An employee wants to use an AI assistant to summarize customer conversations. The business case is straightforward: less administrative work, faster follow-up, and more time for customers. The governance questions are less obvious. Can the assistant access every customer record? Will the provider retain the conversations? Could a summary expose information to someone who cannot access the original?
These are not reasons to reject AI. They are reasons to approve it deliberately.
Before approving an AI use case, establish what data it can access, who is accountable, what the provider may do with that data, and how your organization will verify its controls. A policy alone cannot answer those questions. You need named owners, documented decisions, and evidence that safeguards work.
This vendor-neutral checklist gives business leaders, IT and security teams, and tool approvers a practical way to evaluate AI data use before deployment—and keep it governed afterward.
Start with the Use Case, Not the Tool
“We approved the platform” is not the same as “we approved every use of it.” Drafting marketing copy from public information presents different risks from analyzing employee records, reviewing confidential contracts, or connecting an assistant to internal repositories.
Define the proposed purpose, users, data sources, outputs, and actions the system can take. Identify whether it only generates suggestions or can also update records, send messages, or trigger transactions. Apply review effort proportionate to sensitivity and potential consequences.
The NIST AI Risk Management Framework provides a useful foundation through its Govern, Map, Measure, and Manage functions. It is voluntary. The checklist below is a practical implementation aid, not an official NIST checklist or a guarantee of legal compliance.
Approve a defined use case with defined data access—not an open-ended permission to use AI.
The AI Data Governance Checklist
☐ 1. Name an Accountable Owner
Assign a business owner responsible for the use case and its risks. Identify security, privacy, legal, and technical reviewers. Document who can authorize deployment, accept exceptions, and suspend the system. The owner should have decision-making authority, not simply responsibility for completing paperwork.
NIST’s framework core emphasizes clear, documented roles and communication channels for managing AI risks.
- Owner: Business sponsor, supported by the governance lead.
- Evidence: Responsibility matrix, named approvers, and a recorded approval decision.
☐ 2. Inventory AI Tools and Their Data Flows
Include standalone applications, custom systems, and AI features embedded in existing software. Map prompts, uploads, connected repositories, training or fine-tuning data where applicable, outputs, and logs. Identify where data crosses organizational or service boundaries.
For example, a meeting assistant may process more than audio: attendee details, transcripts, summaries, and shared links also belong in the inventory. The NIST Generative AI Profile recommends inventories covering provenance, sensitive data, underlying models, and oversight.
- Owner: IT asset manager and system architect.
- Evidence: AI register and data-flow diagram identifying storage locations and downstream services.
☐ 3. Define Permitted Data and Purposes
Specify which data classifications each use case may process and which are prohibited. Address personal information, confidential business records, intellectual property, and regulated data explicitly. Review licenses, confidentiality obligations, privacy commitments, and applicable legal requirements before using third-party or personal data.
An assistant approved for public product documentation should not automatically receive customer contracts. Likewise, permission to access a dataset does not necessarily establish permission to use it for model training.
- Owner: Data owner, with privacy and legal reviewers.
- Evidence: Permitted-use policy, classification mapping, and documented data-rights review.
☐ 4. Review the Provider’s Data-Use Commitments
Ask whether the provider uses inputs or outputs for training, allows human review, retains data, or shares it with other parties. Check the specific service, subscription, contract, and configuration being purchased. Consumer, enterprise, and API offerings may have different terms.
The FTC warns that AI providers must honor privacy and confidentiality commitments, including promises about training use. Capture contractual protections and verified settings rather than relying on marketing statements.
- Owner: Procurement and legal, supported by security.
- Evidence: Vendor assessment, relevant contract terms, and dated configuration records.
☐ 5. Minimize Sensitive Inputs
Send only the information needed for the task. Remove unnecessary identifiers, credentials, secrets, and unrelated records before transfer. Evaluate masking, pseudonymization, aggregation, or other privacy-preserving techniques against the intended purpose.
For a customer-service trend analysis, issue categories may be sufficient without names, addresses, or account numbers. Do not assume masking makes data anonymous: remaining details can still identify individuals, and transformations can affect accuracy.
- Owner: Data engineering lead and privacy reviewer.
- Evidence: Documented preprocessing process, sample checks, and a rationale for retained sensitive fields.
☐ 6. Enforce Access and Integrity Controls
Apply least-privilege access to users, service accounts, repositories, and administrative functions. Verify encryption in transit and storage, review permissions, and protect datasets against unauthorized alteration. The joint government guidance on AI data security addresses these controls alongside data minimization and provenance.
For a repository-connected assistant, test whether users can retrieve or infer information they cannot access directly. A connector should not turn restricted documents into broadly available answers.
- Owner: Security engineering and identity administrators.
- Evidence: Access-review results, permission tests, encryption configuration, and integrity checks.
☐ 7. Track Data Provenance
Record where data originated, which version was used, how it was transformed, and who changed it. Include reference documents and retrieval indexes, not just datasets used to train models. Define how questionable, outdated, or potentially tampered data will be investigated.
If an assistant gives incorrect policy guidance, the team should be able to determine whether the source was obsolete, the ingestion process changed it, or the system misinterpreted it.
- Owner: Data steward and AI system maintainer.
- Evidence: Dataset or source register, transformation records, version history, and investigation procedure.
☐ 8. Test Before Release—and After Changes
Set acceptance criteria for output quality, privacy, security, and relevant harmful-bias risks before testing. Evaluate representative tasks, failure scenarios, unauthorized-access attempts, and attempts to manipulate the system through malicious instructions in retrieved content.
Repeat evaluations when models, source data, permissions, integrations, or intended uses change. A successful pilot using public documents does not validate a later deployment using personnel records. Record unresolved risks and the authority accepting them.
- Owner: Evaluation lead, with security and relevant business reviewers.
- Evidence: Test report, acceptance criteria, unresolved-risk register, and release decision.
☐ 9. Define Retention and Exit Procedures
Set retention rules for prompts, uploads, outputs, logs, indexes, and other stored artifacts. Document how those rules interact with legal holds, contractual obligations, and provider deletion practices. Verify what deletion covers and what may remain in backups or downstream systems.
Decommissioning should include disabling connectors, revoking credentials, removing unnecessary stored data, and addressing dependent workflows. Canceling a subscription is not a complete exit plan.
- Owner: Records-management lead and system owner.
- Evidence: Retention schedule, deletion verification where available, dependency inventory, and exit checklist.
☐ 10. Monitor and Rehearse Incident Response
Monitor for unauthorized access, sensitive-data exposure, unexpected transfers, and deteriorating data quality. Collect enough information to investigate without unnecessarily copying sensitive prompts into monitoring systems.
Define escalation paths and containment actions, including disabling connectors or suspending access. Rehearse a scenario in which an assistant exposes restricted information: who stops access, preserves evidence, contacts the provider, and assesses notification obligations?
- Owner: Security operations and incident-response lead, with the business owner.
- Evidence: Monitoring reports, response playbook, exercise record, and tracked remediation actions.
Turn the Checklist into an Approval Gate
Use one tracking sheet for each use case:
Control | Owner | Status | Evidence | Next review | Exception expiry
Link each entry to its supporting document or test result. If a control does not apply, record why. If a gap requires an exception, document its scope, compensating safeguards, approving authority, and expiration.
Do not mark a control complete without evidence.
Approval can be conditional. For example, a team might pilot a summarization tool with public documents while contractual questions remain unresolved. That approval should explicitly prohibit confidential uploads and repository connections until the required reviews are complete.
Revisit approval after material changes, not only on a calendar. Expanded data access, new provider terms, or a new automated action can change the risk substantially.
Make Governed Adoption the Default
Effective AI data governance makes adoption more predictable. Employees know what they may use, approvers know what they must verify, and leaders understand who owns the residual risk.
Start with one proposed AI use case this week. Name its owner, map its data flows, and work through the checklist. Document the gaps, assign remediation, and require explicit approval before expanding access. The goal is not paperwork for its own sake. It is knowing what your AI can touch—and having evidence that those boundaries hold.