Documenting AI risk with NIST's four functions

IBM Technology. "Mastering AI Risk: NIST's Risk Management Framework Explained." YouTube.

Tech, law, and product strategy intersect at the foundational level. This section covers technical concepts that matter for governance (the technical deep-dives), how obligations work in practice, what privacy means for product design, and why emerging frameworks shape what you can build next.

AI can do remarkable things, but it can also cause real damage. Poorly managed systems amplify bias, breach security, and fail catastrophically. Organizations need a repeatable way to manage these risks. The U.S. National Institute of Standards and Technology (NIST) AI Risk Management Framework offers one approach. Drawing from IBM Technology's explanation of the framework, this piece breaks down how the RMF's four functions work in practice—useful for both technical teams building systems and legal teams documenting compliance.

What makes AI trustworthy?

Before you can manage risk, you need to know what success looks like. NIST defines trustworthy AI through seven interconnected characteristics—not a checklist, but a set of principles that define your target.

A trustworthy system must be valid and reliable: accurate outputs, consistent performance. This connects directly to fairness. A biased system isn't valid because it fails to produce correct information for all populations. Fairness isn't just ethics—it's a technical requirement.

These systems also need to protect the information they handle. That means being secure and resilient against external attacks while preserving privacy in internal data handling. Security defends against disruption, data theft, and model poisoning. Privacy measures prevent improper disclosure of sensitive information. Both matter for maintaining system integrity and data confidentiality.

For humans to oversee AI effectively, its operations must be legible. Explainable and interpretable systems let domain experts—medical professionals, financial auditors—understand why the system reached a particular conclusion. This legibility enables accountable and transparent operations, preventing black box decision-making. Together, these characteristics establish clear lines of human responsibility for system outcomes.

These seven characteristics define what you're aiming for. The RMF's four functions provide the operational process to get there.

The four functions: Govern, Map, Measure, Manage

The NIST framework operates through four interconnected functions. They're not sequential steps—they form a continuous improvement cycle for managing AI risk throughout a system's lifecycle.

Govern: Building a risk management culture

Govern is the foundation layer that runs through everything else. As the IBM explanation puts it, "The governance function is where we're going to start by setting the overall culture for the system." This function establishes your organization's approach to AI risk management and ensures compliance with internal policies and external regulations. It shapes every activity in the other three functions, embedding risk management into organizational DNA.

Map: Establishing context

Map addresses a common problem: AI development involves scattered teams—data science, operations, legal—and "they don't all have visibility of what everyone else is doing." This function breaks down silos by creating shared, end-to-end understanding of the system's context. You set explicit goals, define roles and responsibilities, and formally establish your risk tolerance for the specific application. Risk assessment becomes tied to business objectives and a complete view of the AI pipeline.

Measure: Assessing and analyzing risk

Measure is where technical analysis happens. You develop and deploy methods for analyzing, testing, and monitoring AI risks. The framework accommodates both quantitative (numeric) and qualitative (high, medium, low) approaches. The IBM source warns that purely quantitative methods can create a "false sense of security" by implying unwarranted precision. A central element here is implementing thorough procedures for test, evaluation, verification, and validation—continuous monitoring integrated across the entire AI lifecycle.

Manage: From assessment to action

Manage is where assessment becomes response. After identifying and measuring risks, you prioritize them based on potential impact and alignment with your goals from the Map phase. Then you choose a response:

• Mitigate the risk (for instance, by "put[ting] in some kind of compensating control")
• Accept the risk if it falls within established tolerance
• Transfer the risk to another party
• Indemnify against the risk (for example, by "buy[ing] insurance")

This management cycle provides the repeatable process, but it needs specific context to be effective. That's where profiles come in.

Profiles: From framework to implementation

The four functions provide universal structure, but they're deliberately abstract. The RMF uses profiles to translate the general framework into a specific, actionable plan for a particular AI system or context. A profile isn't just customization—it's how you document specific implementation details and outcomes. For legal and compliance purposes, the profile is where abstract governance principles become concrete, auditable evidence of due diligence for a specific system.

For product teams

Product managers and developers can use the NIST RMF as a blueprint for building safety and trustworthiness into the product lifecycle. The Map function's activities—defining goals, mapping who's involved, establishing risk tolerance—should be treated as foundational requirements-gathering tasks. This ensures that risk considerations influence system architecture from the start. The Measure function's emphasis on test, evaluation, verification, and validation provides clear direction: integrate continuous model testing and performance monitoring into your CI/CD pipeline. This approach turns abstract principles into concrete engineering requirements, creating the verifiable foundation that compliance teams need.

For legal and governance teams

Legal counsel and compliance officers can use the NIST RMF as structured methodology for managing enterprise liability as AI regulation evolves. The framework creates a defensible process for demonstrating due diligence and responsible stewardship of AI technologies. The Govern function anchors any compliance program, ensuring legal and regulatory obligations are identified and integrated into the AI lifecycle from the outset. The Manage function creates a defensible, risk-based decision-making process. By documenting how you prioritized risks and explaining each response (mitigation vs. acceptance), you build a clear, auditable trail showing informed and deliberate choices—valuable protection in a complex regulatory environment.

Making it work

The NIST AI RMF shifts AI risk management from abstract principles to structured, operational process. For organizations starting this work, begin with the Govern and Map functions to establish cultural alignment and contextual understanding. The framework helps you move beyond just using AI to managing it with appropriate discipline. When AI is ubiquitous, trust matters. The RMF provides a tool for building that trust through continuous improvement.