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A useful generative-AI data policy does more than ban sensitive prompts. It governs every unstructured-data flow—documents, email, chat, tickets, source code, images, audio, video, indexes, prompts, outputs and logs—from collection through deletion. Organize the policy around NIST’s Govern, Map, Measure and Manage functions, then make each requirement enforceable through ownership, classification, access controls, vendor terms, logging, testing and incident response.
NIST AI RMF 1.0, published January 26, 2023, is a voluntary, rights-preserving, non-sector-specific and use-case-agnostic framework. NIST released its Generative AI Profile, NIST-AI-600-1, on July 26, 2024. Together they provide a practical backbone for deciding what data an AI system may use, under which conditions and with what evidence.
What an unstructured-data governance policy must control
Unstructured data is information that does not fit neatly into a fixed database schema: office documents, PDFs, email, collaboration messages, support tickets, source code, scanned records, photographs, recordings, videos and the metadata attached to them. Generative-AI systems can encounter this material in several ways:
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- A retrieval-augmented generation (RAG) connector indexes a repository and returns passages at answer time.
- A team uses files to fine-tune a model or build an evaluation set.
- An application sends prompts, retrieved context and outputs through an API or plug-in.
- The service stores prompts, responses, safety events, telemetry and audit logs.
- People export an answer to email, a ticket, a document or another system.
A policy that covers only the prompt window misses the index, cache, vendor system, output and backup copies. It also fails to answer who may approve a use case, which legal basis permits the data, how long the data may remain and how a deletion request reaches every derivative copy.
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The policy should therefore define a complete flow: source repository, ingestion, transformation, index or training set, prompt, model or vendor, output, downstream sharing, retention and deletion. It should state the business purpose, affected people, permitted data classes, geographic boundaries, accountable owner and evidence required before launch.
Choose a governance backbone and make it operational
NIST AI RMF 1.0 is the most flexible starting point for an enterprise policy. NIST describes it as “voluntary, rights-preserving, non-sector specific, and use-case agnostic.” More than 240 organizations contributed to its development, according to the NIST AI Resource Center in 2023. The framework is not a certification standard; it gives teams common outcomes and actions for managing AI risk.
The Generative AI Profile (NIST-AI-600-1), released July 26, 2024, adds risks that become prominent when systems generate or transform content, including prompt and retrieval leakage, harmful or misleading outputs, data and model poisoning, rights concerns and third-party supply-chain exposure.
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- Govern: establish accountability, policies, roles, legal obligations, supply-chain expectations, exception authority and review cadence.
- Map: identify the use case, data flows, affected people, jurisdictions, foreseeable harms, rights and threat scenarios.
- Measure: test controls, monitor performance and harm indicators, preserve evidence and report results to accountable owners.
- Manage: prioritize risk, apply mitigations, handle exceptions, respond to incidents and suspend or retire uses that cannot be controlled.
Governance runs across the lifecycle rather than appearing as a one-time approval. NIST’s AI Resource Center says, “The AI RMF Core provides outcomes and actions that enable dialogue, understanding, and activities to manage AI risks.” Its Playbook is deliberately not a rigid procedure: “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Select the outcomes that fit each use case, document why they apply and retain evidence.
ISO/IEC 42001 provides a useful comparison point when an organization wants an AI management system with formal management-system language, documented processes and audit readiness. NIST publishes a crosswalk connecting policy and data-resource activities to ISO clauses. The two approaches can coexist: use NIST functions to structure risk decisions and the ISO crosswalk to identify management-system evidence or certification needs.
| Comparison axis | NIST AI RMF | ISO/IEC 42001 |
|---|---|---|
| Primary character | Voluntary risk-management framework | AI management-system standard |
| Scope | Flexible across sectors and use cases | Organization-wide management-system scope selected by the organization |
| Lifecycle coverage | Govern, Map, Measure and Manage across the lifecycle | Documented management-system processes, objectives and continual improvement |
| Unstructured-data specificity | Requires teams to identify data, impacts, risks and controls; the Generative AI Profile adds relevant risks | Use the published NIST crosswalk and data-resource controls to map evidence |
| Third-party controls | Explicit attention to software, data and supply-chain risks | Address suppliers and outsourced processes within the management system |
| Audit and certification | No certification is implied by using the framework | Can support an auditable management system; certification depends on an accredited assessment route |
| Implementation effort | Scale controls to risk and organizational capacity | Usually requires more formal documentation, objectives, internal review and continual-improvement evidence |
Federal guidance also emphasizes protecting, disseminating and disposing of information correctly and negotiating vendor terms when using generative AI. Those principles belong in the policy’s records, security, privacy and procurement sections.
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Set scope, accountability and decision rights
Write the scope so that a team cannot evade the policy by changing the interface or hosting location. Cover employee prompts, RAG indexes, fine-tuning and training sets, evaluation sets, model outputs, logs, plug-ins, APIs, internally hosted models and vendor-hosted services. State whether personal devices, experiments, open-source models and low-code automations are included.
Name one accountable policy owner. Give other roles explicit decisions rather than generic consultation duties.
| Role | Required decision or duty |
|---|---|
| Policy owner | Maintains the policy, approves the control baseline and reports exceptions and metrics. |
| Data owner | Determines business purpose, sensitivity, access group, retention rule and whether a data class may be used for prompting, retrieval, fine-tuning, evaluation, output storage or external sharing. |
| AI application owner | Documents the use case, model, connectors, prompts, outputs, user population, testing and residual risk. |
| Security | Designs identity, least-privilege, encryption, DLP, secrets, monitoring, vulnerability and incident controls. |
| Privacy | Assesses personal-data processing, notices, individual rights, minimization, cross-border transfers and deletion. |
| Legal and records | Maps statutory, contractual, copyright, privilege, discovery and retention obligations. |
| Procurement | Runs vendor due diligence and ensures required contractual terms and evidence are obtained. |
| Incident-response authority | Can disable a connector, revoke a key, suspend a use case and coordinate containment, notification and lessons learned. |
| Exception approver | Accepts or rejects documented exceptions, sets an expiry date and requires compensating controls. |
Require separation between ingestion, retrieval, administration and export roles. A person who can configure a connector should not automatically be able to read every indexed document or export all model responses.
Build a continuously updated unstructured-data inventory
Inventory is the foundation for every later decision. Start with repositories rather than AI tools, because the same document may feed several applications. Include file shares, document-management systems, email, chat, ticketing, wikis, code repositories, collaboration workspaces, scanned archives, image stores, audio and video libraries, backups and shadow repositories discovered during access reviews.
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- Named owner and steward.
- Business purpose and process supported.
- Data subjects, sensitivity and jurisdiction.
- Retention and legal-hold rule.
- Provenance: creator, source system, acquisition date, transformations and chain of custody.
- Access groups, privileged roles and external collaborators.
- Rights or license basis, including copyright and contractual restrictions.
- Whether the class is permitted for prompting, retrieval, fine-tuning, evaluation, output storage or external sharing.
- Connected indexes, caches, training sets, exports and vendors.
- Deletion method and evidence location.
Make the catalog continuous. A nightly connector, a new collaboration workspace or a copied archive can change the risk profile without changing the formal system name. Reconcile repository permissions with the index permissions and flag orphaned files, stale groups, duplicate copies, unknown owners and data whose retention period has expired.
Classify data and set enforceable defaults
Use a small classification scheme that employees and automated controls can apply consistently. Labels should drive technical behavior, not merely decorate documents.
| Class | Typical content | Default generative-AI handling | Required conditions |
|---|---|---|---|
| Public | Approved public web content, published reports and released marketing material | Allowed for approved tools when the business purpose and rights are clear | Use an approved service, preserve provenance and prevent accidental inclusion of confidential context |
| Internal | Routine internal procedures, non-sensitive project material and ordinary operational messages | Allowed in an approved tenant or gateway after owner confirmation | Least-privilege access, logging, retention setting and restrictions on external sharing |
| Confidential | Non-public strategy, customer information, financial plans or contract material | Allowed only for an approved use case with a named owner and controlled model or tenant | Purpose limitation, DLP or redaction, access testing, vendor terms and short, documented retention |
| Restricted personal | Personal information requiring heightened protection | Deny by default | Documented privacy assessment, minimization, approved region and model, strict access and deletion propagation |
| Regulated | Information subject to sector, statutory, contractual or export restrictions | Deny by default until legal, privacy and security approval for a specific flow | Applicable legal basis, processing location, contractual controls, monitoring and evidence of disposal |
| Trade secret | Source code, algorithms, unreleased designs and strategic know-how | Deny by default | Explicit owner approval, isolated tenant or deployment, no provider training or secondary use, export controls and tested access boundaries |
For restricted classes, record the exact model, tenant, processing region, purpose, retention period, users and compensating controls in the approval. Do not treat “enterprise” branding as proof that a service is suitable. Check the actual configuration and contract.
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Require provenance and rights checks for third-party or copyrighted material. A file being readable by an employee does not automatically grant permission to use it for training, retrieval or a generated derivative.
Control each AI data flow
Prompts and direct uploads
Place approved chat and API access behind identity-aware gateways where feasible. Detect sensitive labels, personal identifiers, secrets and regulated fields before transmission. Offer redaction or tokenization, show the user what will be removed, and block the request when policy cannot be satisfied. Prevent browser plug-ins, personal accounts and unmanaged API keys from bypassing the approved route.
Warn users that an output can inherit confidential context and that copying an answer into email, a ticket or a public channel is a new disclosure decision. Log the user, timestamp, data classification, destination, model or vendor, policy result and any export action without storing more sensitive content than necessary.
Retrieval-augmented generation indexes
Apply source permissions at retrieval time, not only when a document is ingested. A user must not receive a passage merely because another user or service account indexed it. Preserve source identifiers and classifications in the index, filter by the requester’s current authorization, and re-check access after group or employment changes.
Separate ingestion, index administration and retrieval credentials. Test cross-tenant, cross-project and deleted-document cases. Treat prompt-injection text inside a document as untrusted content; it must not override system instructions, authorize a tool call or cause the connector to disclose other records.
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Fine-tuning and evaluation sets
Use a documented inclusion rule for training and evaluation material. Remove unnecessary personal data, secrets, privileged content and licensed material whose terms do not permit the intended use. Keep a manifest with source, owner, rights basis, transformations, version, approval and deletion dependencies. Ensure that removing a source record triggers removal or rebuild of every derived set that contains it.
Outputs, caches and logs
Classify outputs according to their content, not according to the model that produced them. A summary of a restricted contract remains restricted. Define whether prompts and outputs are retained, where they reside, who can search them and when they are deleted. Include caches, safety-review queues, observability systems, backups and vendor-held copies in the retention map.
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Deletion is complete only when it propagates through the source repository, index, cache, training or evaluation set, application database, logs and vendor systems covered by the contract. Keep an auditable deletion result and document any legally required hold that prevents removal.
Map and approve each use case
Do not approve “generative AI” as a single category. Approve a defined use case with a bounded data flow. Use a short risk record that answers these questions:
- Describe the purpose and users. State the business outcome, user population, model or vendor, interfaces, connectors, plug-ins and downstream systems.
- Identify affected people and data. List data classes, jurisdictions, rights or license bases, retention rules and people who may be affected by an inaccurate or harmful result.
- Analyze threats and foreseeable harms. Consider unauthorized retrieval, prompt leakage, malicious documents, poisoning or tampering, hallucination impact, re-identification, unfair treatment, copyright complaints, vendor misuse and service compromise.
- Select mitigations and acceptance criteria. Specify access filters, redaction, human review, output restrictions, rate limits, monitoring, vendor terms, fallback procedures and the conditions under which the system must refuse or escalate.
- Record residual risk and approval. Name the decision maker, evidence location, expiry or review date, open exceptions and the authority that can suspend the use case.
Use stricter review when the output affects employment, eligibility, safety, legal rights, financial decisions, health, children or large groups of people. A human reviewer should have enough source context and authority to challenge or override an output; a nominal click-through review is not a meaningful control.
Govern vendors and the supply chain
NIST identifies third-party software, data and other supply-chain issues as governance concerns. Your policy should require due diligence before data reaches a model provider, hosted index, plug-in, managed gateway or subcontractor.
Contract and verify:
- No provider training, profiling or secondary use of customer content unless the organization expressly approves it for a defined purpose.
- Data-location, cross-border-transfer and subprocessor disclosure.
- Retention controls, prompt and output deletion, backup handling and deletion certificates or equivalent evidence.
- Access logging, administrator controls, encryption and support-access restrictions.
- Security incident and suspected misuse notification timelines, cooperation and preservation of relevant logs.
- Rights to audit or receive independent assessment evidence, testing summaries and material-change notices.
- Support for individual rights, legal holds, copyright complaints and data-subject or customer deletion requests.
- Service exit, portability and verified destruction of indexes, caches, logs and derived data.
Review the service configuration after procurement. A contract cannot compensate for an enabled retention option, shared workspace, public connector or unrestricted administrator role that the organization never disabled.
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Translate policy language into controls that can fail visibly:
- Identity and least privilege: use central identity, multifactor authentication, short-lived credentials and separate roles for ingestion, retrieval, administration and export.
- Classification enforcement: pass labels and repository permissions into indexes and gateways; block or quarantine restricted content before transmission.
- Data-loss prevention: detect secrets, regulated identifiers and high-risk phrases; support redaction, user justification and security review.
- Isolation: separate development, evaluation and production data; prevent one tenant, project or customer from retrieving another’s content.
- Integrity and provenance: hash or version source files where appropriate, record transformations and flag unexpected bulk changes or suspicious documents.
- Monitoring: capture source accessed, requester, model or vendor, retrieval result, export destination, policy decision and administrative changes while minimizing sensitive log content.
- Encryption: protect data in transit and at rest according to the organization’s security baseline, with controlled key access.
- Human and application guardrails: require review for high-impact outputs, constrain tools and actions, validate citations or source passages and provide a safe refusal path.
Test the controls with realistic cases: a user whose access was recently revoked, a deleted source document, a malicious instruction embedded in a PDF, a poisoned data batch, a prompt containing secrets, an attempted cross-tenant retrieval, a vendor retention change and an output copied to an unapproved channel. Record expected and observed results, remediation owner and retest date.
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Define incidents and response
Make the following reportable events explicit: accidental disclosure of sensitive data, unauthorized retrieval or export, poisoned or tampered documents, model or vendor misuse, rights or copyright complaints, and harmful or materially misleading outputs.
- Contain: disable the connector, integration or API key; pause affected jobs; block exports; and isolate suspect indexes or datasets.
- Preserve evidence: retain relevant access, retrieval, prompt, output, configuration and vendor logs under the applicable legal-hold process.
- Revoke and correct access: remove compromised credentials, review groups and permissions, invalidate cached results and rebuild affected indexes or datasets when integrity is uncertain.
- Assess impact: identify data subjects, records, jurisdictions, recipients, vendor systems and downstream copies; determine whether legal, privacy, contractual or regulatory notification duties apply.
- Coordinate: notify the data owner, security, privacy, legal, records and incident-response authority; require vendor action under the contract.
- Recover and learn: restore from a known-good source, validate controls, document root cause and update the policy, training, tests or contract before reopening the use case.
Define severity levels and decision times in the incident plan, but do not rely on a severity label to delay containment when restricted data may still be exposed.
Measure whether the policy works
Every metric needs an owner, evidence location, threshold and review date. A dashboard of unowned percentages creates the appearance of control without accountability.
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| Metric | What it demonstrates | Useful evidence |
|---|---|---|
| Inventory coverage | Known repositories and AI-connected collections versus the discovered estate | Catalog records, connector list and reconciliation report |
| Owner and classification coverage | Percentage of repositories with a current owner and usable label | Catalog history and access-review results |
| Sensitive-data blocking rate | Whether gateway and DLP rules stop or quarantine prohibited transfers | Policy-decision logs, sampled events and false-positive review |
| Access-review completion | Whether repository, index and administrator permissions are reviewed on schedule | Signed review records and revoked-access tickets |
| Provenance and rights-review completion | Whether third-party and copyrighted material has a documented basis | Manifests, licenses, approvals and exception records |
| Deletion propagation success | Whether removal reaches sources, indexes, caches, datasets, logs and vendors | Deletion test results and vendor confirmations |
| Vendor-assessment completion | Whether providers meet security, privacy, retention and supply-chain requirements | Due-diligence files, contract clauses and assessment evidence |
| Retrieval and export logging coverage | Whether investigators can reconstruct who accessed and shared content | Log-source inventory, sample traces and retention checks |
| Time to detect and contain | Operational response speed for AI-data incidents | Incident timeline and ticket timestamps |
| Open-exception count and age | Whether temporary deviations are being closed rather than becoming permanent | Exception register, expiry dates and compensating-control evidence |
| Training completion | Whether users and administrators understand permitted data handling | Course records, attestations and targeted exercise results |
Review metrics at a defined cadence appropriate to risk. Investigate trends, not only threshold breaches: a falling block rate may mean safer behavior, or it may mean a gateway was bypassed.
Roll out the policy in controlled stages
Stage 1: establish the baseline
Appoint the policy owner and decision rights, publish the classification scheme, identify high-risk repositories and freeze unapproved external AI transfers while the inventory is built. Select a small number of representative use cases rather than attempting to authorize every experiment at once.
Stage 2: connect inventory to enforcement
Import repository owners, labels, permissions, retention rules and rights information into a catalog. Require every AI connector to reference an inventory record and a named use-case approval. Put identity, DLP and logging controls in front of the approved services.
Stage 3: test and expand
Run deletion, access-boundary, prompt-leakage, malicious-document and vendor-retention tests. Fix failures, record evidence and expand to additional repositories only when the control owner accepts the residual risk.
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Stage 4: operate continuously
Review owners, permissions, vendors, model changes, connectors, incidents, exceptions and metrics on a defined schedule. Trigger an out-of-cycle review after a material data-source, model, vendor, legal or processing-location change.
Common policy failures and their fixes
- “Never paste confidential data” with no approved alternative: provide a controlled gateway, classification guidance and a documented approval path so employees do not move work to personal tools.
- Index permissions trusted once at ingestion: enforce authorization at retrieval and test revoked users and deleted documents.
- One label for all internal content: separate personal, regulated, trade-secret and ordinary internal material so controls match impact.
- Vendor terms treated as a questionnaire: verify actual retention, training, subprocessor, region, logging and deletion settings and preserve evidence.
- Logs that capture everything forever: minimize log content, set a retention rule and protect logs as sensitive data.
- Exceptions without expiry: require a compensating control, accountable approver, review date and automatic escalation when the date passes.
- Human review as a rubber stamp: give reviewers source context, time, authority and a clear escalation route.
- Policy stops at the model response: classify exports and downstream copies, and test deletion through caches, backups and vendor systems.
Minimum contents of the published policy
A concise policy document can point to detailed standards and procedures, but it should contain these enforceable statements:
- Purpose, scope, definitions and covered AI data flows.
- Classification levels and default permitted or prohibited uses.
- Named roles, approval authority, suspension authority and exception process.
- Inventory, ownership, provenance, rights, retention and deletion requirements.
- Least-privilege, encryption, DLP, logging, monitoring and testing requirements.
- RAG, fine-tuning, evaluation-set, prompt, output, plug-in and API rules.
- Vendor due diligence and mandatory contract terms.
- Use-case risk assessment, human oversight and residual-risk acceptance.
- Incident definitions, containment actions, notification responsibilities and recovery.
- Metrics, evidence retention, review cadence and triggers for out-of-cycle review.
The policy is ready for production when each approved use case has a traceable owner, inventory record, classification decision, access test, vendor assessment where applicable, retention and deletion path, incident route, measurable controls and a documented decision about residual risk. That evidence—not the policy’s length—shows whether unstructured data is actually governed.
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