NIST AI RMF Map Function: How to Identify AI Risks

NIST Map is the function within the NIST AI Risk Management Framework that establishes the context needed to frame risks related to an AI system. It helps organizations understand what an AI system is intended to do, where it will operate, who will use or be affected by it, what components it depends on, and what benefits, risks and impacts may result.

Context matters because the same AI model can present different risks depending on its purpose, users, deployment environment, data, human oversight and influence over real-world decisions. Evaluating technical performance alone is therefore insufficient.

In AI RMF 1.0, Map contains five categories:

• MAP 1 establishes and understands context.

• MAP 2 categorizes the AI system.

• MAP 3 examines capabilities, targeted usage, goals, expected benefits and costs.

• MAP 4 maps risks and benefits across system components.

• MAP 5 characterizes impacts on people, organizations and society.

Map supports AI risk identification and provides a foundation for Measure and Manage. It is not a complete risk assessment, mandatory checklist or rigid sequence of steps.

Framework status: This article explains NIST AI RMF 1.0. NIST currently states that AI RMF 1.0 is being revised and that the Playbook will be updated after the revision. Organizations should review the NIST AI Resource Center for the latest status before applying the framework.

What Is NIST Map?

NIST Map is one of the four core functions in the NIST AI Risk Management Framework. NIST defines its purpose as establishing the context needed to frame risks related to an AI system.

The function examines AI as part of a sociotechnical system. This means considering not only the model or algorithm, but also the people, processes, organizational objectives, technologies and operating conditions that shape its effects.

Relevant context may include:

• Intended purposes and potentially beneficial uses

• Users, operators and affected parties

• Prospective deployment settings

• Applicable laws, norms and expectations

• Organizational goals and risk tolerance

• System requirements and knowledge limits

• Human oversight arrangements

• Data, software and third-party components

• Potentially beneficial and harmful impacts

This information helps an organization identify where risks may arise and which factors may contribute to them. It can also inform an initial decision about whether an AI solution is appropriate for the intended purpose.

NIST states that Map outcomes provide a basis for Measure and Manage. The framework also calls for continued application of Map as context, capabilities, risks, benefits and potential impacts evolve. NIST AI RMF 1.0

Map is therefore broader than a technical security review. Security may be relevant, but so are organizational incentives, legal rights, user behavior, deployment conditions, expected benefits and effects on individuals or communities.

Where Does Map Fit in the NIST AI RMF?

The NIST AI RMF organizes its core around four functions:

• Govern supports a culture of AI risk management through policies, accountability, roles and organizational processes.

• Map establishes and understands context and frames risks related to an AI system.

• Measure analyzes, assesses, benchmarks and monitors risks and related impacts.

• Manage prioritizes risks and supports appropriate risk responses.

These NIST AI RMF functions are connected, but they are not a rigid waterfall process. Govern applies across the framework, while Map, Measure and Manage can inform one another as systems and circumstances change.

For example, testing may reveal a limitation that changes the organization’s understanding of the approved context. A risk response may also change the system’s deployment conditions, requiring the organization to revisit Map.

The AI RMF is voluntary guidance. Using it does not automatically satisfy a law, establish regulatory compliance or create a NIST certification.

The 5 NIST Map Categories Explained

MAP 1: Establish and Understand Context

The official MAP 1 category states: “Context is established and understood.”

MAP 1 defines the environment in which AI risks and benefits must be considered. It covers intended purposes, potentially beneficial uses, context-specific laws, norms and expectations, prospective deployment settings, users and potential impacts.

It also addresses the organization’s mission, AI goals, business value, risk tolerance and system requirements. Assumptions about the system’s purposes, uses and risks should be documented, along with relevant limitations and test, evaluation, verification and validation considerations.

Interdisciplinary participation is another important element. Technical teams may not have all the expertise needed to understand legal, operational, social and human consequences. Depending on the system, the mapping process may involve legal, compliance, risk, security, human-factors, domain and user-experience specialists.

MAP 1 connects system decisions to organizational and societal context. A technically capable system may still be inappropriate if its purpose conflicts with organizational policy, its use exceeds established risk tolerance or its requirements fail to reflect the needs of affected people.

MAP 2: Categorize the AI System

The official MAP 2 category states: “Categorization of the AI system is performed.”

Categorization begins by defining the specific tasks the system will support and the methods used to perform them. Examples include classifiers, recommenders and generative models.

MAP 2 also covers the system’s knowledge limits and how its outputs will be used and overseen by humans. Documentation should give relevant AI actors enough information to make informed decisions and take subsequent actions.

Scientific integrity and TEVV considerations are included as well. NIST identifies experimental design, data availability, representativeness and suitability, system trustworthiness and construct validation as relevant considerations.

In practice, MAP 2 helps distinguish what a system actually does from what users may assume it can do. It links the technical method to the task, output, operating limits and human decision process.

MAP 3: Understand Capabilities, Uses, Benefits and Costs

MAP 3 covers “AI capabilities, targeted usage, goals, and expected benefits and costs compared with appropriate benchmarks.”

This category is broader than documenting capabilities and limitations. It examines whether the system’s functionality and performance could deliver the expected benefits. It also considers monetary and non-monetary costs arising from errors, system behavior or trustworthiness concerns.

MAP 3 includes:

• Potential benefits of intended functionality and performance

• Potential monetary and non-monetary costs

• Appropriate benchmarks

• Targeted application scope

• Operator and practitioner proficiency

• Processes for human oversight

The application scope should distinguish acceptable operation from uses outside the system’s documented boundaries. A system designed to support a narrow administrative task should not automatically be considered suitable for making consequential decisions about individuals.

Human considerations are equally important. Operators need sufficient proficiency to interpret outputs, recognize limitations and follow escalation procedures. Human oversight also requires defined responsibilities and processes, not simply a statement that a person remains involved.

MAP 4: Map Risks and Benefits Across AI System Components

MAP 4 states that risks and benefits are mapped for all components of the AI system, including third-party software and data.

An AI system may depend on models, data, application software, cloud infrastructure, user interfaces, monitoring tools, external APIs and vendor services. Mapping only the primary model can leave important dependencies unexamined.

MAP 4 addresses technology and legal risks associated with these components. It expressly includes third-party software and data, along with possible infringement of intellectual property or other third-party rights.

Organizations should also identify existing internal risk controls for system components, including third-party AI technologies. At this stage, the focus is on identifying the controls and their relationship to mapped components. Assessing whether those controls operate effectively is addressed more directly in Measure.

A useful output from MAP 4 is traceability between system components, their expected benefits, relevant risks, third-party dependencies and existing controls.

MAP 5: Characterize AI Impacts

MAP 5 characterizes impacts on individuals, groups, communities, organizations and society.

It covers both potentially beneficial and harmful impacts. For each identified impact, organizations consider likelihood and magnitude using relevant evidence. NIST identifies expected use, previous uses of similar systems, public incident reports, external feedback and other data as possible evidence sources.

The available evidence will depend on the system and its lifecycle stage. Early mapping may rely on comparable systems, structured assumptions and stakeholder input. Operational systems may provide evidence from monitoring, complaints, incidents and observed outcomes.

MAP 5 also calls for practices and personnel that support regular engagement with relevant AI actors. Feedback should address positive, negative and unanticipated impacts.

Input from users, affected groups, domain specialists and other relevant actors can reveal consequences that the development or deployment team did not anticipate.

The official categories and subcategories are available in the NIST AI RMF Core and the NIST Map Playbook.

How to Apply the NIST Map Function in Practice

The following is one practical way to organize Map activities. It is not an official or mandatory sequence. NIST states that the Playbook is neither a checklist nor a set of steps that must be followed in its entirety.

Start With the AI System’s Purpose and Context

Define the intended purpose, expected value, users, affected parties, deployment setting and relevant requirements. Record what the system is intended to support and which uses fall outside its approved scope.

Connect the proposed use to organizational objectives, policies, applicable requirements and risk tolerance.

Characterize the System and Its Capabilities

Describe the system’s tasks, methods, inputs, outputs and knowledge limits. Clarify how outputs will influence decisions and what human oversight is expected.

Document targeted usage, appropriate benchmarks, application boundaries, expected benefits, potential costs and operator proficiency requirements.

Identify Risks and Benefits Across Components

Map relevant data sources, models, software, interfaces, infrastructure and third-party dependencies.

For each component, identify technology and legal risks, expected benefits, existing controls, assumptions and unresolved information gaps.

Characterize Potential Impacts

Identify the individuals, groups, communities and organizations that may experience beneficial or harmful effects. Consider the likelihood and magnitude of each impact using the best available evidence.

Include input from relevant AI actors, especially when internal teams may not fully understand the deployment environment or affected population.

Document the Results and Revisit Them

Maintain a usable record of purposes, assumptions, system boundaries, components, risks, benefits, impacts and evidence sources.

Revisit Map when the model, data, provider, users, purpose, operating environment or decision process changes. New evidence, feedback, incidents and unanticipated uses may also require remapping.

NIST Map Example: Identifying Risks in an AI Hiring System

The following is an illustrative example, not an official NIST case study.

Assume an organization is considering an AI system that helps recruiters prioritize applications for human review.

This exercise does not demonstrate that the system is accurate, fair or acceptable. It establishes context and identifies questions that can guide subsequent measurement, evaluation and management decisions.

How NIST Map Supports AI Risk Assessment

Map supports AI risk assessment by defining the conditions under which risks, benefits and impacts should be examined.

It helps establish the relevant system boundaries, intended and foreseeable uses, affected parties, dependencies, limitations, assumptions and potential impacts. These outputs can inform the selection of testing methods, metrics, benchmarks and assessment priorities in Measure.

They can also support decisions about risk prioritization and response in Manage.

Completing MAP 1-5 is not equivalent to completing an entire risk assessment or risk-management process. Map establishes context and frames relevant risks. Further analysis, measurement, prioritization, response and monitoring may still be necessary.

Understanding Map is one part of applying the complete framework. Build practical knowledge of how Govern, Map, Measure and Manage work together with NIST AI Risk Management Framework Training.

NIST Map vs Measure vs Manage

Map identifies what requires attention and why it matters in the relevant context. Measure generates evidence about identified risks, impacts and trustworthiness characteristics. Manage uses mapped and measured information to prioritize and respond to risk.

This distinction helps organizations assign responsibilities for managing AI risks. Mapping does not replace testing, and measurement alone does not determine organizational risk tolerance.

How NIST Map Applies to Generative AI

Organizations can apply Map to generative AI by defining the intended task, users, deployment context and approved output uses. They should also document knowledge limits, human oversight and uses that fall outside the approved scope.

The system map may include foundation models, application layers, retrieval systems, relevant data, external tools and third-party providers. Impact characterization should consider how generated outputs may affect users, other individuals, groups and organizational decisions.

Because generative AI systems may be adapted or used in unexpected ways, organizations should revisit context, capabilities and impacts as uses evolve.

NIST AI 600-1, the Generative Artificial Intelligence Profile, is a voluntary companion resource to AI RMF 1.0. It provides additional guidance for examining generative AI risks within the framework’s broader structure. NIST Generative Artificial Intelligence Profile

Common Mistakes When Using NIST Map

Treating Map as a Checklist

Completing a template does not prove that context is understood. Select and adapt relevant Map and Playbook activities to the system, lifecycle stage and organizational circumstances.

Focusing Only on Technical Risks

Technical performance and security are only part of the context. Include organizational objectives, legal rights, human behavior, deployment conditions, expected benefits and broader impacts.

Mapping Only the Model

An AI system includes data, interfaces, infrastructure, application software, human processes and third-party dependencies. Each can introduce or amplify risk.

Ignoring Human Oversight

Do not rely on a vague statement that a person reviews outputs. Define who provides oversight, what information they receive and when they must intervene, reject or escalate a result.

Treating Mapping as a One-Time Exercise

Revisit Map when capabilities, data, components, uses, deployment settings, evidence, risks, benefits or impacts change.

NIST Map and AI RMF Implementation

Within broader NIST AI RMF implementation, Map connects organizational governance with system-level measurement and risk response.

Govern provides policies, roles, accountability and risk-management expectations. Map applies that direction to a specific AI system and context. Measure assesses mapped risks and relevant trustworthiness characteristics, while Manage prioritizes and responds to the resulting information.

Organizations should adapt the framework to their objectives, resources, sector, legal environment and risk tolerance. The NIST AI RMF Playbook provides voluntary suggested actions and documentation practices aligned with the framework’s subcategories.