Framework across the full AI lifecycle.
Assess your complianceResponsible AI through risk management
The NIST AI RMF enables organizations to manage risks across the AI lifecycle. It promotes trustworthy AI by addressing the characteristics that define it, while reducing harm and supporting beneficial outcomes.
A structured approach to AI risk management
The NIST AI RMF is organized around four core functions:
– Govern establishes structures, processes, and oversight to embed AI risk management across the organization.
– Map defines the context to understand AI systems and identify relevant risks.
– Measure evaluates and monitors risks through testing, evaluation, verification, and validation processes.
– Manage prioritizes risks, allocates resources, and supports ongoing monitoring, response, and continuous improvement.
Why it matters?
The NIST AI RMF provides a flexible framework to manage AI-related risks across technical, ethical, legal, and societal dimensions. It strengthens decision-making and accountability, enabling organizations to build trust, reduce harm, and scale AI with confidence.
With Naaia
Govern AI with confidence
Build trust in your AI use cases with structured governance, oversight, and risk management.
Map AI risks
Identify, assess, and monitor AI risks across your systems, models, and use cases.
Scale AI risk management
Extend AI risk oversight consistently across teams, models, and business functions.
Operationalize trustworthy AI
Turn trustworthy AI principles into scalable governance, risk management, and operational practices.
Learn about other regulations & norms
Frequently asked questions
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What are the four core functions of the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework is organized around four core functions:
(1) GOVERN — establishing the organizational structures, policies, and accountability mechanisms for AI risk management;
(2) MAP — contextualizing AI risks by identifying the system’s intended uses, affected stakeholders, and potential impacts;
(3) MEASURE — analyzing and assessing identified risks through quantitative and qualitative methods; and
(4) MANAGE — prioritizing and implementing risk treatments, monitoring their effectiveness, and maintaining a risk register. The four functions are designed to be applied iteratively and adapted to any organization size, sector, or AI use case. -
Is the NIST AI RMF mandatory, and which organizations are required to follow it?
The NIST AI RMF is not universally mandatory — it is a voluntary framework developed for broad applicability across sectors. However, mandatory application is expanding: US federal agencies and their contractors are increasingly required to align with NIST AI RMF principles under executive orders and sector-specific guidance. Financial services regulators, healthcare authorities, and critical infrastructure operators are also beginning to reference NIST AI RMF compliance in supervisory expectations. For non-US organizations, the NIST AI RMF is widely used as a governance reference because it maps closely to EU AI Act requirements — making it a practical tool for multinational compliance programs.
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How does the NIST AI RMF Govern-Map-Measure-Manage framework reduce AI risk in practice?
The NIST AI RMF reduces AI risk by providing a structured process that ensures no dimension of risk is overlooked. GOVERN establishes the organizational foundation — without clear ownership and policy, risk management efforts remain inconsistent. MAP forces teams to think systematically about who is affected by the AI system and how, preventing narrow technical risk assessments that miss social or operational impacts. MEASURE provides the analytical rigor to prioritize which risks require action. MANAGE completes the process by implementing treatments and monitoring their effectiveness. Applied continuously, the framework transforms AI risk management from a reactive process into a proactive organizational capability.