The Tool Desk
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Start with a defined use and an accountable owner
Do not approve a tool simply because it is available or performs well in a demonstration. First document what the system will do, who will use it, and where its outputs will go. A generative AI assistant used to draft internal notes presents different risks from one whose output informs decisions about customers, employees, or other people.
For each proposed deployment, record:
- Intended and prohibited uses: Describe the tasks the system may support and any decisions or activities it must not perform.
- Users and affected people: Identify who will interact with the system and who could be affected by its outputs.
- Business owner and decision-makers: Name the person responsible for the deployment, the people who approve it, and the person or team authorized to pause it.
- Risk tolerance and escalation: Define which errors or harms are unacceptable, how concerns are raised, and who resolves them.
Place the system within existing enterprise risk processes where those processes are adequate; revise the organization’s risk tiering if generative AI creates risks they do not capture. The NIST AI Risk Management Framework (AI RMF) organizes risk work into Govern, Map, Measure, and Manage, and its Generative AI Profile discusses governance across the AI value chain. Both are voluntary guidance, not a certification or a guarantee of safety.
Test the system in the setting where it will be used
Require evidence from testing before release, not just a vendor demonstration or a general claim that a model is capable. NIST’s Generative AI Profile identifies pre-deployment testing as a primary consideration, but it does not prescribe a single test suite that fits every business.
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Build evaluations around the proposed task and likely failure conditions. Depending on the use, test representative inputs, ambiguous requests, edge cases, and situations where an incorrect or unsupported answer could cause harm. Decide in advance who reviews results, what evidence is sufficient for approval, and which results block deployment. The more consequential an error could be, the more demanding the evaluation and release criteria should be.
Document the system and configuration tested, the tasks and conditions covered, the results, known limitations, and the approval decision. Revisit that evidence if the model, provider, integration, data, users, or purpose changes.
Make human review meaningful
Set rules for when a qualified person must check an output, how that person can correct or reject it, and when a case must be escalated. A nominal sign-off is not a meaningful safeguard if the reviewer lacks time, authority, relevant context, or a practical way to challenge the system.
Assign review based on the consequences of the output. For example, a business may allow lighter review for low-impact internal drafting while requiring stronger review before an output is sent externally or used to inform a consequential decision. Specify who owns the final decision; do not leave responsibility ambiguous between the user, provider, and business.
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NIST states in its 2024 Generative AI Profile: “Organizations’ use of GAI systems may also warrant additional human review, tracking and documentation, and greater management oversight.” Translate that principle into named reviewers, decision rights, records, and an escalation route.
Review privacy, security, and data handling
Before users enter business information, establish what information the system may receive, where it is processed, and what the provider retains or uses. Confirm how access, retention, and deletion work for the service and any connected tools. The specific settings and controls depend on the business’s data classification, architecture, contract terms, and applicable obligations; there is no universal retention setting established for every deployment.
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Assess the system, integrations, credentials, and handling of outputs as part of the security review. Consider confidentiality, integrity, and availability: whether information could be exposed, altered or used in an unintended way, or whether disruption could affect business operations. The AI RMF also treats privacy and secure, resilient operation as trustworthiness concerns.
Set practical rules for users, including which information they may submit, how outputs may be stored or shared, and where to report a suspected exposure. Align those rules with existing access controls and data-handling policies rather than assuming a provider’s default settings meet the organization’s needs.
Assess providers, provenance, and generated content
Keep a record of the model and service dependencies, data sources where known, relevant provider commitments, and terms for change or incident notification. This creates a basis for assessing third-party risk and understanding what may have changed when a service is updated. NIST’s Generative AI Profile addresses third-party governance and data provenance.
Decide whether outputs need labeling, provenance records, or additional review before internal or external use. The answer depends on the use and audience; define the rule rather than assuming that generated material will always be obvious to readers. NIST identifies content provenance as a primary consideration in its profile.
Compare deployment options using the same evidence
When evaluating more than one provider, model, or deployment approach, use consistent criteria tied to the intended task. The following comparison framework reflects NIST risk and trustworthiness themes; it is a practical decision aid, not a NIST-published scoring rubric.
| Decision area | Evidence to compare |
|---|---|
| Task fit and consequence of error | How well the option supports the defined task, and what harm or business impact an incorrect output could cause. |
| Evaluation evidence | Results from testing relevant to the business’s actual use, including the failure conditions that matter for that use. |
| Human oversight | Whether qualified reviewers can inspect outputs, intervene, and escalate issues in the proposed workflow. |
| Data handling and security | How information is processed, retained, accessed, protected, and handled across connected services. |
| Provider transparency and commitments | Available information about dependencies and data provenance, plus terms for changes and incident notifications. |
| Operational control | Whether the business can monitor changes, respond to problems, and discontinue the deployment if needed. |
Plan for incidents and reassessment
Before launch, define how users report harmful, incorrect, or exposed information; who triages reports; what conditions trigger a pause; and how corrective actions are recorded. Include provider notification arrangements in the incident plan where relevant. NIST’s Generative AI Profile identifies incident disclosure among its primary considerations.
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Risk management continues after approval. Reassess the deployment when its model, provider, integration, data, user population, or purpose changes, and when incident reports or monitoring show that the original assumptions no longer hold. NIST’s AI RMF treats risk management as a lifecycle activity, from design and development through deployment, use, and evaluation.
Apply the framework without mistaking it for legal approval
NIST’s AI RMF and Generative AI Profile can help organize governance and risk decisions, but they do not determine whether a particular deployment complies with law. Legal duties vary by jurisdiction, sector, data, and use. Identify the rules that apply to the specific deployment and involve appropriate legal, privacy, security, and operational specialists before approving it.
NIST published the Generative AI Profile, AI 600-1, on July 26, 2024. As of October 2026, NIST reports that AI RMF 1.0 is under revision, so organizations relying on the framework should verify its current status and any applicable requirements when making deployment decisions.
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