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Evaluate an AI tool against the specific job, data, people, jurisdictions, and consequences involved—not against a generic “secure” or “compliant” label. A defensible decision combines documented scope, data-flow and privacy review, security evidence, obligation mapping, representative testing, a risk-weighted comparison, and controls for operation, monitoring, suspension, and exit.
The same model can be acceptable for drafting public copy and unacceptable for decisions affecting employment, credit, health, safety, or legal rights. Treat vendor statements and certifications as evidence to examine, not as proof that your deployment is safe or lawful.
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Start with the use case, not the product
Write down what the system will do before reviewing features. Risk and regulatory treatment depend on intended purpose and context. The European Commission explains that an AI system’s classification under the EU AI Act depends on its intended purpose and how it is used, so a single product can receive different treatment in different deployments. See the Commission’s Navigating the AI Act guidance.
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Create a use-case record
Capture these facts in a short, versioned record that procurement, security, privacy, legal, compliance, and the business owner can all review:
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- Purpose and decisions: what task the AI performs, what decisions it informs, and whether a person can reject or override its output.
- Users and affected people: employees, contractors, customers, children, patients, applicants, members of the public, or other groups.
- Data: prompts, uploaded files, retrieved records, generated outputs, feedback, telemetry, identity data, confidential information, personal data, special-category data, trade secrets, and copyrighted material.
- Geography: where users, affected people, systems, and data are located, including cross-border transfers.
- Integrations: identity providers, storage, ticketing, code repositories, business systems, plug-ins, APIs, and tools the model can call.
- Failure consequences: financial loss, privacy harm, discrimination, unsafe action, security compromise, regulatory breach, reputational damage, or interruption of a critical service.
- Operating boundaries: permitted users, prohibited inputs, human approval points, retention period, service availability needs, and conditions for suspension.
Identify the AI roles
Determine whether your organization is a provider, deployer, or both for this particular use. A company may deploy a third-party model in one workflow while providing a downstream AI application in another. Record which party controls the purpose, configuration, user instructions, data, and decisions; those facts affect contractual duties and regulatory responsibilities.
Classify data and map the privacy exposure
Do not infer data handling from a product’s marketing page. Ask for a written data-flow description covering every input, intermediate artifact, output, log, and support channel. NIST’s Generative AI Profile (NIST AI 600-1) identifies third-party data collection and use as a privacy and information-security risk.
Questions to put to the vendor
- What prompts, files, outputs, ratings, clicks, device details, and diagnostic telemetry are collected?
- Are inputs or outputs retained after a request completes? State the default and the customer-configurable period separately.
- Are customer data, prompts, or outputs used to train, fine-tune, evaluate, or improve a model? Is that use disabled by default, contractually prohibited, or merely configurable?
- Which employees, support personnel, contractors, subprocessors, and automated systems can access the data, and under what approvals and logging?
- Which subprocessors handle storage, inference, safety filtering, analytics, support, or backups? How are changes announced and challenged?
- Where is data processed and stored, including backups and disaster-recovery locations? What transfer mechanism applies when data crosses borders?
- How can your organization retrieve, correct, restrict, or delete data? What remains in backups, caches, abuse-prevention systems, or legal holds, and for how long?
- How are outputs associated with a user or customer tenant, and how is tenant isolation tested?
- What happens to data when an account is suspended, a contract ends, or the provider changes its service?
Compare answers with your own classification
Map each data element to your internal classification scheme and to the privacy obligations that apply in the relevant jurisdictions and sector. A tool that is acceptable for public information may be unsuitable for confidential negotiations or regulated records. If the vendor cannot give a precise answer, treat the uncertainty as a risk requiring a compensating control, a narrower use, or a no-go decision—not as permission to upload sensitive data.
Check intellectual-property and confidentiality exposure
Confirm who may receive prompts and outputs, whether the provider claims rights to use them, and whether your contract preserves confidentiality and ownership terms. Review employee and customer notices where people’s information may be processed. Establish a safe-input rule that forbids users from pasting information that the approved configuration cannot protect.
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Demand security evidence proportionate to risk
Security review should examine architecture and operating evidence, not just a badge on a sales page. NIST lists procurement due diligence, software bills of materials (SBOMs), service-level agreements (SLAs), and attestation reports as possible transparency and risk-management measures. None is a guarantee; check scope, period, exceptions, and the controls that matter to your use.
Evidence request matrix
| Area | What to request | How to assess it |
|---|---|---|
| Identity and access | SSO and MFA options, role-based administration, privileged-access controls, service-account handling, and access-review process. | Confirm that least privilege, tenant separation, administrator logging, and rapid off-boarding fit your identity architecture. |
| Encryption and key management | Encryption in transit and at rest, key ownership or customer-managed-key options, backup protection, and secrets management. | Check algorithms and key scope, not merely the word “encrypted”; verify which components and backups are covered. |
| Logging and monitoring | Authentication, administrator, data-access, prompt, tool-call, policy-change, and deletion logs; retention and export options. | Ensure logs support investigations without creating an excessive new store of sensitive content. |
| Vulnerability management | Secure-development practices, scanning, penetration-test summaries, patch timelines, dependency management, and an SBOM where appropriate. | Look for dates, scope, severity targets, unresolved findings, and evidence that hosted and client-side components are included. |
| Incident response | Notification commitments, severity definitions, contact paths, forensic cooperation, evidence preservation, and customer communication process. | Match contractual deadlines and information supplied to your legal and regulatory reporting obligations. |
| Subprocessors and supply chain | Current subprocessor list, locations, functions, change notice, and objection or termination rights. | Trace every party that can access data or influence model behavior; do not stop at the primary vendor. |
| Availability and recovery | SLA, service status history, backup and restore objectives, disaster-recovery testing, and rate-limit behavior. | Decide whether outages or throttling could create unsafe workarounds or interrupt a critical process. |
| Deletion and exit | Export format, deletion certificate or attestation, backup-expiry process, configuration portability, and transition assistance. | Run a practical exit test; a promise to delete is weak if you cannot identify all copies or retrieve essential records. |
Read assurance reports critically
For an attestation report or certification, identify the exact service, environment, locations, control period, auditor or assessor, and exceptions. Determine whether the report covers the AI function, the underlying cloud, or only a narrow corporate process. Ask how controls address model-specific risks such as prompt injection, unsafe tool calls, data leakage, abuse monitoring, and rapid model changes. A report outside your deployment’s scope is context, not evidence that your use is covered.
Map the obligations that actually apply
There is no universal “AI compliant” status. Build an obligation map from purpose, role, geography, data, sector, and impact. Include privacy, employment, consumer-protection, safety, accessibility, records, intellectual-property, cybersecurity, and contractual requirements where relevant. Obtain advice for your facts when the consequences are material; this process is not an individualized legal determination.
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The European Commission’s AI Act materials describe obligations that vary by actor and use. For high-risk systems, the Commission describes requirements involving risk management, data quality, documentation and traceability, transparency, human oversight, accuracy, cybersecurity, and robustness. Some deployments also require monitoring, relevant input data, human oversight, and notices.
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| Commission timeline stated on the reviewed pages | What it means for planning |
|---|---|
| 2 August 2025 | Rules for general-purpose AI (GPAI) models began applying, according to the Commission’s guidance. |
| 2 August 2026 | The Commission’s GPAI guidance says its enforcement powers apply; Article 50 transparency obligations are also stated to apply from this date. |
| 2 August 2027 | The GPAI guidance gives pre-existing GPAI models this compliance date. |
| 2 December 2027 | The published high-risk application schedule lists this date for high-risk application rules. |
These dates are time-sensitive. Recheck the Commission’s current GPAI provider guidelines, Article 50 transparency guidance, and AI Act pages immediately before approval. Do not assume a date or category applies to your system without confirming whether you are the provider, deployer, importer, distributor, or another actor and whether an exception or transition rule applies.
Use frameworks as organizing tools, not certificates
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 its Generative AI Profile, NIST AI 600-1, on 26 July 2024. Use current versions and retain the date of the material you relied on.
NIST’s AI RMF 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.” Apply that lifecycle view to procurement: validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. A framework crosswalk helps find gaps; it does not establish that a particular vendor or deployment meets your obligations.
Test the proposed system before rollout
Run a controlled evaluation using representative tasks and realistic users. NIST recommends iterative, documented test, evaluation, validation, and verification (TEVV) early in the AI lifecycle, with representative AI actors involved. Preserve prompts, configurations, model version, tools, expected answers, observed outputs, reviewer decisions, and test date so results can be reproduced.
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Build a representative test set
- Sample normal work. Include the formats, languages, edge cases, volume, and time pressure the system will encounter in production.
- Define success and unacceptable outcomes. Specify accuracy, completeness, citation or provenance needs, maximum review effort, and errors that require automatic rejection.
- Probe privacy leakage. Test whether one user can retrieve another tenant’s information, whether prompts appear in logs or outputs, and whether the model reveals memorized or confidential material.
- Probe unsafe and biased behavior. Use realistic adversarial and ambiguous prompts, protected characteristics where relevant, and scenarios involving vulnerable people. Record refusal quality, stereotyping, disparate error patterns, and escalation behavior.
- Test robustness and misuse. Vary wording, language, document formatting, missing fields, long context, conflicting instructions, prompt injection, and attempts to bypass policy.
- Test connected tools. Verify authorization boundaries, confirmation steps, parameter validation, least privilege, rate limits, and what happens when a tool or retrieved document is malicious.
- Exercise human oversight. Give reviewers uncertain or harmful outputs and measure whether they can understand, challenge, correct, and override the system before an irreversible action.
- Repeat after changes. Re-run the relevant suite after a model, prompt, retrieval source, integration, policy, or configuration change.
Record limitations, not just scores
A pass rate without failure examples can conceal unacceptable risk. Keep a failure catalog showing the input, output, impact, detectability, reviewer action, and proposed mitigation. State where the system is not reliable, which populations or languages were not adequately tested, and which conclusions cannot be generalized beyond the tested configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare tools with a risk-weighted scorecard
When several options remain, use minimum gates for non-negotiable controls before scoring desirable features. Then weight the remaining axes according to the harm and exposure of this use case rather than producing a generic “best AI tool” ranking.
| Axis | Questions to score | Possible gate |
|---|---|---|
| Data lifecycle and privacy | What is collected, retained, trained on, accessed, transferred, and deleted? | No use of restricted data unless the approved contract and technical setting enforce it. |
| Security architecture and evidence | Are identity, isolation, encryption, logging, vulnerability management, and incident processes adequate and evidenced? | Reject if critical controls are absent or evidence is outside scope. |
| Legal and regulatory fit | Does the provider support the obligations mapped to this role, purpose, sector, and geography? | Reject unresolved legal prohibitions or missing mandatory notices and oversight. |
| Task performance and failure behavior | How does it perform on representative and adversarial tests, and how detectable are errors? | Reject if critical errors are not reliably detected or prevented. |
| Transparency and explainability | Are limitations, model changes, provenance, confidence signals, and reasons for output available at the level your decision requires? | Require a human-review path where explanation is necessary. |
| Administration and integration | Can you enforce SSO, roles, network boundaries, data-loss controls, approvals, and tool permissions? | Reject integrations that cannot be constrained to least privilege. |
| Incident support | Are notification, investigation, evidence, and service-restoration commitments usable? | Require contractual contacts and timelines matching your obligations. |
| Portability and exit | Can you export records, remove data, replace the model, and continue safely during an outage or termination? | Reject an exit plan that leaves essential records or controls inaccessible. |
| Total operating cost | What are license, usage, integration, review, monitoring, training, assurance, incident, and exit costs? | Include the cost of required human review and controls, not just API or seat price. |
Use a consistent scoring scale and record evidence beside every score. Separate “not provided” from “does not meet”: missing information is itself a procurement risk, but it should not be disguised as a technical failure. Have the accountable owner approve the weights and document why a lower-performing option is acceptable if it materially reduces privacy, security, or compliance exposure.
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Approval is the beginning of governance, not the end. Put the decision into a control plan with named owners and measurable triggers.
Before enabling users
- Name a business owner accountable for the purpose, a technical owner for configuration and access, and privacy, security, legal, and compliance reviewers appropriate to the risk.
- Publish permitted uses, prohibited data, approved models and workspaces, human-approval points, and a route for reporting harmful or unexpected output.
- Configure SSO, MFA, least-privilege roles, tenant restrictions, retention settings, logging, content filters, network controls, and tool-call approvals.
- Train users to verify outputs, protect confidential information, recognize prompt injection and fabricated content, and escalate incidents.
- Define records to retain: approval, contract, data-flow map, test results, model and configuration version, user guidance, exceptions, and monitoring plan.
During operation
- Monitor usage, access, data-policy violations, performance drift, unsafe outputs, bias indicators where relevant, tool calls, incidents, complaints, and service changes.
- Review logs for both security investigations and privacy minimization; retain only what the purpose and obligations require.
- Track vendor notices about model updates, subprocessors, locations, policies, and incidents. Trigger reassessment when a change could affect the approved risk profile.
- Give reviewers a practical override and escalation path. Do not let automation silently make an irreversible or high-impact decision.
Suspension and exit triggers
Predefine events that pause or restrict use: a confirmed data leak, inability to delete required data, material security incident, unexplained performance or bias deterioration, loss of required evidence, unapproved subprocessor or model change, regulatory direction, or failure of human oversight. Maintain a tested fallback process, export needed records, revoke credentials and integrations, request deletion, and document the closure.
Common evaluation mistakes and corrections
“The vendor is certified, so we are compliant”
Correction: verify the certification’s scope and map your own purpose, data, role, and jurisdiction. A certificate can support one control conclusion while leaving model behavior, human oversight, or your downstream use unaddressed.
“The contract says data is not used for training”
Correction: reconcile contract language with product defaults, support access, telemetry, subprocessors, backups, and configuration. Test the setting and document who can change it.
“The demo looked accurate”
Correction: evaluate representative, adversarial, multilingual, incomplete, and tool-connected tasks with predefined failure thresholds and independent reviewers.
“We can fix governance after launch”
Correction: require an owner, permitted-use policy, logging, user training, incident route, and rollback plan before enabling production access.
“The model is the only component that matters”
Correction: assess the entire system: prompts, retrieval data, plug-ins, identity, storage, monitoring, human workflow, subprocessors, and fallback process. Risk can enter through any connected component.
Quick Recap
A practical approval checklist
- Purpose, users, affected people, data, geography, integrations, and failure consequences are recorded.
- Provider/deployer roles and accountable owners are identified.
- Collection, retention, training use, access, subprocessors, processing locations, deletion, and exit answers are documented.
- Internal data classification and applicable privacy, security, sector, and AI obligations are mapped.
- Security evidence is current, scoped to the service, and includes unresolved exceptions.
- Representative TEVV covers privacy leakage, unsafe or biased output, robustness, misuse, connected tools, and human override.
- Non-negotiable gates are passed and weighted comparison scores have evidence.
- Contract, configuration, user policy, training, logging, monitoring, incident response, and fallback controls are ready.
- Material-change, suspension, reassessment, deletion, and exit procedures are tested and owned.
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