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Before deploying generative AI, require a documented use case and risk owner, testing in the intended setting, meaningful human oversight, privacy and security review, vendor and data-provenance checks, and a plan for incidents and ongoing reassessment. The controls should match the system, the people affected, and the consequences of errors—not just the model’s advertised capabilities.
Set the rules for the proposed use
Start with an inventory entry for the system, its provider, integrations, and business owner. Describe what the system is meant to do, who will use or be affected by it, and what it must not be used for. Name the people accountable for approving deployment and handling concerns, set the organization’s risk tolerance, and specify an escalation path.
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Fit the system into existing enterprise risk processes, adjusting risk tiers where generative AI creates risks those processes do not capture. Consider dependencies across the AI value chain, not just the interface employees see. NIST’s AI Risk Management Framework (AI RMF) organizes risk work around Govern, Map, Measure, and Manage; its trustworthiness considerations apply across design, development, deployment, use, and evaluation. The framework is voluntary guidance, not a legal compliance determination.
Test it in the context where it will be used
Before release, evaluate the system on representative work tasks and plausible failure conditions in the intended setting. A general-purpose demonstration is not evidence that the system is suitable for a particular workflow. Decide in advance what evidence is sufficient, who will review it, and which results require changes or block deployment. Scale the depth of testing to the potential harm and the consequences of an error.
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NIST’s Generative AI Profile identifies pre-deployment testing as a primary consideration, but it does not prescribe one test suite that fits every business. The organization must define tests for its own use case, including how it will recognize unacceptable outputs and what happens when the system cannot be relied on.
Make human oversight real
Specify which outputs require review by a qualified person, how that person can correct or reject them, and when they must escalate a case. A sign-off is not meaningful protection if the reviewer lacks time, authority, or enough context to assess the output. For higher-consequence uses, define who can stop or override the workflow.
NIST notes: “Organizations’ use of GAI systems may also warrant additional human review, tracking and documentation, and greater management oversight.” The business should translate that into assigned responsibilities and records appropriate to the use, rather than relying on a general instruction to “check the AI.”
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Review data handling, privacy, and security
Document what information users may enter, where it is processed, what the provider retains or uses, and how access, retention, and deletion are handled. Check that the arrangements fit the organization’s data classification, system architecture, contractual terms, and applicable obligations. The evidence here does not establish a universal retention setting or technical control set.
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Review the service and its integrations for confidentiality, integrity, and availability risks involving both systems and data. Include credentials, access permissions, connected applications, and the handling of generated output in the security review. Consider privacy and secure, resilient operation as part of the overall risk assessment, not as a substitute for evaluating the specific data flows.
Assess providers, dependencies, and generated content
Record model and service dependencies, data sources where known, relevant provider commitments, and terms for change and incident notification. Ask what the provider will communicate if the service, model, or relevant controls change, and how the business will assess that change before continued use.
Decide whether generated content needs labeling, provenance records, or review before it is shared externally. NIST’s Generative AI Profile treats third-party governance, data provenance, and content provenance as relevant considerations. Requirements should reflect the content’s audience and use; do not assume that a label alone verifies accuracy or origin.
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Prepare for incidents and changes
Define how users report harmful or incorrect output, exposed information, or other problems; who triages reports; when use is paused; and how corrective action is documented. Include a route for notifying relevant internal decision-makers and, where applicable, affected parties under the organization’s obligations.
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Reassess the deployment when the model, provider, integration, data, user population, or intended use changes. Also set a review cadence suited to the risk and establish how the organization can restrict or discontinue use if the system no longer meets its requirements. NIST identifies incident disclosure as a generative AI consideration and frames risk management as a lifecycle activity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare deployment options against the same evidence
Use consistent criteria when choosing between systems or deployment approaches. The following comparison axes are derived from NIST’s risk and trustworthiness themes; they are not a NIST-published scoring rubric.
| Criterion | What to establish |
|---|---|
| Task fit and consequence of error | Whether the option suits the intended task, who may be affected, and what could follow from an incorrect or unsuitable output. |
| Evaluation evidence | Results from testing in the business’s actual use context, including failure conditions and the organization’s release criteria. |
| Human review and escalation | Who can review, correct, reject, or escalate outputs, and whether the workflow gives them the authority and context to do so. |
| Data handling, privacy, and security | Information flows, provider retention and use, access controls, integrations, and relevant confidentiality, integrity, and availability risks. |
| Provider transparency and provenance | Known model and data dependencies, provider commitments, content provenance needs, and change or incident notification terms. |
| Monitoring and exit capability | How changes and incidents will be tracked, when the organization will reassess, and whether it can restrict or stop use. |
Use a release gate, not a one-time approval
Before authorizing use, confirm that the intended and prohibited uses are documented; the risk owner and escalation route are named; testing meets the agreed release criteria; reviewers can meaningfully intervene; data and security arrangements have been assessed; provider dependencies and content provenance needs are recorded; and incident, monitoring, reassessment, and discontinuation responsibilities are assigned.
NIST reports that AI RMF 1.0 is under revision. Because the framework is voluntary and legal duties vary by jurisdiction, sector, data, and use, verify its current status and determine applicable obligations for the specific deployment before relying on it as a governance reference.
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