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NIST’s Generative AI Profile and AI Risk Management Framework provide a useful cross-sector structure, but they are voluntary resources rather than a private-sector legal mandate. Use them to build and document a control program, then add requirements from your industry, contracts and jurisdictions.
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Start with a written governance decision
Do not begin with a model subscription or an employee prompt guide. First decide who is accountable for the system and what the organization is willing to permit. NIST describes governance, context mapping, measurement and management as connected activities across an AI system’s lifecycle. Its AI Risk Management Framework is intended for voluntary use, so adapt it to your own risk profile and obligations (NIST’s Generative AI Profile publication page; NIST AI RMF Core).
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Assign a business owner who is responsible for the intended outcome, a technical owner for configuration and integration, a security or privacy owner for protection controls, and a records or compliance owner where retention and disclosure rules apply. Give each owner authority to pause use, reject a proposed integration and require remediation. Record an escalation route for suspected data exposure, harmful output, security events and unexpected model behavior.
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Define the operating boundaries
Write down the approved purpose, users, environments and data classes. Separate:
- Approved uses: tasks such as drafting, summarizing or classification that have a named owner and review process.
- Restricted uses: activities requiring additional approval, such as processing confidential customer records, employment information, regulated data or material used in consequential decisions.
- Prohibited uses: processing that the organization cannot lawfully or safely support, attempts to bypass security controls, or decisions made solely from unverified generated content.
Document the risk tolerance, applicable contracts and legal requirements, and the conditions that trigger a new review. A change in model, connected tool, data source, user population or purpose should not silently remain under an old approval.
Map the data, model and surrounding system
Make an inventory before enabling access. The system is more than a chat interface: it can include a foundation model, fine-tuning data, retrieval indexes, prompts, plugins, application code, logging systems and external services. NIST’s profile calls for attention to data governance, provenance, privacy, intellectual property and third-party risks (NIST AI 600-1, Generative AI Profile).
Record each data flow
| Inventory item | Questions to answer | Evidence to retain |
|---|---|---|
| Inputs | What can users submit? Are prompts, uploaded files, images, audio or retrieved records included? | Data-flow diagram, field list and classification rules |
| Outputs | Where are responses stored, displayed, exported or sent to another system? | Output destinations, permissions and review requirements |
| Model and application | Which model version, hosting environment, application components and safety settings are used? | Configuration snapshot, version history and architecture record |
| Connected tools | Can the system call search, databases, code execution, email, business applications or other APIs? | Integration list, scopes, credentials and network boundaries |
| Third parties | Who receives prompts, files, telemetry, support data or fine-tuning material? | Provider list, data-processing terms and subprocessors |
| Context and impacts | Who may be affected, and what could happen if the system is wrong, biased, unavailable or misused? | Use-case assessment, affected-party analysis and risk rating |
Capture provenance and permission
For every dataset or document source, record where it came from, the owner, the permitted purpose, applicable license or confidentiality restriction, and the date it was approved. Do not assume that information visible to an employee is automatically suitable for model input or training. Check whether the organization has permission to copy, transform, retain or disclose it to a provider. Include retrieved content and user-uploaded files, not only the data used to train or fine-tune a model.
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Protect information during use
Data protection must cover the prompt, the model’s intermediate handling where known, logs, outputs and backups. Do not leave these decisions to a provider’s default settings.
Set input and access controls
- Publish a data-classification rule that states which categories may be entered, which require approval and which are never permitted.
- Use single sign-on, multi-factor authentication and role-based access where available. Give users the minimum model, connector and export permissions needed for their task.
- Separate development, testing and production environments. Keep real sensitive records out of experimentation unless the use is approved and protected.
- Restrict high-risk connectors by role and scope. A summarization assistant does not automatically need write access to a customer database or the ability to send email.
- Log access and administrative changes in a way that supports investigation without creating an unnecessary second copy of sensitive content.
Minimize and transform data
Use the smallest amount of information that can accomplish the task. Remove names, account numbers, direct identifiers and irrelevant fields; use aggregation, masking, tokenization or synthetic examples when the task permits. Limit retrieval results and attachment size. Treat re-identification risk as part of the design review: removing a name does not make a record anonymous if other fields identify the person.
Decide retention and deletion explicitly
There is no single correct retention period. Determine how long prompts, files, outputs, logs, evaluation data and backups must be kept based on the purpose, applicable requirements, contractual commitments and system architecture. Confirm whether deletion propagates to provider systems, indexes, caches, backups and human support records, and how the organization can verify it. Define a process for legal holds, correction requests and secure disposal.
Control provider use of data
Before launch, establish whether the provider uses prompts, outputs, telemetry or uploaded content to operate, improve or train a service, and whether an opt-out or separate processing mode exists. Check where data is stored and accessed, how subprocessors are governed, and what happens when the account is closed. Keep the approved configuration and the provider’s relevant terms with the system record; a marketing statement is not a substitute for enforceable terms.
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Handle outputs as untrusted information
Generated text, code, images and decisions can contain errors, confidential material or copied content. Require human review before publication, external communication, code deployment, financial action, medical or safety action, or any decision affecting a person. Label or otherwise track AI-assisted material when your policy or a contract requires it. Preserve the source context and reviewer decision for outputs that become part of an official record.
Perform provider and integration due diligence
Procurement should evaluate the complete service, not only a model’s advertised capability. NIST lists due diligence, software bills of materials, service-level agreements and SSAE reports as possible third-party risk controls; these are examples to select according to your exposure, not universal requirements (NIST AI 600-1).
Questions for a provider
- What data is collected from prompts, files, outputs, account metadata and support interactions?
- Is customer content used for training or service improvement, and what settings or contract terms control that use?
- What security measures protect data in transit, at rest, in logs and in backups?
- Where are processing and support operations located, and which subprocessors can access content?
- What are the incident-notification, vulnerability-disclosure, uptime, support and data-return obligations?
- Can the provider delete content on request and provide evidence or a defined deletion process?
- How are model, safety-policy, feature and subprocessor changes announced and assessed?
- What independent assurance, audit rights, penetration-test summaries or relevant compliance reports are available?
Match controls to the service type
| Service arrangement | Distinct governance concern | Control emphasis |
|---|---|---|
| Hosted foundation model | Provider controls the base model and may change versions or usage terms. | Prompt and output handling, version notices, isolation, access scopes and exit plan |
| Fine-tuned or retrieval-augmented model | Your proprietary data can influence responses or be exposed through retrieval. | Training-data permission, index isolation, update approval, deletion propagation and leakage testing |
| Embedded AI feature | AI is built into an existing business application and may inherit broad permissions. | Application roles, connector scopes, audit logs, user notices and vendor responsibility boundaries |
Require security, privacy, intellectual-property and records specialists to review the proposed terms when the data or impact warrants it. Approve integrations separately: a trusted model does not make every plugin, browser extension or external API trustworthy.
Give employees concrete acceptable-use rules
A policy that merely says “use AI responsibly” leaves users to guess. Provide an approved-services list and examples tied to the organization’s classifications.
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Minimum user rules
- Use only organization-approved accounts, models and connectors. Do not paste work information into a personal account.
- Check the classification of every prompt, attachment and retrieved record before submission.
- Do not request or infer sensitive personal information without an approved purpose and access authority.
- Verify facts, calculations, citations, code, permissions and generated recommendations before relying on them.
- Do not represent unreviewed output as a human decision, an original source or professional advice.
- Protect generated content at the same classification as the most sensitive material it contains.
- Report suspected disclosure, malicious prompt injection, unsafe output, copyright concern or account compromise through the stated escalation route.
Train and authorize users
Training should cover proper use, protection, dissemination and disposition of information, as well as the system’s limits and the organization’s reporting process. Give extra instruction to administrators, developers, reviewers and people whose work affects customers, employees, safety or regulated decisions. Revoke access when a role changes or the person leaves.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test before enabling production access
Pre-deployment evaluation should reflect the intended real-world context. NIST’s AI RMF Core states: “AI systems should be tested before their deployment and regularly while in operation.” Read the guidance in its lifecycle context (NIST AI RMF Core).
Build a documented test plan
- Capability: Does the system perform the approved task at an acceptable level on representative data?
- Limitations: Does it fail on language, format, domain, recency, long context or ambiguous instructions that users will encounter?
- Safety and security: Can prompt injection, data exfiltration, privilege misuse or unsafe instructions bypass controls?
- Privacy and confidentiality: Does the system reveal information from another user, tenant, retrieval source or conversation?
- Fairness and impact: Are error rates or harmful effects concentrated for relevant groups or use contexts?
- Human oversight: Can a reviewer understand the output, verify it and override the system before harm occurs?
Use representative, permissioned test data and define pass/fail criteria before reviewing results. Keep the model version, prompts, test set, configuration, evaluator qualifications, results, unresolved issues and approval decision. NIST cautions that anecdotes, human tests or jailbreak exercises alone do not establish validity or reliability; combine them with repeatable, context-relevant evaluation.
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Gate the launch
Set a written go/no-go decision. A failed control should result in remediation, narrower scope, additional human review or rejection—not an informal exception. Record who accepted any residual risk, its expiry or review date, and the condition that would suspend the system.
Monitor, respond and reassess after launch
Deployment is the beginning of operational governance. Monitor the system and its surrounding process for changes in data, behavior, use and impact.
Monitor useful signals
- Access, prompt-volume and export anomalies, including use outside approved roles or geography.
- Data-loss-prevention alerts, secrets in prompts, unexpected retrieval results and connector activity.
- Accuracy, refusal, hallucination, bias and escalation trends measured against the approved task.
- User complaints, reviewer overrides, harmful outputs, security findings and provider incidents.
- Model, application, policy, subprocessor, data-source and dependency changes.
Set thresholds and owners for investigation. Monitoring records should be proportionate: retain enough evidence to investigate and demonstrate control operation without collecting unnecessary sensitive content.
Define incident response
Provide a single reporting route and triage categories such as unauthorized disclosure, account compromise, malicious prompt injection, unsafe advice, discriminatory output and intellectual-property concern. The response plan should identify how to disable access or a connector, preserve evidence, notify affected parties and regulators where required, correct or delete exposed data, and restore service safely. Run exercises for the scenarios most plausible in your environment.
Use change management
Reassess when the model, prompt templates, retrieval corpus, permissions, provider, connector, user population or intended purpose changes. Repeat testing after material changes and at a scheduled interval appropriate to the risk. A system that was acceptable for internal drafting may need a new assessment before it supports customer communications or consequential decisions.
A practical approval sequence
Organizations can turn the controls into an approval workflow:
- Describe the use case: purpose, users, affected people, decisions and success criteria.
- Classify the information: inputs, outputs, logs, retrieval data, training material and backups.
- Map the architecture: model, application, connectors, providers, locations and permissions.
- Set boundaries: approved, restricted and prohibited uses; human-review and escalation requirements.
- Configure protection: identity, least privilege, minimization, retention, deletion and provider data-use settings.
- Complete due diligence: contracts, subprocessors, security evidence, service commitments and exit arrangements.
- Train and test: user instruction, representative evaluation, adversarial checks and documented pass/fail criteria.
- Approve and launch narrowly: record residual risk, owners and a review date before production access.
- Monitor and improve: review signals, incidents, user feedback and changes; suspend or redesign when controls no longer fit.
Common control failures and their fixes
| Failure | Why it matters | Fix |
|---|---|---|
| Employees choose unapproved tools | Security, retention and provider terms are unknown. | Publish approved services, block or monitor unsanctioned access where appropriate, and offer a usable approved alternative. |
| A broad “no confidential data” rule | Users cannot tell which fields are allowed, so they either ignore the rule or avoid useful work. | Give classification examples, transformation methods and an approval path for exceptions. |
| Default administrator permissions | A connector or model can expose more data or take actions than the task requires. | Apply least privilege, separate read and write scopes, and review permissions periodically. |
| Testing only a few impressive prompts | Success anecdotes do not show reliability, privacy or resistance to misuse. | Use representative data, defined metrics, adverse cases and repeatable documentation. |
| No deletion or exit plan | Data can remain in provider systems, indexes, backups or dependent applications after the project ends. | Specify deletion, return, verification and migration steps in the contract and system design. |
| One-time approval | Models, providers, data and real-world use evolve. | Trigger reassessment on material change and schedule ongoing monitoring and review. |
Keep an auditable record
For each approved deployment, retain the use-case description, risk assessment, data-flow map, classification and provenance record, provider review, contract terms, configuration, access list, training completion, test results, approval, residual-risk acceptance, monitoring plan, incidents and change history. This evidence lets the organization explain what it allowed, why it allowed it, who reviewed it and how it responded when conditions changed.
U.S. federal agency provisions cited in Title 15 address access limits based on risk, appropriate-use rules, workforce training, vendor terms, record keeping, cybersecurity, confidentiality, privacy and data protection. Those provisions govern the federal context; they should not be presented as a universal rule for private organizations (United States Code, Title 15, Chapter 119). Organizations elsewhere should identify their own statutory, regulatory, contractual and labor requirements.
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