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The right question is not whether an AI tool is “safe” in the abstract. It is whether a specific tool, configured for a specific task, can handle the data and consequences involved within risks your organization is willing and able to control. A defensible evaluation maps the use case and data flows, checks ordinary and AI-specific security, obtains evidence from the supplier, tests the complete workflow, and records a decision with owners and review triggers.
Use the process below for a public chatbot, an enterprise assistant, an API model, or an AI feature embedded in another product. NIST’s AI Risk Management Framework (AI RMF) is a voluntary, use-case-agnostic structure for this work—not a certification or legal safe harbor—and its guidance is intended to be adapted to an organization’s resources and context.
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What an AI-tool evaluation is actually deciding
An evaluation decides whether a defined deployment is appropriate, what controls are required, and who accepts the remaining risk. It does not certify that a model is secure, accurate, private, unbiased, or compliant in every situation. The same model can be reasonable for drafting an internal meeting summary and unacceptable for making an eligibility decision, handling privileged material, or sending unsupervised payments.
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Separate four questions that are often collapsed into one:
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- Fit: Can the system perform the intended task at the required quality and speed?
- Exposure: What information and systems can the provider, model, application, users, and subprocessors access?
- Control: Can your organization limit, observe, review, correct, and stop what the system does?
- Residual risk: After controls, what could still go wrong, how severe would it be, and is an accountable owner willing to accept that?
A vendor statement, benchmark score, audit badge, or polished demonstration can provide evidence for one of those questions. None answers all four.
Start with the use case and its risk boundary
Write a one-page use-case record
Before asking a supplier for security paperwork, record the deployment in terms another reviewer could understand. Identify the business owner, technical owner, intended users, affected people, task, success criteria, jurisdictions, retention needs, and downstream systems. State whether the output is advice, a draft for a person to review, or an input to an automated action.
Describe the complete operating boundary: what the model can read, what context is retrieved, what memory is retained, which tools or APIs are connected, and what the surrounding application can do with an output. Include the failure cases that matter. Ask what happens if the service is wrong, unavailable, manipulated, delayed, or fed malicious content.
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Classify the consequences of failure
Risk rises with the sensitivity of the data, the number and vulnerability of people affected, the reversibility of an error, and the authority granted to the system. A useful first pass is to classify the use case rather than the vendor:
| Use-case profile | Typical examples | Evaluation emphasis |
|---|---|---|
| Low consequence | Optional brainstorming or formatting of non-sensitive material | Basic account security, acceptable-use rules, output review, and a simple fallback |
| Moderate consequence | Internal knowledge search, customer-support drafting, code assistance, or analysis of confidential business material | Data-flow restrictions, access controls, logging, retention, quality testing, prompt-injection tests, and human approval before external use |
| High consequence | Inputs to decisions about employment, credit, healthcare, safety, legal rights, money movement, or access to critical systems | Documented governance, domain validation, strong isolation, independent review, contestability, least privilege, continuous monitoring, and a tested shutdown and fallback process |
This table is a planning aid, not a legal classification. Sector and jurisdiction-specific duties can change the required controls. Obtain qualified legal or compliance advice when the use involves regulated data or decisions about people.
Define a risk boundary before a product demo
Write explicit exclusions and approval gates. For example, a pilot might prohibit personal identifiers, health information, credentials, source code from restricted repositories, and automatic external actions. It might require a named reviewer for every customer-facing answer and disable long-term memory. Boundaries make testing measurable and give administrators something enforceable to configure.
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Map every prompt, output, and side channel
Draw the complete data path
Prompts and outputs are data flows, not merely text typed into a box. Map user messages, uploaded files, retrieved documents, system prompts, conversation history, memory, tool results, generated outputs, telemetry, error reports, abuse-monitoring records, and support tickets. Show where each item travels: browser or client, your application, the AI provider, embedded model or search service, plug-ins, cloud storage, analytics, and backups.
For every component, record the data owner, purpose, location, retention period, access roles, encryption, deletion method, and whether data can be exported. Include data generated indirectly, such as embeddings, indexes, cached prompts, evaluation logs, and administrator audit records. If a provider cannot explain a path, treat the unknown as an unresolved risk rather than assuming it is harmless.
Ask these questions for each data category
- Is the content stored? If so, where, for how long, and for which purposes?
- Is it used to train, fine-tune, evaluate, or improve a model? Is an opt-out available, and what exactly does it cover?
- Which affiliates, subprocessors, foundation-model providers, embedded services, or human reviewers can access it?
- Can support, abuse-prevention, or debugging staff view it? What approval, masking, logging, and time limits govern that access?
- How do deletion, account closure, export, legal holds, and backup expiration work?
- Which personal, confidential, privileged, regulated, or third-party material is prohibited or requires an additional plan?
- In which countries or regions can processing and support occur, and can those locations change without notice?
“Not used for training” answers only one question. It does not describe retention, human access, support logs, subprocessors, backups, legal requests, or the model and application components that receive the content.
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Classify the data before granting access
Use your existing information-classification scheme where possible. Mark whether a workflow contains public, internal, confidential, personal, privileged, regulated, security-sensitive, or third-party information. Consider combinations: a single harmless field can become sensitive when joined with an identifier, a customer record, a location, or an inference about a person.
Set technical and procedural limits that match the classification. Possible controls include tenant isolation, private networking, redaction before submission, field-level filtering, disabling conversation history, short retention, customer-managed keys where supported, role-based access, and blocking uploads to consumer accounts. Make the rule visible at the point of use; a policy hidden in a contract will not prevent accidental disclosure.
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Confirm that the organization has a legitimate purpose and an appropriate basis for processing the data in each jurisdiction. Determine whether prompts and outputs can be reused for product improvement, advertising, profiling, or other secondary purposes. Ask how requests from individuals are handled, including access, correction, deletion, objection, and explanation where applicable. These are organization- and jurisdiction-specific questions; a vendor’s general privacy page cannot answer them for your deployment.
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Plan deletion and exit before launch
Define what “delete” means for live systems, indexes, derived embeddings, logs, disaster-recovery copies, and subcontractors. Establish a time-bound process for exporting records needed for continuity, disabling accounts and tokens, revoking integrations, and confirming deletion at service termination. Test the process during the pilot instead of discovering that an archive or connector cannot be removed after a production incident.
Review ordinary cybersecurity and AI-specific attack surfaces
Verify baseline security controls
AI does not replace conventional security engineering. Request evidence appropriate to the deployment and verify how it applies to the exact service, edition, region, and configuration you will use.
| Control area | Questions to answer | Evidence to request |
|---|---|---|
| Identity and access | Are single sign-on, multi-factor authentication, role separation, service accounts, and least privilege supported? Can administrators restrict model, data, and tool access independently? | Configuration documentation, access-control model, privileged-access process, and sample audit events |
| Isolation and encryption | How are tenants, files, indexes, and runtime workloads separated? Where is data encrypted, and who controls keys? | Architecture and data-flow diagrams, encryption and key-management descriptions |
| Logging and detection | Which prompts, outputs, tool calls, administrator actions, and policy changes are logged? Can logs be exported to your monitoring system without exposing sensitive content? | Log schema, retention settings, alerting examples, and access controls |
| Vulnerability and patch management | How are model-serving infrastructure, dependencies, connectors, and client libraries scanned and fixed? How are customers notified? | Vulnerability-management summary, remediation targets, and notification process |
| Resilience | What availability commitments, recovery objectives, rate limits, and offline procedures apply? Can the business continue if the model or an integration fails? | Service terms, continuity and recovery summaries, status and incident procedures |
| Incident response | Who is notified, how quickly, with what facts, and through which channel? Can you preserve evidence and suspend access immediately? | Incident-notification commitments, response roles, and customer playbook |
Assess confidentiality, integrity, and availability for the AI system, its training or retrieval data, its outputs, and the surrounding software and hardware. An AI feature can inherit a serious vulnerability from an ordinary web application, identity provider, dependency, or cloud configuration.
Use the OWASP 2026 LLM risk categories as a test menu
The OWASP 2026 LLM Top 10, published in August 2026, lists prompt injection, sensitive-information disclosure, excessive agency, supply-chain weaknesses, data and model poisoning, unbounded consumption, misinformation, hidden-context exposure, vector and embedding weaknesses, and improper output handling. Not every category applies equally to every tool. Use the applicable categories to create questions and test cases, and check for updates because AI threat guidance changes quickly.
- Prompt injection: Can instructions in user input, retrieved documents, web pages, tool results, or persistent memory override system rules?
- Sensitive-information disclosure: Can one user retrieve another user’s data, system prompts, secrets, hidden context, or memorized training material?
- Excessive agency: Can the model send messages, change records, execute code, spend money, or alter permissions without a person’s approval?
- Supply-chain and poisoning risks: Could a compromised model, package, plug-in, data source, or fine-tuning set change behavior?
- Unbounded consumption: Can oversized inputs, recursive tool calls, or repeated requests exhaust budget, capacity, or service quotas?
- Misinformation and output handling: Are unsupported claims, unsafe code, malicious markup, or generated commands validated before downstream use?
- Vector, embedding, and hidden-context weaknesses: Can retrieval boundaries be crossed, or can an attacker infer information from indexes and internal instructions?
OWASP’s project leads describe a corpus of 7,714 incidents assembled from public vulnerability databases and an AI-harm database; 6,639 contained enough detail for classification. Those figures describe that research corpus, not the number of incidents in the market and not the probability that a particular product will fail.
Constrain connected tools and actions
For assistants connected to documents, browsers, code execution, email, ticketing, finance, or cloud APIs, design the system so a fooled model cannot cause an important outcome by itself. Give the model the smallest possible permissions, separate read from write access, limit destinations and amounts, validate arguments in trusted code, and require explicit human approval for consequential actions. Treat external retrieved content and tool output as untrusted data, not as instructions.
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Test whether a malicious document can make the assistant reveal hidden context, call an unauthorized tool, or carry an instruction into a later conversation. Log the attempted and completed action separately, and make emergency revocation possible without waiting for a model response.
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Inventory the whole supply chain
The supplier is more than the company named on an order form. Identify the hosted model, embedded foundation models, retrieval and search services, plug-ins, data providers, infrastructure providers, support contractors, and any customer-managed components. Ask who can change each component and how those changes are communicated.
Match evidence to the risk
Request proportionate documentation rather than collecting badges. Depending on the deployment, useful evidence may include architecture and data-flow diagrams, a software bill of materials, model and version records, independent assurance reports, penetration-test summaries, vulnerability-management practices, incident-response commitments, service-level terms, data-processing terms, and provenance information for training or retrieval sources.
An attestation, certificate, or assurance report covers a defined scope, period, control set, and service configuration. It is not a blanket guarantee for every AI feature, region, integration, or customer setting. Record the scope and any exceptions, then verify that it matches your proposed deployment.
Put material promises in the contract
Clarify permitted uses, ownership and licensing of inputs and outputs, confidentiality, retention, deletion, subprocessors, security controls, audit or assessment rights, incident notification, service commitments, support access, model-change notice, data-location commitments, and exit assistance. Define what counts as a material change and whether it gives your organization a right to test, suspend, or terminate.
Ask how the supplier handles a newly discovered vulnerability, a service outage, a government request, a subcontractor change, a model retirement, and an end-of-service event. Assign internal owners for monitoring, incident response, user communications, and the decision to suspend the tool. NIST’s Generative AI Profile treats procurement diligence and ongoing monitoring as lifecycle activities, not a one-time questionnaire.
Test the intended workflow, not just the model
Build a representative test set
Use tasks, users, permissions, documents, languages, edge cases, and volume that resemble the planned deployment. Create a test set that includes normal requests, ambiguous requests, known difficult cases, prohibited inputs, stale or conflicting documents, and realistic adversarial content. Use de-identified or synthetic data until controls are proven, and restrict access to any sensitive test material.
- Define expected behavior. For each scenario, specify an acceptable answer, required citation or source, refusal condition, escalation path, latency target, and permitted tool actions.
- Run normal and boundary cases. Measure task quality, uncertainty, consistency, speed, cost, availability, and the frequency of human correction.
- Probe failure modes. Test prompt injection in user text, retrieved files, web pages, tool results, and memory; attempt cross-tenant retrieval, secret disclosure, unsafe output execution, privilege escalation, and resource exhaustion.
- Exercise downstream controls. Verify that parsers, filters, approval queues, permissions, rate limits, and audit logs behave safely when the model is wrong or malicious.
- Replay after changes. Save test cases and rerun them after a model update, prompt change, connector change, policy change, or significant incident.
Evaluate people and process
Observe whether users understand uncertainty, notice unsafe suggestions, follow approval rules, and know how to report an incident. Test fallback operations during an outage and determine whether work can continue without silently bypassing controls. Include affected stakeholders where the output can influence them, and provide a way to challenge or correct an erroneous result.
Understand what a benchmark cannot prove
A benchmark, standardized exam, anecdotal prompt test, or vendor demonstration measures a narrow slice of behavior under selected conditions. It cannot establish validity or reliability in your domain, with your permissions, documents, users, integrations, or consequences. NIST describes test, evaluation, validation, and verification as iterative activities that should be documented and informed by representative actors; current methods can still be inadequate or mismatched to a deployment. OWASP likewise emphasizes adaptive attackers and layered defenses rather than a one-time score.
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For two or more candidates, use identical scenarios, data classifications, questions, test duration, and acceptance thresholds. Weight the criteria according to the use case instead of producing an unexplained average score.
| Axis | Compare these specifics |
|---|---|
| Privacy and data handling | Input and output retention, training or improvement use, staff access, subprocessors, data locations, deletion, export, backups, and controls for sensitive data |
| Security | Identity controls, isolation, encryption, logging, incident response, vulnerability handling, integration security, and applicable AI-threat mitigations |
| Vendor and supply chain | Embedded models, third parties, provenance, version and change notices, assurance scope, dependency transparency, and exit options |
| Performance and reliability | Task-specific quality, known limits, refusal and uncertainty behavior, error handling, availability, latency, rate limits, and realistic operating cost |
| Human oversight | Review points, approval gates, contestability, accountability, user training, escalation, and fallback procedures |
| Business and contract fit | Permitted use, intellectual-property terms, service commitments, support, portability, implementation effort, and operational burden |
Record evidence and confidence separately. A candidate with excellent quality but opaque retention may be unsuitable for confidential data; a highly controlled service that cannot meet the task’s accuracy or availability needs may also be unsuitable. NIST frameworks support adaptable risk management; they do not prescribe a universal control set or rank vendors.
Make the decision and document residual risk
Use four possible outcomes
- Approve: The use case meets its requirements with controls already implemented.
- Approve with conditions: Use is limited by data classes, users, regions, integrations, approval gates, or monitoring tasks that have named owners and deadlines.
- Pilot only: Evidence or testing is incomplete, so access is time-limited, non-production, and restricted to approved data and users.
- Reject or defer: The consequences, unknowns, or supplier terms exceed the organization’s risk tolerance, and available controls cannot reduce them sufficiently.
The decision record should state the intended use, data and jurisdictions, affected people, evidence reviewed, test results, assumptions, unresolved questions, required mitigations, accountable risk owner, monitoring plan, fallback, and conditions that trigger re-review or suspension. “The vendor is secure” is not a decision rationale; a traceable statement about a defined configuration is.
Monitor for change
Reassess when the model, system prompt, data source, connector, permissions, vendor, subprocessor, legal context, user population, or business purpose changes. Watch for incidents, material version changes, unusual access, drift in quality, rising refusal or hallucination rates, cost spikes, and new attack techniques. Maintain an approved-provider list and a current inventory of AI systems, model versions, access modes, provenance, issues, and sensitive-data considerations.
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Keep a tested kill switch, revoke tokens and integrations, preserve necessary evidence, notify affected users, and move work to a documented fallback. When retiring a tool, export what the business must retain, disable accounts and automations, delete live and derived data according to the agreed schedule, and confirm how backups and subprocessors are handled. A service that cannot be safely paused or replaced creates operational dependence that belongs in the initial risk decision.
Quick evaluation checklist
- Is the task, owner, user group, affected population, jurisdiction, and failure consequence written down?
- Do you know every input, output, log, memory store, index, subprocessor, and support path?
- Are sensitive-data rules, retention, training use, access, deletion, and export contractually and technically clear?
- Have ordinary security controls and relevant OWASP AI-specific risks been assessed?
- Are connected tools limited by least privilege, trusted-code validation, and human approval for consequential actions?
- Did representative users test normal, boundary, and adversarial cases in the real workflow?
- Does the evidence cover the exact service, edition, region, version, and configuration?
- Is there a named risk owner, monitoring plan, incident process, fallback, and decommissioning path?
- Are review triggers defined for model, data, integration, supplier, legal, and purpose changes?
The practical standard is not a promise that a model will never be fooled. As OWASP 2026 project lead Steve Wilson puts it: “Stop trying to build a model that cannot be fooled. Build the system around it, so that when the model is fooled, and it will be, nothing important breaks.” Treat that as a design principle, not a measured guarantee: constrain consequences, keep people accountable, and make the service replaceable.
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