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Assess the AI system in the setting where you will actually use it—not just the model name. Check the provider, service tier, configuration, data flows, connected tools, users and consequences of failure. Then document what you verified, test representative tasks and set controls before entering sensitive information or relying on the system.
Start by defining the use and its stakes
Before reviewing a provider, write down what the AI will do and what could go wrong. The relevant unit of review is the full system in context: model, service, settings, integrations, data and people affected. NIST’s AI Risk Management Framework (AI RMF) treats trustworthiness as context-dependent, not as a single label that can be assigned to a model in isolation.
- Users and purpose: Who will use the system, for which tasks, and whether its outputs will be reviewed by a person.
- Data: What prompts, files, personal information, confidential material or connected-app data may enter or leave the system.
- Deployment: Which service tier and settings are in use, where it is deployed, and what tools or other services it can access.
- Consequences: What harm could follow from exposure, an incorrect output, an unavailable service or an action taken by an integration.
This scoping step helps you decide which risks matter most and what evidence or safeguards are necessary. NIST describes the AI RMF as voluntary guidance for managing AI risks; its landing page says the framework is being revised. It is a way to organize a review, not a product certification. See the NIST AI Risk Management Framework.
Trace what data enters, leaves and may be retained
Map the data path across prompts, attachments, outputs, usage telemetry, connected apps and any human review or support process. A privacy review should cover the complete service configuration, not only the text typed into a chat box. NIST’s Generative AI Profile warns that third-party integrations can increase privacy, intellectual-property and information-security risks, and calls for clear transparency and risk management around third-party data used as model input.
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Questions to ask the provider or system owner
- What information is collected from prompts, files, outputs, connected tools and usage telemetry?
- Are inputs or outputs retained? For what purposes, for how long, and under which account or service tier?
- Can data be used to train or improve models? If an opt-out exists, which data routes and products does it cover?
- Can users or administrators delete data? Does deletion include active systems, logs, backups and downstream processors?
- Which employees, contractors, subprocessors or integrations can access or process the data, and in what locations?
- Could the system reveal personal or sensitive information through inference or re-identification? Would data minimization, de-identification, aggregation or another privacy-enhancing technique reduce the exposure?
Check the current terms and settings for the exact service tier and configuration you plan to use. Do not assume that a control or promise for one product or account type applies to another. NIST’s Generative AI Profile (NIST AI 600-1, published July 26, 2024) gives general due-diligence guidance; it does not establish the current practices of a particular provider.
Review security across the whole service
Assess confidentiality, integrity and availability: whether data is protected from unauthorized access, whether systems and outputs can be altered or manipulated, and whether the service can remain available when needed. Include the provider’s service, the underlying software and hardware, training and output data, and the systems your organization connects to it.
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Check controls and responsibilities
- How are access and administrative privileges controlled? What logging is available to help investigate use or incidents?
- What encryption and key-management controls are documented, and which responsibilities belong to the provider versus the deployer?
- How are data and integrations isolated? Can connected tools access more information or perform more actions than the task requires?
- What processes address vulnerabilities, security incidents, data export and service availability?
- For procurement or embedded deployments, would software bills of materials, service-level agreements or attestation reports help establish transparency and responsibilities?
Those documents can support due diligence when appropriate; their existence does not by itself guarantee security. NIST also identifies AI-specific concerns such as evasion, model extraction and membership inference, alongside broader machine-learning attacks and availability risks. Its AI Research: Security and Resilience page notes areas where existing guidance is incomplete, so a conventional security checklist alone may not address every AI-specific concern.
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Evaluate the configured system with representative tasks, data and users—not just a model benchmark or a handful of striking prompts. Include the languages, document types, user roles, connected tools and failure conditions expected in deployment. Record the test method, results, known limitations and any human review needed.
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NIST cautions that games or standardized human-designed exams may not establish validity in a particular domain; anecdotal jailbreak or prompt-engineering tests may not systematically measure validity or reliability; and laboratory or benchmark conditions may differ from real-world use. A test should answer a defined question about your context, not serve as general proof that a system is safe or reliable. The NIST Generative AI Profile recommends structured, documented evaluation informed by relevant AI actors and revisited over the system lifecycle.
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If you are choosing among services or configurations, collect the same information for each and weight it according to data sensitivity and the consequences of failure. Mark unknowns explicitly rather than treating a missing statement as a favorable answer.
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| Review area | Evidence to record | Decision question |
|---|---|---|
| Data flows | Inputs, outputs, telemetry, connected services and third parties | Can you limit collection and avoid unnecessary disclosure? |
| Retention and use | Retention periods, deletion scope, training or improvement use, user and administrator controls | Do the terms and controls fit the data and account tier? |
| Security and resilience | Access, isolation, incident handling, availability and integration exposure | Are responsibilities and safeguards clear enough for the use? |
| Transparency | Terms, security documentation, attestations and stated limitations | Can you verify material claims and identify unresolved gaps? |
| Task evaluation | Test method, representative conditions, results and coverage limits | Does the evidence address the actual task and affected users? |
| Operational fit | Available controls, accountable owners, monitoring and exit options | Can you manage the system over time or discontinue it if needed? |
NIST emphasizes that trustworthiness qualities can involve trade-offs. Its AI RMF identifies validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Their relative importance depends on the use context; document why the chosen balance is acceptable. See NIST AI Risks and Trustworthiness.
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Document the decision and revisit it when things change
Keep a record of the intended use, data categories, service tier and settings checked, evidence reviewed, test method and limitations, unresolved questions, safeguards and the person accountable for the decision. Set acceptable-use boundaries, define any required human review, and plan how to respond if data is exposed, outputs cause harm or the service becomes unavailable.
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Reassess after a material change to the model, terms, settings, integrations, data or deployment. A decision made for one configuration may no longer fit a changed system. NIST’s AI RMF organizes this work across governance, mapping, measurement and management; use it to structure your process rather than to imply that a vendor or model has been certified.
What a risk review can—and cannot—tell you
The official NIST materials cited here do not establish a general probability that an arbitrary AI model will expose private data or suffer a security incident. Avoid applying a single unsourced percentage to a specific service. A useful review instead identifies the data and systems at stake, the provider’s documented practices, the evidence from context-specific tests, and the remaining uncertainty. The NIST Cybersecurity, Privacy, and AI page was updated July 15, 2026; provider terms and configurations can change independently, so check the current materials relevant to your deployment.
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