What Is The NIST AI Risk Management Framework?

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As artificial intelligence (AI) rapidly transforms industries, from healthcare and finance to law enforcement and education, questions of risk, responsibility, and trust loom large. To address these concerns, the National Institute of Standards and Technology (NIST) released the AI Risk Management Framework (AI RMF) in January 2023 — a voluntary but powerful tool designed to help organizations develop and deploy trustworthy AI systems. While the AI RMF is not a regulatory mandate, its adoption signals a growing consensus around best practices in AI governance. It provides a flexible and principle-based structure that can be used by companies, government agencies, and developers to identify and mitigate the unique risks associated with AI technologies.

What Is the NIST AI RMF?

The AI RMF is a risk-based, socio-technical framework that guides organizations in managing the many facets of AI risk — not just technical errors or security issues, but also fairness, transparency, privacy, and societal impact. It is a voluntary guidance framework developed by the NIST to help organizations identify, assess, manage, and minimize risks associated with artificial intelligence (AI) systems. The AI RMF helps organizations (1) understand and manage AI-related risks across the lifecycle; (2) build transparency, accountability, fairness, and security into AI systems; and (3) align with global AI governance trends (e.g., EU AI Act, OECD AI Principles). It is sector-agnostic and technology-neutral, meaning it can be applied to any organization building or using AI, whether in healthcare, finance, education, defense, or consumer technologies.

The framework is organized into two primary parts:

  1. Core – Practical functions for managing AI risks: Govern, Map, Measure, and Manage.
  2. Profiles – Customizable use-case templates that organizations can adapt based on their risk tolerance and operational needs.

The Four Core Functions

  1. Govern

The “Govern function” establishes the overarching policies, processes, and organizational structure needed for effective AI risk management. It includes:

  • Accountability structures
  • Ethics guidelines
  • Organizational culture of responsibility

For example, it urges organizations to define roles and responsibilities for AI oversight, ensuring that someone is responsible for outcomes — including negative externalities or unintended bias.

  1. Map

The “Map function” emphasizes understanding the AI system’s intended purpose, context, stakeholders, and risks. This stage requires:

  • Documenting the use case
  • Identifying relevant legal and societal impacts
  • Understanding dependencies (e.g., datasets, third-party models)

This step is especially critical for preventing “black box” deployments in sensitive fields like criminal justice or hiring.

  1. Measure

Here, organizations assess the reliability, robustness, bias, privacy, and security of AI systems. This includes:

  • Model evaluation metrics (accuracy, precision, fairness, etc.)
  • Auditing data provenance and quality
  • Testing under edge cases or adversarial conditions

The “Measure function” reinforces that technical metrics alone aren’t enough — organizations must also measure ethical and societal implications.

  1. Manage

Finally, the “Manage function” is about implementing risk response strategies. This includes:

  • Mitigating known risks
  • Updating AI models in production
  • Communicating with stakeholders about limitations or residual risks

This is a continuous process, requiring lifecycle management rather than “set-it-and-forget-it” deployment.

Why does the NIST AI RMF matter?

The NIST AI RMF is quickly becoming the “de facto standard” for AI governance in the U.S., especially as regulatory momentum builds at the state and federal level. It aligns with global efforts like:

  • The EU AI Act
  • OECD AI Principles
  • The White House AI Bill of Rights

By adopting the framework, organizations demonstrate a proactive approach to AI risk — a critical posture as regulators, investors, and the public increasingly demand transparency and accountability in AI systems.

Practical Benefits for Businesses and Lawyers

For businesses, the AI RMF offers:

  • A structured process to reduce legal, reputational, and operational risk
  • Better collaboration between technical, legal, and compliance teams
  • A roadmap to building consumer trust and ethical AI products

For lawyers and privacy professionals, the framework:

  • Serves as a tool for due diligence in AI procurement and deployment
  • Informs contractual obligations with vendors or clients (e.g., in AI SaaS agreements)
  • Helps assess compliance readiness under evolving regulations

Conclusion

As artificial intelligence becomes more embedded in daily life, managing its risks is no longer optional — it’s essential. The NIST AI Risk Management Framework provides a clear and actionable path for any organization that wants to build safe, trustworthy, and compliant artificial intelligence systems. Whether you’re a startup experimenting with generative models or a multinational deploying predictive analytics at scale, integrating the NIST AI RMF can help ensure that your AI strategy is not just innovative, but responsible. So, it’s a critical tool for organizations that want to build and deploy artificial intelligence responsibly — and for lawyers, privacy professionals, and regulators who want a common language for AI risk. Please feel free to call our law firm to speak with an artificial intelligence attorney regarding your questions.