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Do not approve an AI tool because a vendor calls it “secure,” “private,” or “compliant.” Approve a specific use under documented conditions, after you understand the data flow, verify proportional security evidence, map the obligations that actually apply, test representative work, and assign ongoing accountability.
The same model can be low risk for drafting public copy and high risk for making decisions about employees, patients, customers, or access to money. This guide gives procurement, security, privacy, legal, compliance, and operations teams a repeatable, risk-based evaluation process.
1. Define the proposed use before evaluating the product
Risk starts with context, not the model’s marketing category. Write a short use-case record before requesting a demonstration or security questionnaire. Include:
- the business purpose and decisions the system will support;
- the people who will use it and the people affected by its outputs;
- the prompts, files, records, images, audio, code, or other data involved;
- jurisdictions, business units, and retention requirements;
- integrations, plugins, APIs, browser access, databases, and actions the system can trigger;
- what happens if an answer is wrong, unavailable, manipulated, or delayed; and
- the proposed human review and escalation path.
Record whether your organization is acting as an AI provider, deployer, or both for this particular activity. Under the EU framework, classification depends on intended purpose and how the system is used, not simply on the vendor’s product label. The European Commission explains this context-based approach in Navigating the AI Act.
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Separate assistance from autonomous action
An assistant that suggests text for a trained employee is different from a system that ranks applicants, approves a loan, changes a production configuration, sends regulated communications, or calls external tools without review. Describe the highest-impact action the system could take, even if the initial plan is “human in the loop.” A nominal reviewer is not a control if the workflow gives that person no time, information, or authority to reject the output.
Write an impact statement
State who could be harmed and how. Consider privacy loss, discrimination, unsafe advice, intellectual-property exposure, financial loss, operational outage, security compromise, regulatory breach, and loss of public trust. This statement determines how much evidence, testing, approval, and monitoring you need.
2. Classify the data and map the privacy exposure
Obtain a product-specific data-flow description. Do not infer the answer from a general privacy policy or from the fact that a plan is labeled “business.” Ask about every category that can leave your environment:
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- prompts and conversation history;
- uploaded documents, images, audio, source code, and database extracts;
- generated outputs, ratings, corrections, and user feedback;
- telemetry such as account identifiers, device details, IP addresses, usage events, and diagnostic logs;
- administrator exports, support tickets, backups, and abuse-monitoring records; and
- data sent to plugins, model hosts, analytics services, or other subprocessors.
Questions that must have precise answers
- What is collected by default, and which fields can an administrator disable?
- How long is each category retained in active systems, backups, and support systems?
- Is input, output, or feedback used to train, fine-tune, evaluate, or improve a shared model? Is the setting contractual, or can it change through a policy update?
- Which vendor personnel, contractors, subprocessors, or customers can access the data, under what approvals, and with what audit trail?
- In which countries is data stored, processed, or supported? Can the vendor identify the region for each processing activity?
- How are access, correction, deletion, objection, and data-export requests handled, and what exceptions apply to backups or legal holds?
- What happens to data when an account is suspended, a workspace is closed, or the contract ends?
NIST’s Generative Artificial Intelligence Profile (NIST AI 600-1) identifies third-party data collection and use as privacy and information-security risks. Treat prompts and outputs as information assets, not as disposable chat text.
Match the product to your internal data classification
Create a simple allowlist for the proposed deployment. For example, public information may be permitted, internal operational information may require an approved workspace, and personal, confidential, export-controlled, health, payment, or authentication data may be prohibited unless a separate assessment authorizes it. Define whether users may paste entire records, whether identifiers must be masked, and whether outputs may be copied into a system of record.
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Consider intellectual property as well as personal information. Source code, unpublished designs, customer contracts, and research notes can create trade-secret or copyright exposure even when they contain no names.
3. Verify security controls with evidence proportionate to risk
A questionnaire is a starting point, not proof. Ask for evidence that matches the impact statement and the system’s access. For a low-impact drafting assistant, a clear architecture and incident process may be sufficient. A tool connected to production systems or sensitive records requires substantially more assurance.
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|---|---|---|
| Identity and access | Architecture description, role matrix, single sign-on and multifactor documentation, privileged-access process | Least privilege, administrator separation, lifecycle for joiners and leavers, service-account controls, and whether your organization can enforce its own policies |
| Encryption and key management | Data-flow diagram and encryption/key-management description | Protection in transit and at rest, tenant separation, key ownership options, and treatment of logs, backups, and support channels |
| Logging and detection | Sample audit events and retention configuration | Coverage of sign-ins, exports, prompt or file access, administrative changes, tool calls, and alerts; verify that logs can be retrieved in a usable format |
| Incident response | Incident-response policy, notification commitments, contact path, and recent exercise summary where available | Detection-to-notification timing, customer responsibilities, evidence preservation, containment, and post-incident reporting |
| Vulnerability management | Testing summary, remediation policy, dependency-management process, and security-contact procedure | Scope and frequency of testing, severity targets, treatment of model and prompt-injection vulnerabilities, and whether findings are independently validated |
| Supply chain | Software bill of materials (SBOM), subprocessor list, and change-notification terms | Critical dependencies, update controls, material subprocessor changes, and your right to object or exit |
| Availability and recovery | Service-level agreement (SLA), resilience design, recovery objectives, and outage history if disclosed | Whether commitments cover the functions you need, how credits are calculated, and how you operate during an outage |
| Exit and deletion | Export format, deletion procedure, retention exceptions, and termination clause | Ability to retrieve prompts, outputs, metadata, configurations, and audit records; confirmation that deletion reaches subprocessors and backups within stated limits |
NIST lists procurement due diligence, SBOMs, SLAs, and attestation reports as possible transparency and risk-management measures. An attestation describes a scope and a period; it does not guarantee that your configuration, integration, or use is secure. Check the boundaries, exceptions, control owners, testing method, and report date instead of accepting a badge or certificate at face value.
4. Map the obligations that apply to this deployment
Make a written applicability analysis using five inputs: intended purpose, geography, organizational role, data, and sector. Include privacy, employment, consumer-protection, records-management, cybersecurity, accessibility, intellectual-property, and industry rules where relevant. Ask counsel or a qualified privacy professional to resolve questions that depend on local law; this process is not an individualized legal opinion.
EU AI Act considerations
The European Commission’s AI Act materials and Navigating the AI Act FAQ describe provider and deployer responsibilities according to intended purpose and risk category. For high-risk systems, the Commission describes duties involving risk management, data quality, documentation and traceability, transparency, human oversight, accuracy, cybersecurity, and robustness. Some deployments also require monitoring, relevant input data, and notices to affected people.
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Do not ask only, “Is this vendor compliant?” Ask which actor has which duty, what your deployment changes, what documentation you must retain, and what notices or human controls are required. A vendor’s compliance statement cannot perform your deployer obligations.
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Keep a dated regulatory timeline
Regulatory dates can change, so verify the Commission pages immediately before approval and again when the system changes. The pages reviewed state:
| Date | Published Commission position | How to use it |
|---|---|---|
| 2 August 2025 | Rules for general-purpose AI (GPAI) models began applying. | Confirm whether the model and the parties in your supply chain fall within the applicable provisions. |
| 2 August 2026 | Commission GPAI enforcement powers apply, and specified Article 50 transparency obligations apply. | Plan for documentation, transparency, and enforcement expectations that are in force by your launch date. |
| 2 August 2027 | Pre-existing GPAI models have the compliance date described in the Commission’s GPAI guidance. | Check whether a model first placed on the market earlier is covered by transitional treatment. |
| 2 December 2027 | The published high-risk application schedule lists this date. | Recheck the current schedule and assess whether later guidance or amendments affect your use. |
Read the Commission’s guidelines for providers of general-purpose AI models and guidelines on transparency obligations for the current interpretation. The dates above are not a substitute for a role- and use-specific determination.
5. Test the actual system before rollout
Documentation cannot reveal how a configured tool behaves with your data, users, permissions, and integrations. Run a controlled evaluation using representative tasks and record the results. NIST recommends iterative, documented test, evaluation, validation, and verification (TEVV) early in the AI lifecycle, with relevant AI actors involved.
Build a representative test set
- Use realistic task types, languages, formats, edge cases, and workload volumes.
- Include ordinary cases and deliberately difficult cases, but remove unnecessary personal or confidential data.
- Define the correct answer, acceptable variation, and escalation requirement before running the test.
- Test the exact model version, system prompt, retrieval sources, permissions, plugins, and downstream actions planned for production.
Measure more than answer quality
| Test dimension | Questions to answer |
|---|---|
| Performance and reliability | Does it complete the intended task accurately and consistently? What happens at peak load, with long inputs, or when a source is missing? |
| Privacy leakage | Can one user obtain another user’s data? Does the system reveal hidden prompts, retrieved documents, metadata, or memorized sensitive content? |
| Unsafe or biased output | Does it produce discriminatory, dangerous, misleading, or disallowed content for relevant groups and scenarios? |
| Robustness and misuse | How does it respond to malformed input, adversarial instructions, prompt injection, jailbreak attempts, conflicting sources, and deliberate misuse? |
| Connected-tool risk | Can an instruction cause an unauthorized email, transaction, code change, record update, file disclosure, or privilege escalation? |
| Human control | Can a reviewer understand the basis and uncertainty of an output, override it, pause an action, and escalate a suspected failure? |
| Operational behavior | Are latency, rate limits, outages, version changes, and error messages compatible with the process? |
Document failures, not just averages
Keep the test cases, inputs, outputs, model and configuration versions, reviewer decisions, severity ratings, and remediation. A high average score can hide a rare failure with severe consequences. Define go/no-go criteria in advance: mandatory controls are pass/fail gates, while desirable capabilities can be weighted. If a failure cannot be mitigated, narrow the use, add a human approval step, remove an integration, or reject the deployment.
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6. Compare tools with a weighted, risk-based scorecard
There is no universal “best AI tool.” Set minimum gates first, then compare the tools that pass. Weight each axis according to the likely harm and your exposure; a customer-support drafting tool and a clinical decision aid should not use the same weights.
| Axis | What to compare | Typical gate question |
|---|---|---|
| Data lifecycle | Collection, retention, training use, access, residency, deletion, and export | Can the tool meet your data-classification and deletion requirements? |
| Security assurance | Identity, encryption, logging, incident response, vulnerabilities, supply chain, resilience, and evidence quality | Are required controls independently evidenced and available for your configuration? |
| Legal and regulatory fit | Provider/deployer roles, notices, records, human oversight, sector rules, and jurisdictional limits | Can you document why this deployment satisfies each applicable duty? |
| Task performance | Accuracy, consistency, latency, failure modes, and accessibility on your test set | Does it meet the predefined threshold without unacceptable exceptions? |
| Transparency | Documentation of capabilities, limitations, model changes, sources, and uncertainty | Can users and reviewers recognize when not to rely on an output? |
| Administration | Roles, policy controls, audit logs, rate limits, environment separation, and configuration management | Can administrators enforce permitted use and investigate events? |
| Integration and subprocessors | APIs, plugins, data paths, permissions, change notifications, and dependency concentration | Can every connection be approved, monitored, and disabled? |
| Incident support | Notification, investigation, evidence sharing, service restoration, and cooperation | Will the vendor help you meet your own reporting and containment deadlines? |
| Portability and exit | Data and configuration export, deletion, migration effort, and termination rights | Can you leave without losing records or creating an uncontrolled data copy? |
| Total operating cost | Licenses, usage, storage, integration, testing, monitoring, human review, training, and exit | Is the full control cost affordable over the expected life of the use? |
A practical scoring method
Use a documented scale, such as zero to five, with written definitions for each score. Multiply each score by the approved weight, but never allow a high feature score to compensate for a failed non-negotiable control. Keep source links, document versions, reviewer names, and assessment dates beside every score so another reviewer can reproduce the decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Make adoption conditional and assign ownership
An approval should state the conditions under which the tool may be used, not merely record that a committee liked it.
Specify operating controls
- Name an accountable business owner, technical owner, security contact, privacy contact, and escalation authority.
- Publish permitted uses, prohibited uses, approved data classes, and rules for copying outputs into official records.
- Train users to verify outputs, protect confidential information, recognize manipulation, and report incidents.
- Retain the logs needed for accountability while limiting unnecessary content collection.
- Monitor quality, harmful outputs, access violations, complaints, outages, cost, and changes in data or model behavior.
- Require reassessment after a material model, system-prompt, integration, subprocessor, purpose, jurisdiction, or data change.
- Define suspension triggers, such as a confirmed privacy incident, repeated severe errors, loss of required evidence, an unapproved model change, or inability to provide deletion.
- Maintain an exit plan that identifies replacement procedures, exported records, revoked credentials, deletion confirmation, and communications to affected users.
Use a change-control record
For each material change, capture what changed, why it changed, which tests were rerun, who approved it, and whether the risk rating changed. A model update that appears minor can alter refusals, data handling, tool calls, or error patterns. Treat the model, retrieval index, system instructions, connectors, and user interface as one controlled system.
8. Use recognized frameworks as structure, not as a safety certificate
NIST describes the AI Risk Management Framework (AI RMF) 1.0 as voluntary guidance released on 26 January 2023, and says the framework is being revised. NIST released the Generative AI Profile, NIST AI 600-1, on 26 July 2024. Check the official pages for updated material before relying on a version in policy.
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The framework’s trustworthiness characteristics include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Its FAQ states:
“The Framework users and AI actors should consider and encompass trustworthiness characteristics during pre-design, design and development, deployment, use, and test and evaluation of AI technologies.”
That lifecycle perspective is useful because procurement is only one control point. A framework, certification, audit report, or vendor assurance statement can organize questions and provide evidence about a defined scope; none proves that a particular configuration is lawful, accurate, or safe for your use.
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- Starting with a brand or model. Teams choose a tool before defining the decision, data, and harm. Fix this by approving a use case and impact statement first.
- Accepting “no training” as “no risk.” A vendor may not train on prompts yet still retain them, expose them to support staff, send them to subprocessors, or place them in logs. Review the entire lifecycle.
- Relying on a generic certification. A report may exclude your region, plan, connectors, or AI-specific behavior. Read scope, exceptions, and test period.
- Testing only happy paths. Normal examples miss leakage, prompt injection, biased outputs, and unsafe actions. Include adversarial and failure scenarios.
- Assuming a human reviewer cures automation risk. Define what the reviewer sees, how much time is available, and whether rejection is technically possible.
- Ignoring connected tools. A harmless-looking assistant can become high impact when it can send messages, alter records, execute code, or retrieve broad datasets.
- Approving once and forgetting it. Model, subprocessor, purpose, and legal requirements change. Set reassessment triggers and monitoring ownership in the approval.
- Calculating only subscription price. Include integration, data preparation, testing, human review, monitoring, incident response, training, migration, and exit costs.
10. A concise decision record for the approval file
At the end of the evaluation, retain a decision record that another team could audit:
- Use case, intended purpose, affected people, jurisdictions, roles, and impact rating.
- Approved data classes, prohibited data, data-flow diagram, retention and deletion terms, and subprocessor locations.
- Security evidence reviewed, its scope and date, unresolved exceptions, and compensating controls.
- Applicable obligations, the reasoning for each classification, required notices, human oversight, and records.
- Test plan, representative results, severe failures, remediation, and acceptance criteria.
- Weighted comparison with pass/fail gates, rejected alternatives, and total operating-cost assumptions.
- Named owners, user controls, monitoring measures, reassessment dates, suspension triggers, and exit steps.
- Approval signatures or electronic authorizations from the accountable business, security, privacy, legal, and compliance roles required by your governance process.
This record turns a procurement decision into an auditable operating decision. It also makes it possible to narrow or stop a deployment when evidence, behavior, or obligations change.
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