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Start by defining the policy’s scope and risk approach
Specify which AI systems and data uses the policy covers, including systems developed internally and those supplied by vendors. State who may authorize each use and how the depth of review will scale with the system’s intended use, potential effects, and the organization’s risk tolerance. A tool that summarizes internal notes and a system that influences consequential decisions may warrant different review and oversight.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary resource for managing risk across AI design, development, deployment, use, and evaluation. Its four functions are Govern, Map, Measure, and Manage, with governance applying throughout the lifecycle. NIST released AI RMF 1.0 on January 26, 2023, and says it is being revised. See NIST’s AI RMF status page and the AI RMF Core.
Assign accountability and keep an inventory
Make decision rights explicit
Name accountable executives, AI system owners, data owners or stewards, reviewers, and escalation contacts. Define who approves a new system or data use, who may grant an exception, who signs off on material changes, and who can pause or withdraw a system when a concern arises. Assign responsibilities for monitoring and communicating risk, not just initial approval. NIST’s AI RMF Playbook recommends documented roles, responsibilities, communication lines, and competent, empowered teams.
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Track systems and supporting data through retirement
Maintain an inventory of AI systems and the datasets they rely on. For each, record ownership, intended purpose, lifecycle status, and risk priority. Set a process for phase-out and retirement so that ending a system’s use also prompts decisions about associated data, access, records, and dependencies. An inventory helps reviewers see which uses rely on a dataset or supplier when a risk, restriction, or change is discovered.
Document data provenance and permitted use
Require documentation that lets a reviewer trace how data was obtained and prepared. Record the source and origin, collection context, relevant rights or restrictions, transformations, labeling, augmentation, dependencies, constraints, and metadata. The NIST Playbook’s governance prompts specifically ask organizations to consider these provenance details; it is guidance to adapt, not a checklist that every organization must follow in full.
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Pair provenance records with a review of whether the proposed use is permitted. A dataset’s availability does not by itself establish that it can be reused for a particular AI purpose. Require applicable privacy and security review, access controls, and retention and deletion rules. Have legal and privacy teams map obligations to each use case and jurisdiction rather than relying on one general policy as a substitute for that analysis. NIST’s Playbook also calls for identifying and documenting applicable legal requirements.
Set criteria for data quality, suitability, and bias
Define how teams will decide whether data is fit for the system’s intended context. Review relevance, availability, quantity, suitability, completeness, errors, and representativeness. Ask whether the data reflects the people, settings, and conditions in which the system will be used, and document important gaps, assumptions, and mitigation steps. Require teams to identify plausible data-related harms and bias, record decisions, and revisit them when the data or context changes.
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For high-risk AI systems within the scope of EU AI Act Article 10, the regulation sets specific data-governance requirements. These include examining design choices, data collection and origin, the original purpose of personal data, preparation steps, assumptions, dataset availability and suitability, bias, and gaps. Data must be sufficiently representative and, to the best extent possible, free of errors and complete for its purpose; appropriate bias detection, prevention, and mitigation measures are also addressed. These requirements apply to systems in scope, not to every AI rollout everywhere. Consult the European Commission’s Article 10 page and check the applicable consolidated text on EUR-Lex before treating a provision as binding for a particular system. The Commission service page describes its consolidated text as current through July 27, 2026, and notes amendments.
Include vendors and other third parties
Extend governance expectations to suppliers of data, models, software, and evaluation services. Due diligence should establish what evidence a supplier provides and who in your organization owns and reviews it. Specify how suppliers must notify you of material changes, cooperate during incidents, and support contingency plans if a high-risk third-party data source or system fails. Address relevant data and intellectual-property concerns as part of that review.
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Require approval, monitoring, and change control
Set pre-deployment sign-offs and name the people responsible for ongoing monitoring. Define a periodic review schedule appropriate to the system’s risk and use. A policy should also identify triggers for reassessment—for example, a change to the data, model, vendor, or intended use—and specify how incidents are reported and escalated and which records must be preserved. NIST’s AI RMF calls for monitoring and periodic review to be planned, with roles clear; a one-time approval alone does not establish lifecycle oversight.
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Require training appropriate to each role, such as what system owners must document or when reviewers should escalate a data concern. Establish a process for exceptions that records the rationale, accountable owner, and an expiry or review date. Define how noncompliance is reported and corrected, and who follows up. These provisions make accountability usable in day-to-day decisions rather than leaving it as a statement of principle.
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Compare alternatives using consistent criteria
When teams are choosing among datasets, vendors, or deployment designs, use the same criteria for each option. The following comparison axes are a practical synthesis of NIST governance guidance and the EU Act’s high-risk data criteria, not a published scoring standard.
| Criterion | Questions to ask |
|---|---|
| Fit to purpose and context | Does the option suit the intended use, users, and conditions of deployment? |
| Provenance and permitted use | Can the source, preparation, relevant restrictions, and basis for the proposed use be documented? |
| Quality and representation | Are the data sufficiently relevant, complete, representative, and suitable for the context, with errors and gaps understood? |
| Privacy and security | What exposure, access controls, retention, deletion, and review obligations apply? |
| Bias and impact | What data-related harms are plausible, and can they be identified and mitigated? |
| Third-party transparency and resilience | Will the supplier provide necessary evidence and change notices, cooperate during incidents, and support a contingency plan? |
| Monitoring and remediation | Can the organization feasibly monitor the option and correct problems after deployment? |
Use NIST guidance as a resource, not a legal shortcut
The NIST AI RMF and its Playbook are voluntary. The Playbook offers suggested actions aligned with the framework’s four functions; NIST says it is based on AI RMF 1.0 and will be updated after the framework revision. Use it to shape a policy suited to your organization rather than assuming every suggested action is mandatory. Legal duties depend on jurisdiction, sector, system, and use. For the EU AI Act, determine whether the system is within the relevant scope and verify the current official text before relying on a specific obligation. NIST’s status page, AI RMF Core, and Playbook provide starting points.
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