What Is the NIST AI RMF “Map” Function and How Do You Apply It?

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Key takeaways

• The Map function is the first of four core NIST AI RMF functions: Map, Measure, Manage, Monitor.
• It documents purpose, deployment setting, stakeholders, assumptions, and TEVV (trustworthiness, explainability, validity, verifiability) metrics.
• Use the official MAP playbook sub - activities (e.g., MAP 1.1, MAP 2.3) to create a machine - readable JSON/YAML artifact.
• The MAP artifact feeds directly into the Measure and Manage functions, enabling automated risk tooling.
• NIST also provides a separate “Map Overlay Schema” for GIS raster overlays and a research - grade map - quality metric for robotics.

What does the NIST AI RMF “Map” function actually do?

The Map function captures the context, intended purpose, stakeholders and expected impact of an AI system before it is deployed. It creates a structured record that can be read by tools and shared across teams.

The function is defined in the NIST AI RMF playbook and is one of four core functions: Map, Measure, Manage, Monitor. Its goal is to make the risk profile of an AI system explicit early in the lifecycle.

Which sub - activities should you complete for a complete MAP artifact?

The MAP playbook lists numbered sub - activities that together cover purpose, requirements, task definition, knowledge limits, scientific integrity and TEVV considerations.

Complete each item in the order that makes sense for your project. The playbook provides templates and checklists for each sub - activity.

How do you produce a machine - readable MAP document?

NIST recommends storing the MAP information in JSON or YAML so that downstream tooling can parse it automatically. A minimal JSON example looks like this:

{
 "purpose": "Fraud detection for online payments",
 "deployment": "Cloud service, EU region only",
 "stakeholders": ["Risk team", "Compliance officer", "End users"],
 "impact": {
 "positive": "Reduced chargebacks",
 "negative": "Potential false positives affecting legitimate users"
 },
 "requirements": {
 "privacy": "GDPR compliant data handling",
 "safety": "No automated denial of service"
 },
 "tevv": {
 "trustworthiness": "Accuracy > 95% on validation set",
 "explainability": "Feature importance scores provided",
 "validity": "Model retrained quarterly",
 "verifiability": "Model version hash stored in audit log"
 }
}

Store the file alongside your model artifacts and reference it in your CI/CD pipeline. Automated checks can verify that required fields are present before a release is allowed.

How does MAP feed into the other AI RMF functions?

The output of MAP becomes the input for Measure and Manage. Measure uses the documented purpose and TEVV goals to define quantitative performance and trust metrics. Manage uses the identified risks and stakeholder concerns to plan mitigations, assign responsibilities and schedule reviews.

A simple workflow:

1. Complete MAP sub - activities and generate JSON.
2. Run a script that extracts TEVV goals and creates a Measure baseline (e.g., target precision, recall, bias thresholds).
3. Feed the risk list into a risk - register tool that links each risk to a mitigation action for the Manage phase.

What other “Map” artifacts does NIST publish?

Beyond the AI RMF, NIST defines a Map Overlay Schema for GIS raster overlays. The schema (object name mapOverlay) includes fields for opacity, geographic extent, coordinate reference system and metadata. It is used in the IoT Data Foundations Project to standardise map - overlay exchange.

NIST also published research on Map Quality Assessment for robot - generated maps. The algorithm extracts local features (Harris corners, Hough Transform, SIFT) and reports the proportion of matched features against a ground - truth map as a quality score.

How can you start using the MAP function today?

1. Download the MAP playbook from the NIST AI RMF website.
2. Choose a JSON or YAML template that matches your organization’s tooling.
3. Fill in the required sub - activities (MAP 1.x and MAP 2.x) for your AI project.
4. Integrate a linting step in your CI pipeline to verify the JSON schema.
5. Pass the validated MAP artifact to your Measure and Manage processes.

Following these steps gives you a concrete, auditable record of AI context and risk before any code is released.

For a deeper dive into the full NIST AI RMF, see the official playbooks and the accompanying JSON schema definitions on the NIST website.