Before deploying AI, a data governance policy should make clear who can approve and pause a system, what data it uses and where that data came from, how teams check that it is fit for purpose, and how risks will be monitored throughout the system’s life. It should cover vendors and third-party data as well as internal assets. Treat the policy as a framework tailored to your organization and each use case—not a universal checklist.
Start with scope, purpose, and risk
Define which AI systems, datasets, and uses the policy covers. Record each system’s intended purpose and deployment context, and specify who may authorize it. Set review depth according to the risks and the organization’s risk tolerance rather than applying identical checks to every use.
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NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for managing risk across AI design, development, deployment, use, and evaluation. Its four functions are Govern, Map, Measure, and Manage; governance runs across the framework. NIST says AI RMF 1.0 was released on January 26, 2023 and is being revised. NIST AI RMF status and framework information.
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Name the people or roles accountable for the system and its data. A workable policy identifies executive accountability, system owners, data owners or stewards, reviewers, and escalation contacts. It should state who approves a new use, grants an exception, authorizes a material change, and can pause a system when a concern arises.
Document how these roles communicate and ensure teams have the competence and authority to carry out their responsibilities. NIST’s Govern guidance emphasizes defined roles, responsibilities, communication lines, and empowered teams. NIST AI RMF Playbook: Govern.
Maintain an inventory through retirement
Require an inventory of AI systems and the datasets they depend on. At minimum, record ownership, intended purpose, lifecycle status, and risk priority so reviewers can identify what is in use and who is responsible for it. Include a process for retiring or phasing out systems and their associated data access, rather than treating deployment approval as the end of governance.
Document data provenance and permitted use
For each material dataset, preserve enough information for a reviewer to trace how it was obtained and prepared. The policy should require records of:
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- Source and origin, collection context, and relevant rights, restrictions, or other use constraints.
- Transformations, preparation, labeling, and augmentation applied to the data.
- Dependencies and metadata needed to interpret the dataset and its role in the system.
NIST’s Playbook prompts organizations to document sources, origins, transformations, augmentations, labels, dependencies, constraints, and metadata. It also recommends identifying and documenting applicable legal requirements. NIST AI RMF Playbook: Govern.
Check data quality and fit for the intended context
Set review criteria before a dataset is approved for a particular use. Teams should assess whether the data is relevant and suitable for the intended purpose, available in adequate quantity, complete enough for that purpose, and affected by errors or gaps. They should also consider whether it represents the people, settings, and conditions in which the system will be used.
For high-risk AI systems within the scope of EU AI Act Article 10, dataset governance has specific requirements. The article addresses design choices; collection and origin; the original purpose of personal data; preparation steps and assumptions; dataset availability and suitability; bias examination and mitigation; and gaps. It requires relevant datasets to be sufficiently representative and, to the best extent possible, free of errors and complete for their purpose. These duties are specific to systems covered by the Act, not a general rule for every AI deployment. Check the official text and whether the law applies to the system and organization before treating a provision as binding. European Commission AI Act Service Desk: Article 10.
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Address privacy, security, and bias together with use
Require privacy and security review for the specific use case, with appropriate access controls and retention and deletion rules. Before data is reused or repurposed, check that the proposed use is permitted under the applicable restrictions and requirements. A general policy cannot replace jurisdiction- and sector-specific legal analysis; route the use case to legal and privacy reviewers where needed.
Have teams identify plausible data-related bias and harms, record their decisions and mitigations, and revisit the assessment when the data or context changes. For covered EU AI Act high-risk systems, Article 10 specifically addresses bias examination and appropriate detection, prevention, and mitigation measures. European Commission AI Act Service Desk: Article 10.
Bring vendors and third parties inside the governance boundary
Apply documentation and due-diligence expectations to suppliers of data, models, software, and evaluation services. The policy should identify who owns and retains evidence about supplied components, how vendors must notify the organization of material changes, and how they will cooperate on incidents. It should also set contingency actions for a supplier failure, especially when a high-risk system depends on third-party data or services.
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NIST’s Govern guidance calls for attention to third-party AI risks, including data and intellectual-property concerns, and contingency planning for failures involving high-risk third-party data or systems. NIST AI RMF Playbook: Govern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set approval, monitoring, incident, and change procedures
Specify required sign-offs before deployment and set a review cadence appropriate to the system’s risk and use. Assign responsibility for monitoring and define how issues are reported and escalated, what records must be preserved, and who decides whether the system should be paused.
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Train staff and govern exceptions
Require training suited to each person’s role, including the people who approve, build, operate, review, or monitor AI systems and data. Establish a documented exception process: identify the approver and accountable owner, explain the exception, and set an expiry or review date. Include a route for reporting and correcting noncompliance.
Compare alternatives on the same evidence
When choosing between datasets, vendors, or deployment designs, use consistent criteria rather than relying on a single quality score. Compare:
- Fit to the intended purpose and context.
- Provenance and permitted use.
- Quality and representativeness.
- Privacy and security exposure.
- Potential bias and impact risks.
- Third-party transparency and resilience.
- Feasibility and cost of monitoring and remediation.
These comparison dimensions are practical synthesis, not a published scoring standard. NIST’s AI RMF Playbook is voluntary suggested guidance aligned with the framework, not a checklist organizations must follow in full. NIST says the Playbook is based on AI RMF 1.0 and will be updated after the framework revision. NIST AI RMF Playbook overview.
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Distinguish guidance from legal obligations
NIST’s AI RMF and Playbook are voluntary resources. They can help structure a governance program, but they do not replace applicable law or establish a universal compliance checklist. Legal obligations depend on jurisdiction, sector, system characteristics, and use. EU AI Act Article 10 applies to high-risk systems covered by that regulation; consult the official text and assess applicability before making a binding-obligation claim. The European Commission service page describes its consolidated text as current through July 27, 2026, and notes that amendments exist. European Commission AI Act Service Desk: Article 10.
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