A workable policy for generative AI starts with the data, not the model. Define which documents and other unstructured content may enter prompts, retrieval indexes, fine-tuning sets, evaluation sets, outputs and logs; assign an accountable owner; enforce least-privilege access; control vendors; and retain evidence that the rules work. Organize the program around NIST’s Govern, Map, Measure and Manage functions, with governance operating across the entire AI lifecycle.
NIST AI RMF 1.0, published January 26, 2023, is voluntary, rights-preserving, non-sector-specific and use-case-agnostic. NIST released its Generative AI Profile, NIST-AI-600-1, on July 26, 2024, to address risks that are especially acute when models ingest and reproduce large volumes of content. The result should be a living control system for documents, email, chats, tickets, code, images, audio, video and the services that process them.
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What the policy must govern
“Unstructured data” is any content whose meaning is not captured solely by a fixed database schema. In an AI program, it includes the original source material and every derivative copy created during processing.
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- Documents, spreadsheets, presentations, PDFs, scans and images
- Email, chat, meeting transcripts, support tickets and collaboration pages
- Source code, notebooks, configuration files and technical diagrams
- Audio, video, photographs and other multimodal files
- Employee prompts, retrieved passages, embeddings and retrieval-augmented generation indexes
- Fine-tuning and preference data, evaluation sets, model outputs and conversation logs
- Plug-ins, API payloads, agent tool calls, caches and vendor-hosted copies
Write the scope so it follows the data flow rather than a particular product. A file can move from a repository to an index, into a prompt, through a hosted model, into a response and then into an application log. Each stage needs an owner, an allowed purpose, an access rule and a deletion rule.
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Set decision rights before technical controls
Name one accountable policy owner with authority to enforce the standard. Assign supporting responsibilities to data owners, information security, privacy, legal, records management, procurement, AI application owners and incident response. Document who may approve a new use case, who may grant an exception, how long an exception lasts and who can suspend a connector, model or application when risk becomes unacceptable.
Define the approval boundary clearly. A business team may propose using a repository for retrieval, but the data owner should confirm the classification and purpose, privacy and legal teams should assess obligations, security should review the architecture, and the application owner should demonstrate access tests. Incident-response authority must be able to disable a key or connector without waiting for a product team’s consensus.
Build a living inventory of unstructured data
An inventory is the foundation for every later decision. Start with repositories rather than attempting to label every sentence manually. Record enough metadata to answer who owns the content, why it exists, where it may be processed and what happens when it is deleted.
Required inventory fields
| Field | What to record | Why it matters |
|---|---|---|
| Repository and content type | Location, system, format and whether it contains documents, email, chat, code, media or other content | Identifies the systems that need connectors, scanning and access controls |
| Owner and steward | Business owner, technical steward and escalation contact | Creates accountability for classification, approvals and reviews |
| Business purpose | Why the content is collected and the approved AI use, such as search, summarization or drafting | Prevents a broad “AI use” permission from exceeding the original purpose |
| Sensitivity and jurisdiction | Classification, countries or regions involved and applicable legal or contractual restrictions | Determines model, tenant, region and transfer requirements |
| Retention and deletion rule | Required retention period, legal holds and the system that authorizes deletion | Allows deletion to propagate beyond the source repository |
| Provenance and rights basis | Creator, source, license, contract or other authority to use and transform the content | Supports copyright, confidentiality and data-subject rights decisions |
| Access group | Human groups, service identities and role conditions that may read the source | Provides the baseline for retrieval and export authorization |
| AI destinations | Prompting, retrieval, fine-tuning, evaluation, output storage and external sharing permissions | Turns classification into an enforceable allow or deny decision |
Make the inventory continuously updated. A nightly connector scan can detect new repositories and changes in permissions; owners should attest to purpose, classification and retention on a defined schedule. Treat a missing owner or unknown provenance as a control failure, not as permission to proceed.
Use a small classification scheme with strict defaults
Classifications are useful only when employees and automated controls can apply them consistently. A practical starting set is Public, Internal, Confidential, Restricted Personal, Regulated and Trade Secret. Map existing labels into this scheme instead of creating a separate AI taxonomy that employees must learn.
Set handling rules by class
| Class | Default AI handling | Required conditions |
|---|---|---|
| Public | Prompting, retrieval, evaluation and output storage may be allowed | Verify that the organization has permission to reuse the material and that the destination is approved |
| Internal | Allow only in approved enterprise tenants or gateways | Use access-aware retrieval, logging and the approved retention setting |
| Confidential | Deny by default for external services; allow only for documented use cases | Named owner, approved model and tenant, purpose limitation, least privilege and deletion plan |
| Restricted Personal | Deny by default | Privacy assessment, minimization or redaction, lawful purpose, region control and explicit approval |
| Regulated | Deny by default until obligations are mapped | Document sector, records, residency, access, audit and breach requirements before use |
| Trade Secret | Deny by default for third-party processing | Executive or designated owner approval, contractual protection, tightly scoped access and monitoring |
“Deny by default” means the connector, gateway or application blocks the flow until a specific model, tenant, region, purpose and retention period are approved. It does not mean that every sensitive use is permanently forbidden; it means the burden of proof belongs to the proposed use case.
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Require a provenance and rights check for third-party, licensed or copyrighted material. Record whether the organization may copy, index, transform, retain and disclose the content. If the rights basis cannot be established, exclude the material or obtain a documented legal decision before ingestion.
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Control the data flow from source to answer
Enforce least privilege at every layer
Repository permissions alone are not enough. Apply least privilege to source repositories, ingestion jobs, indexes, prompts, tools, outputs and administrative interfaces. Separate roles for ingestion, retrieval, administration and export so that a service that builds an index cannot automatically read every result or send data outside the approved boundary.
Retrieval must preserve source-level authorization. If an employee cannot open a document in the source system, the AI application should not reveal a passage from it through a search result, summary or generated answer. Recheck group membership at query time where practical, and define how quickly permission changes reach indexes and caches.
Log meaningful events
Logs should let an investigator reconstruct a transaction without storing more sensitive content than necessary. Capture the identity or service principal, source identifier, access decision, retrieved source references, model and vendor, purpose or application, output destination, export or sharing action, timestamp and policy version. Hashes, identifiers or redacted excerpts can often provide evidence while reducing the need to retain full prompts and responses.
Encrypt and delete all copies
Encrypt data in transit and at rest where appropriate, and manage keys separately from application credentials. Define deletion propagation across the source repository, extracted text, embeddings, indexes, caches, prompt stores, conversation history, backups, logs and vendor systems. A source deletion that leaves an embedding searchable is not a completed deletion.
Govern vendors and the AI supply chain
Generative AI introduces third-party software, models, data processors, plug-ins and infrastructure. NIST identifies third-party software, data and other supply-chain issues as governance concerns; its AI Resource Center states that policies should address risks and benefits arising from these dependencies.
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Minimum due-diligence questions
- Will prompts, files, outputs or telemetry be used to train or improve a shared model?
- Can the provider contractually prohibit secondary use and identify every subprocessor?
- Where are data, backups and support access located, and can the customer select a region?
- What retention, deletion and legal-hold controls are available, and how is deletion verified?
- Can the organization obtain access logs, security evidence and records of administrative access?
- How quickly must the provider notify the organization of a breach, misuse or compelled disclosure?
- Can the provider support export, investigation, suspension and connector revocation during an incident?
Put the answers in contracts and security schedules, not only in marketing material. Procurement should block production use until the service, model, region, subprocessor list, retention settings and evidence obligations are approved. Reassess vendors when the model, terms, data location or processing purpose changes.
Organize the program around Govern, Map, Measure and Manage
NIST’s AI RMF Core provides outcomes and actions that support dialogue and risk management. The functions are not a one-time project plan: governance runs across the lifecycle, while mapping, measurement and management repeat as data, models and use cases change.
Govern
- Approve the policy, classification scheme, roles, exception process and suspension authority.
- Define legal, privacy, records, security, contractual and rights obligations for each data class.
- Set minimum vendor terms and require evidence from application and infrastructure owners.
- Fund training, inventory maintenance, testing and incident response.
Map
- Describe the use case, affected people, data sources, model, tools, outputs and operating region.
- Identify foreseeable harms such as confidentiality loss, discrimination, inaccurate advice, rights violations, data poisoning or unsafe automation.
- Document threat scenarios, existing mitigations, residual risk, dependencies and the decision to approve, restrict or reject the use case.
Measure
- Test whether access boundaries prevent unauthorized retrieval and export.
- Use malicious-document and prompt-injection tests to examine whether content can override application instructions.
- Check for poisoning or tampering indicators, hallucination impact, deletion propagation and logging gaps.
- Retain test results, approvals, exceptions and remediation evidence with an owner and review date.
Manage
- Apply mitigations, monitor residual risk and close findings according to severity.
- Review exceptions on a defined cadence and expire them automatically unless renewed.
- Pause a connector, model or workflow when controls fail or the risk changes.
- Feed incidents, complaints and test results back into classification, architecture and training.
Use an implementation sequence that produces evidence
- Charter the program. Publish the accountable owner, decision rights, covered systems, approval thresholds and suspension authority.
- Draw the data-flow map. Include employee prompts, source repositories, ingestion jobs, indexes, models, tools, outputs, logs, backups and vendors.
- Inventory repositories. Populate owner, purpose, sensitivity, jurisdiction, retention, provenance, access group and permitted AI destinations.
- Apply classifications. Map existing labels to the six-class scheme, mark unknown content for review and configure deny-by-default handling for restricted classes.
- Approve use cases. For each application, record affected people, data classes, model and tenant, region, purpose, mitigations, residual risk and expiration or review date.
- Configure technical controls. Enforce source-aware retrieval, role separation, redaction or minimization, encryption, logging, export restrictions and deletion propagation.
- Assess vendors. Verify training and secondary-use restrictions, subprocessors, location, retention, deletion, breach notification and audit evidence before production.
- Test adversarially. Attempt unauthorized retrieval, prompt and retrieval leakage, malicious documents, poisoned data, unsafe outputs and incomplete deletion.
- Launch monitoring. Assign every metric to a control owner, evidence location, threshold and review date.
- Exercise response. Run a scenario involving a sensitive file disclosed in a response or sent to a vendor, then record timing, decisions and corrective actions.
Define reportable incidents and the response path
Report accidental disclosure of sensitive data, unauthorized retrieval, poisoned or tampered documents, model or vendor misuse, rights complaints and harmful outputs that create material risk. Do not limit incidents to confirmed breaches; a policy bypass or near miss can reveal a control that needs repair.
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- Preserve evidence: protect relevant logs, policy versions, source identifiers, prompts, outputs and vendor records, while limiting further exposure.
- Revoke and assess: remove affected access, identify the data classes and people involved, and determine whether copies remain in indexes, caches, logs or vendor systems.
- Notify: inform the data owner, security, privacy, legal, records and incident-response authorities according to the organization’s escalation rules.
- Require vendor action: obtain containment, deletion, access records and a written incident account when a provider processed the data.
- Recover and learn: restore only after testing, close the root cause, update the policy or control and document the lesson.
Measure whether the policy works
Metrics should show control coverage and failure, not merely the number of policy documents published. Assign each measure to an owner, evidence repository, threshold and review date.
| Metric | What it reveals | Useful evidence |
|---|---|---|
| Inventory coverage | Whether known repositories and AI data stores are represented | Connector scans, inventory records and unresolved-system queue |
| Owner and classification coverage | Whether content can be governed by an accountable person and a handling rule | Repository attestations and label reports |
| Sensitive-data blocking rate | Whether deny-by-default controls stop disallowed prompts, retrieval and exports | Gateway decisions, blocked-event samples and test results |
| Access-review completion | Whether source, index and administrative permissions remain appropriate | Review records, revocations and overdue approvals |
| Provenance and rights-review completion | Whether third-party and copyrighted content has a documented basis | Rights assessments, licenses and owner decisions |
| Deletion propagation success | Whether deletion reaches indexes, caches, logs and vendors | Deletion jobs, verification queries and provider confirmations |
| Vendor-assessment completion | Whether production services meet contractual and security requirements | Completed assessments, contracts and subprocessor reviews |
| Retrieval and export logging coverage | Whether investigators can reconstruct data movement | Log-integrity checks and sampled transactions |
| Time to detect and contain | How quickly the organization limits an exposure | Incident timestamps and exercise results |
| Open-exception count and age | Whether temporary risk acceptances are accumulating | Exception register, owners and expiration dates |
| Training completion | Whether people who create or use AI workflows understand the rules | Learning records and targeted assessments |
NIST AI RMF and ISO/IEC 42001: how to use each
NIST AI RMF 1.0 is a flexible, voluntary framework for managing AI risk. It is not limited to one sector or use case, which makes it useful for setting outcomes and designing controls across a varied portfolio. More than 240 organizations contributed to its development, according to the NIST AI Resource Center.
ISO/IEC 42001 is an AI-management-system standard and a useful comparison point when an organization needs a formal management-system structure, documented processes and audit readiness. NIST publishes a crosswalk connecting policy and data-resource activities to ISO clauses. Use the crosswalk to map existing controls rather than creating two unrelated programs.
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| Decision factor | NIST AI RMF | ISO/IEC 42001 comparison |
|---|---|---|
| Primary use | Flexible risk-management outcomes and actions | Management-system structure and conformity-oriented evidence |
| Scope | Enterprise or individual use case, depending on implementation | Defined management-system boundaries and organizational processes |
| Unstructured-data detail | Requires the organization to specify inventory, provenance, access, retention and flow controls | Can provide a formal system context when mapped through the published crosswalk |
| Measurement | Risk, testing and monitoring outcomes selected for the use case | Documented objectives, processes, records and continual improvement evidence |
| Implementation effort | Adaptable; effort varies with risk and control maturity | Typically greater process and evidence discipline, especially for audit preparation |
Choose the framework combination that matches your objective. A small team can use the AI RMF functions to establish practical controls immediately, while a larger organization can map those controls to ISO/IEC 42001 requirements when formal management-system assurance is needed.
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“We banned public chatbots, so the data is safe.”
Employees can still paste content into approved tools, build unsanctioned connectors or export generated answers. Replace a product-only ban with data-flow inventory, classification, gateway enforcement, training and monitoring.
“The repository already has permissions.”
An index, cache or service identity can bypass the source user interface. Test authorization at retrieval time, separate ingestion and export roles, and verify that permission changes propagate.
“The vendor says it is secure.”
Security marketing does not establish training restrictions, region, subprocessor handling, deletion or audit rights. Put those requirements in contracts and retain evidence.
“Deleting the original file completed the deletion.”
Extracted text, embeddings, caches, logs and vendor copies may remain searchable. Run deletion verification across every derivative store and record the result.
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“A policy document proves compliance.”
A policy without inventory coverage, blocked-event samples, access reviews, tests, incident exercises and exception records cannot demonstrate operation. Tie each requirement to an owner and evidence.
Policy language you can adapt
“The organization shall inventory unstructured data and AI derivatives, assign an owner, classification, provenance and retention rule, and record whether each class is permitted for prompting, retrieval, fine-tuning, evaluation, output storage or external sharing.”
“Restricted personal, regulated and trade-secret data shall be denied to generative-AI processing by default. A documented approval must specify the model, tenant, region, purpose, access scope, retention period, mitigations and expiration date.”
“AI applications shall enforce source-aware authorization, least privilege, logging of retrieval and export events, encryption appropriate to the risk, and deletion propagation across source, index, cache, log and vendor systems.”
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“The organization shall test leakage, malicious documents, poisoning indicators, harmful outputs and deletion controls; maintain evidence; review exceptions; and suspend a use case when required controls fail.”
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