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Evaluate an enterprise AI tool as a configured system—not just as a model or a vendor’s marketing claims. Map its purpose, users, data flows, integrations, permissions, and accountable owners; verify the safeguards and contractual terms; test the workflows you intend to deploy; and set conditions for monitoring and change. NIST’s voluntary AI Risk Management Framework can organize this work, while OWASP’s LLM guidance can help structure application-security testing. Neither framework establishes that a particular tool or deployment is safe or legally compliant.
What exactly are you evaluating?
Start by drawing the boundary around the system and the decisions it may influence. “An AI assistant” can mean a hosted chat product, a model API, a retrieval-augmented application that searches company files, an agent that can call tools, or AI embedded in another product. Their data paths, permissions, and failure modes differ.
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Record the intended tasks and users, prohibited uses, human decision points, connected systems, and what happens if an output is wrong. Include the application layer, model provider, integrations, third-party components, and relevant stages of the lifecycle—not only the model itself.
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- Specify what decisions people may make using its output and who remains accountable for those decisions.
- List the business owner and the security, privacy, legal, procurement, and technical owners responsible for review and approval.
This boundary gives the rest of the assessment a real use case to test. A tool may be acceptable for low-sensitivity drafting but unsuitable for a workflow that exposes regulated data or takes consequential actions.
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How should you assess data privacy?
Trace information through the complete service lifecycle. Do not rely on a product label such as “enterprise” or “private” to establish how data is handled. Ask the vendor for contractual commitments and available technical settings, then verify the settings in the tenant and configuration you plan to use.
Map each data path
For prompts, uploaded files, retrieved content, outputs, user feedback, logs, and telemetry, establish what is collected, where it is processed and stored, who can access it, and how long it is retained. Include subprocessors, storage locations, cross-border transfers, deletion procedures, and any use for model training or service improvement.
Ask specifically whether prompts and files are used to train models, improve services, or otherwise reused; whether the answer varies by product, setting, or contract; and whether the commitment covers every relevant data type and subprocessor. “Not used for training” alone does not answer questions about logging, retention, human access, or other processing.
Set data-use rules for your organization
Classify the information users may submit: personal, confidential, regulated, privileged, or public. For each category, document whether use is allowed, which configuration or safeguards are required, and whose approval is needed. Test access boundaries using the actual connected repositories and user roles. A correct-sounding answer is not evidence that the system retrieved only information the user was authorized to see.
What security risks should you test?
Test the entire configured workflow, including retrieval sources, connected tools, identity controls, and downstream systems. OWASP’s Top 10 for LLM Applications 2025 identifies risks including prompt injection and sensitive-information disclosure. It cautions that prompt-level restrictions may be bypassed, so a system prompt or instruction telling the model not to reveal information is not a security boundary on its own.
Prompt injection and untrusted content
Test direct attacks in user prompts and indirect attacks in material the system reads, such as documents, websites, email, or integration content. Check whether hostile instructions can alter the answer, reveal data, or cause a connected action. Where possible, separate untrusted content from trusted instructions, and enforce authorization and action limits outside the model.
Disclosure and authorization failures
Use representative test cases to check whether personal information, financial or health data, confidential business information, credentials, legal material, or proprietary content appears in outputs or is exposed through application behavior. Verify retrieval permissions and access boundaries for different users. Limit what the system can retrieve, and sanitize sensitive material where appropriate.
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For workflows connected to APIs, plugins, email, or business systems, determine which actions are possible and which identities or permissions the system uses. Apply least privilege at both the application and API layers. Require human approval for privileged or consequential operations, and test whether the approval step can be bypassed or whether an action can exceed the intended scope.
Supply-chain and lifecycle risk
Review how the model and application producers handle secure development, dependencies, updates, vulnerabilities, and lifecycle responsibilities. NIST SP 800-218A extends the Secure Software Development Framework for AI model and system development and is intended to help both producers and acquirers. Use it to inform questions about development and acquisition; it does not by itself prove that a vendor or product meets your requirements.
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How do you compare enterprise AI tools fairly?
Compare candidates using the same intended task, data sensitivity, user roles, connected systems, and configuration. Weight each dimension according to your threat model, sector, and legal duties; a single generic score can conceal a critical weakness in the area that matters most to your use case.
| Evaluation area | What to establish | Useful evidence or test |
|---|---|---|
| Data use and retention | Training or service-improvement use, logging, retention, deletion, and subprocessors | Applicable contract terms and settings verified in the intended tenant |
| Privacy and data-subject support | How personal data is handled and what support is available for relevant privacy obligations | Data-processing terms, documented processes, and configuration-specific answers |
| Identity, access, and isolation | Authentication, roles, tenant separation, repository permissions, and administrative access | Access-control documentation and tests using representative roles and data |
| Encryption and key management | What protections apply to data in transit and at rest, and what key-management choices are available | Technical documentation matched to the selected service and deployment |
| Model and application security | Prompt-injection, disclosure, misuse, and workflow failure protections | Security testing summaries plus tests of your configured application |
| Connected-tool permissions | Available actions, API scopes, approval gates, and privilege boundaries | Integration configuration and end-to-end tests, including attempted unauthorized actions |
| Audit and incident response | Available logs, access to audit evidence, incident notification, and response processes | Current documentation, contractual terms, and an understood escalation path |
| Evaluation evidence and reliability | Quality, consistency, operational failure modes, and evidence relevant to your use case | Repeatable tests against acceptance criteria using representative workflows |
| Deployment and location | Available deployment choices, processing or storage locations, and cross-border implications | Service-specific documentation and contract terms for the chosen configuration |
| Change management and exit | Notice of model, feature, subprocessor, or term changes; export, deletion, and decommissioning options | Documented change processes, contractual commitments, and an exercised exit plan |
Request current architecture and data-flow diagrams, security testing summaries, vulnerability and incident processes, access and audit controls, data-processing terms, retention and deletion options, subcontractor information, and change notices. Map each item to a requirement and to the service configuration under review. A generic certification, policy, or vendor capability claim is evidence to evaluate, not proof that your intended deployment is safe or compliant.
How can NIST guidance organize the evaluation?
NIST AI RMF 1.0 is voluntary guidance, not a certification or a substitute for applicable law. Its four functions provide a practical way to organize work across the system lifecycle:
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| Function | How it applies to procurement and use |
|---|---|
| Govern | Assign ownership, policies, decision authority, and ongoing accountability. |
| Map | Describe the context, intended use, users, affected parties, data, and risks. |
| Measure | Test and evaluate the system against requirements, risks, and acceptance criteria. |
| Manage | Prioritize risks, select responses, document residual risk, and monitor controls. |
NIST says trustworthiness considerations should span pre-design, design and development, deployment and use, and testing and evaluation. Its Generative AI Profile, NIST AI 600-1, is a cross-sector companion that describes risks novel to or amplified by generative AI and suggests actions aligned with the AI RMF. Both AI 600-1 and SP 800-218A were published on July 26, 2024. NIST’s AI RMF site notes that version 1.0 is being revised, so check the current NIST materials before building or updating a control mapping.
Does the EU AI Act apply to your AI use?
Do not apply one date or one conclusion to every enterprise AI tool. Applicability and obligations depend on factors including the system’s classification, its use, the organization’s role, and territorial scope. An organization may have obligations as a provider, deployer, or another operator; its role needs to be assessed for the specific arrangement.
As summarized by EUR-Lex, general application of the EU AI Act began on August 2, 2026, while some provisions began earlier and high-risk-system obligations have category-specific later dates. Following a 2026 amendment, the cited schedule gives December 2, 2027, for Annex III systems and August 2, 2028, for systems related to Annex I regulated products. Those dates concern specified high-risk obligations; they are not a universal start date for every AI Act duty or every enterprise tool.
Before relying on a date, check the operative legal text, classification rules, transitional provisions, territorial scope, and your organization’s role. The AI Act does not replace data-protection duties: its text says deployers should use provider information, where applicable, to comply with GDPR or law-enforcement data-protection impact-assessment duties. Organization-specific legal review is necessary to determine which obligations apply.
What should an approval decision record?
Set acceptance criteria before testing, based on the use case, threat model, impact, and risk tolerance. Evaluate output quality and reliability alongside security, privacy, bias, explainability, and operational failure modes. The decision record should make both the evidence and the remaining uncertainty visible.
- Document test scope, configuration, findings, and whether each acceptance criterion was met.
- Record residual risks, compensating controls, restrictions on users or data, and any conditions attached to approval.
- Name the approving owners and define incident escalation, review cadence, and responsibility for reassessment.
- Specify rollback, exit, data deletion, and safe decommissioning steps before broad deployment.
After procurement, reassess when intended use or system design changes, an incident occurs, legal requirements change, or there are material changes to the model or version, features, connected tools, data terms, subprocessors, or controls. A purchase-time review is only one point in a system’s lifecycle.
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