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Check the AI service in the configuration you will actually use—not just the model name. Before entering sensitive information or connecting a tool, map the data it receives and sends, verify the provider’s current terms and controls for your service tier, assess security across the whole system, and test it against representative tasks. Then document whether the remaining risks are acceptable for your use. No model has a universal safe-or-unsafe label: the answer depends on the provider, service configuration, data, integrations, users, and consequences of failure.
1. Define the use and what could go wrong
Start by describing the proposed use in plain language. A model used to summarize public documents presents different risks from one given employee records, connected to internal systems, or used to influence decisions about people. Assess the actual system and context, not the model in isolation; NIST says the importance and trade-offs among trustworthiness characteristics depend on context. NIST AI Risks and Trustworthiness
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- Users and affected people: Who will enter information, use the outputs, or be affected by decisions based on them?
- Task and setting: What work will the AI do, and will it be used directly by people, embedded in a product, or connected to other systems?
- Data classes: Will it handle public material, confidential business information, personal data, credentials, health or financial information, or other sensitive content?
- Consequences: What would happen if data were exposed, an output were wrong, the service were unavailable, or a connected action were triggered improperly?
Use those answers to set boundaries before testing: what information is prohibited, which tasks need human review, and what level of failure would make the deployment unacceptable.
2. Trace the data from entry to deletion
Follow every route by which information enters or leaves the system. A prompt is only one possible input: files, connected applications, tool calls, usage telemetry, and generated outputs may also be collected or processed. Ask the provider or system owner for specific answers, preferably in writing.
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- What is collected from prompts, attachments, connected tools, telemetry, and outputs?
- For what purposes and how long are inputs and outputs retained? Does the answer differ by account or service tier?
- Can submitted data or generated outputs be used to train or improve models? Which controls let users or administrators opt out, and do they cover every product and data route in this workflow?
- Who can access or process the information—including employees, contractors, subprocessors, and integrations—and in which locations?
- What does deletion remove: data in active systems, logs, backups, and downstream processors? Who can request it?
- Could an output reveal personal information through inference or re-identification, even if that information was not explicitly entered?
Decide whether you can reduce exposure by omitting fields, using less sensitive examples, de-identifying or aggregating data, or applying an appropriate privacy-enhancing technique. These measures do not replace checking the service’s data handling. NIST identifies privacy concerns including re-identification, inference about people, and amplified behavioral tracking or surveillance. NIST Cybersecurity, Privacy, and AI
Third-party generative AI integrations can introduce privacy, intellectual-property, and information-security risks. NIST’s Generative AI Profile calls for clear guidance on transparency and management of third-party data collected and used as model inputs. That is a reason to verify the particular service and configuration; it does not establish how any one provider currently handles data. NIST AI 600-1, Generative AI Profile
3. Verify the provider’s terms and controls for your exact service
Read the current privacy notice, service terms, security documentation, and administrator settings that apply to the product and tier you intend to deploy. Do not assume that a consumer account, organizational plan, API, or separately integrated product has the same retention, training, deletion, or access rules as another tier. Record the date and version of the terms you reviewed, who verified the settings, and any unanswered questions.
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Check that written commitments match the controls you can actually configure. For example, confirm whether an opt-out applies to prompts and files as well as other data, whether administrators can restrict connected tools, and whether deletion procedures cover downstream processors. If a control or commitment is unclear, treat it as unresolved rather than inferring a stronger protection.
4. Assess security across the whole service
Review the provider, deployment, underlying software and hardware, data, and integrations—not only the model. NIST frames conventional AI security around confidentiality, integrity, and availability: protecting information from unauthorized disclosure, preserving the accuracy and trustworthiness of systems and data, and keeping the service usable when needed. NIST AI Research: Security and Resilience
- Access and data protection: Ask how access is controlled, how data is isolated, and how encryption and key management are handled.
- Visibility and response: Check logging, data export, vulnerability handling, incident notification and response, and the division of security responsibilities between provider and deployer.
- Availability and recovery: Understand service availability commitments, dependencies, and what happens to your workflow if the service is interrupted.
- Integration exposure: Review what connected tools can read or change, what information they send onward, and how their permissions are limited.
- AI-specific threats: Consider whether inputs could manipulate behavior, whether a model could be extracted, or whether outputs could reveal whether particular information was in training data.
NIST identifies evasion, model extraction, membership inference, availability, and broader machine-learning attacks as areas where existing guidance is incomplete. A conventional security review is important, but it should not be treated as proof that AI-specific risks have been eliminated. NIST AI Research: Security and Resilience
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For organizational procurement or AI embedded in a product, consider asking for documentation such as a software bill of materials, service-level agreement, or attestation report where relevant. NIST presents these as possible due-diligence and transparency measures, not universal guarantees or documents every provider will supply. NIST AI 600-1, Generative AI Profile
5. Test the system in its intended context
Evaluate the configured service against the tasks, users, data, languages, tools, and failure conditions it will encounter. Keep a record of the test method, results, limitations, and the person responsible for reviewing them. Include ordinary cases and plausible edge cases—for example, ambiguous instructions, incomplete documents, unauthorized requests, or an unavailable integration—when those situations fit your use.
Do not treat a benchmark, a standardized exam, or a handful of jailbreak prompts as proof of real-world safety or reliability. NIST cautions that games and human-designed exams may not be valid for a particular domain, anecdotal prompt attacks may not systematically measure validity or reliability, and laboratory or benchmark conditions may not reflect deployment. Involve people who understand the intended use and state plainly what the test did not cover. NIST AI 600-1, Generative AI Profile
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6. Compare actual options on the same evidence
If you are choosing between services or configurations, use the same questions for each and compare the evidence available—not just marketing claims or model names. Weight each area according to data sensitivity and the consequences of failure.
| Review area | Evidence to compare | Decision question |
|---|---|---|
| Data flows | Inputs, outputs, telemetry, connected tools, subprocessors, and processing locations | Can you account for where information goes and who handles it? |
| Retention and use | Retention periods, deletion scope, training or improvement use, and opt-out controls | Are the terms and controls acceptable for your data and service tier? |
| Security and resilience | Access controls, encryption, isolation, incident handling, availability, and integration permissions | Are protections and responsibilities clear for the whole workflow? |
| Transparency | Terms, technical documentation, attestations, service commitments, and stated limitations | Can you verify the claims that matter to this deployment? |
| Evaluation | Test methods and results for representative tasks, users, data, and failure conditions | Does the evidence address your actual use rather than a generic benchmark? |
| Operational fit | Available controls, accountable owners, monitoring, and ability to change or stop use | Can you manage the system throughout its use, not only at procurement? |
There is no universal risk score or breach-probability figure established for an arbitrary AI model by the NIST sources cited here. Avoid turning a generic percentage into a prediction for your deployment; record the specific evidence and uncertainties behind your decision instead. NIST AI Research: Security and Resilience NIST Cybersecurity, Privacy, and AI
7. Make the decision, assign ownership, and revisit it
Document the intended use, data categories, approved configuration, evidence reviewed, unresolved risks, required controls, and the person accountable for the decision. Set clear conditions for approval, such as prohibiting specified data, limiting integrations, requiring human review, or restricting access. If a material risk cannot be reduced to an acceptable level, do not use the system for that task.
Assign an owner to monitor the deployment and define when a reassessment is required—for example, a change to the model, provider terms, data, service tier, connected tools, or deployment setting. Maintain an incident response plan and a way to suspend or discontinue use if assumptions stop holding.
NIST’s AI Risk Management Framework is voluntary guidance organized around governing, mapping, measuring, and managing risk; it is not a product certification. NIST says AI RMF 1.0 is being revised, and its Generative AI Profile was published July 26, 2024. Use these materials to structure local decisions, then verify current provider terms and applicable requirements for your deployment. NIST AI Risk Management Framework NIST AI 600-1, Generative AI Profile
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