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You can use an AI tool with organizational data only after you understand the specific use case, map every data flow, test the tool in the real workflow, and decide whether the remaining risk is acceptable. A vendor’s privacy statement, a benchmark score, or an audit badge cannot answer those questions by itself.
This guide gives you a repeatable evaluation process. It treats the AI service, its embedded models, plug-ins, data sources, integrations, and human operators as one system. The result should be a documented decision with an owner, controls, monitoring, and an exit plan.
What does it mean to evaluate an AI tool?
Evaluation is a risk decision, not a search for a universally safe or unsafe product. The same model may be reasonable for drafting public marketing copy and unacceptable for making an employment, credit, medical, safety, access, or legal decision. Risk changes with the people affected, the data involved, the system’s permissions, and the consequences of an error or outage.
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A sound review answers five questions:
- What exactly will the system do, for whom, and with what authority?
- What information will enter the service, where will it go, and when will it be deleted?
- What can go wrong through ordinary failure, attack, misuse, or a supplier change?
- What evidence shows that the controls work in your intended workflow?
- Who accepts the residual risk, monitors it, and can suspend or replace the tool?
How do I define the use case and its risk boundary?
Write the use-case record before requesting a sales demonstration. A precise scope prevents a low-risk pilot from quietly becoming a high-impact production system.
| Record | Questions to answer | Why it matters |
|---|---|---|
| Task and purpose | What job will the AI perform? Is it generating, summarizing, classifying, retrieving, recommending, or taking actions? | Different tasks have different error modes and validation requirements. |
| Users and affected people | Who operates it, whose information is processed, and who could be affected without using it? | Children, employees, customers, patients, applicants, and the public may require stronger safeguards and review. |
| Business owner | Which person is accountable for the outcome, budget, controls, and shutdown decision? | An owner prevents responsibility from being split between procurement, IT, and a business team. |
| Data categories | Will prompts or files contain personal, confidential, privileged, regulated, copyrighted, security-sensitive, or third-party information? | Sensitivity determines what may be sent, which jurisdictions matter, and how much assurance is needed. |
| Location and jurisdiction | Where are users, data subjects, systems, and providers located? Which sector or contractual rules apply? | Legal duties depend on jurisdiction, role, data, and use; a general framework is not legal advice. |
| Connected systems | Can the application read mail, documents, tickets, databases, browsers, code repositories, or cloud APIs? | Permissions and integration paths often create more risk than the model alone. |
| Impact of failure | What happens if the output is wrong, biased, unavailable, delayed, leaked, or manipulated? | Errors affecting money, safety, rights, access, or reputation need tighter controls than harmless drafting errors. |
| Model authority | Can it only suggest text, or can it send, delete, approve, purchase, change records, or execute code? | Action capability determines whether human approval and technical guardrails are mandatory. |
Describe normal use, foreseeable misuse, and an attacker’s likely objective. Include what happens when the model is unavailable and what a user can do manually. This boundary becomes the reference for vendor questions, testing, approval, and later re-review.
Can I use this AI tool with company data?
Do not answer that question from a single phrase such as no training on your data. Training use is only one part of handling. You also need retention, support access, logging, subprocessors, embedded models, backups, deletion, and the permissions of the surrounding application.
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Draw the path for prompts, uploaded files, retrieved documents, tool results, generated outputs, telemetry, error reports, and support tickets. Identify each system and organization that can receive a copy. Include browser extensions, mobile clients, plug-ins, vector stores, analytics services, foundation-model providers, and internal gateways when they are present.
For every data category, obtain a written answer to these questions:
- Is the content stored? If so, in which regions, for how long, and for what purpose?
- Is it used to train or improve a model, and can your organization opt out? Does the answer differ for prompts, files, outputs, feedback, and abuse-monitoring logs?
- Which affiliates, subprocessors, embedded model providers, or integration services can access it?
- Can vendor personnel view it for support, abuse prevention, safety review, or debugging? What approval, least-privilege, masking, and audit logging apply?
- How do deletion, export, account termination, and backup expiration work? What evidence can you obtain?
- Which data is prohibited, and which data requires an enterprise configuration, regional hosting, encryption key, or other control?
Ask your internal owner the same questions about copies created outside the vendor: downloaded outputs, cached prompts, evaluation datasets, screenshots, logs, and records written to downstream systems. NIST identifies data protection, retention, and decommissioning as governance concerns and warns that third-party generative-AI integrations can create intellectual-property, privacy, and information-security risks.
“Third party GAI integrations may give rise to increased intellectual property, data privacy, or information security risks, pointing to the need for clear guidelines for transparency and risk management regarding the collection and use of third-party data for model inputs.” — NIST Generative AI Profile
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Set data-use rules before the pilot
Create an allowed-data policy that users can apply without guessing. For example, public information may be allowed in a general service; internal information may require an approved tenant; personal, privileged, regulated, or customer-provided information may be prohibited unless named controls and a legal review are complete. State whether users may paste secrets, upload source code, use real customer records, or copy outputs into systems of record.
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Where data minimization is possible, redact identifiers, use synthetic records, limit document retrieval, and configure short retention. These measures reduce exposure but do not prove that a service is private or compliant.
How should I review AI security?
Start with ordinary cybersecurity. The confidentiality, integrity, and availability objectives apply to the service, its training and retrieval data, its outputs, and every integration around it.
Baseline security questions
- Identity and access: Does the service support single sign-on, multifactor authentication, role-based access, service-account separation, and rapid deprovisioning?
- Least privilege: Can an assistant access only the records and tools required for a task? Are administrator, developer, evaluator, and end-user privileges separated?
- Isolation: Are tenants, projects, retrieval indexes, uploaded files, and conversation histories isolated? What prevents one user or customer from seeing another’s context?
- Encryption and keys: Is data encrypted in transit and at rest? Can the organization manage or rotate keys where the use case requires it?
- Logging: Are sign-ins, prompts, retrievals, tool calls, approvals, exports, configuration changes, and administrative access logged with useful retention?
- Vulnerability management: How are vulnerabilities reported, prioritized, patched, and communicated? Are penetration tests or independent assessments available for the relevant service?
- Resilience: What availability commitments, recovery objectives, backup controls, regional failover, and offline procedures exist?
- Incident response: When will the provider notify you, what information will it provide, and how will you preserve evidence and contain access?
- Secure development: How are code, models, data pipelines, dependencies, and deployment credentials protected?
AI-specific attack surfaces
Use the relevant categories from the OWASP Top 10 for Large Language Model Applications 2026 to shape tests and controls. The guide was published in August 2026; threat guidance changes quickly, so check for later updates.
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| Risk category | What to examine | Useful control examples |
|---|---|---|
| Prompt injection | Can instructions in user input, retrieved documents, web pages, tool output, or persistent memory override the intended task? | Separate instructions from data, treat retrieved content as untrusted, restrict tools, validate actions, and require approval for consequential operations. |
| Sensitive-information disclosure | Can prompts, secrets, personal data, system instructions, or another user’s context appear in an answer, log, or retrieval result? | Minimize data, enforce authorization before retrieval, redact secrets, test cross-tenant isolation, and monitor exports. |
| Excessive agency | Can the model send messages, modify records, spend money, delete data, execute code, or grant access? | Use narrow permissions, separate planning from execution, allow-list tools, set transaction limits, and obtain human approval. |
| Supply-chain risk | Which models, libraries, plug-ins, data sets, and hosting providers are embedded? How are they changed? | Maintain an inventory, review provenance, receive change notices, and keep a replacement or rollback path. |
| Data and model poisoning | Could malicious or low-quality training, retrieval, feedback, or evaluation data alter behavior? | Control write access, validate sources, version data, review changes, and monitor unexpected behavior. |
| Unbounded consumption | Can prompts, loops, agents, or large files exhaust budget, rate limits, compute, or service capacity? | Set quotas, timeouts, file and token limits, concurrency controls, and cost alerts. |
| Misinformation | Does the system present unsupported, fabricated, stale, or overconfident content as fact? | Use source citations where appropriate, confidence and uncertainty handling, domain review, and a prohibition on unattended high-impact decisions. |
| Hidden-context exposure | Can system prompts, hidden instructions, memory, retrieved metadata, or internal reasoning be elicited? | Keep secrets out of prompts, enforce authorization at the application layer, and assume model instructions can be revealed. |
| Vector and embedding weaknesses | Can poisoned, cross-tenant, stale, or incorrectly authorized embeddings influence retrieval? | Partition indexes, authenticate retrieval, filter by authorization, validate ingestion, and test deletion and reindexing. |
| Improper output handling | Are generated strings inserted into HTML, SQL, shell commands, code, tickets, or policy decisions without validation? | Use trusted-code parsers, output encoding, schema validation, sandboxing, and explicit approval before execution. |
OWASP’s 2026 project leads describe a corpus of 7,714 incidents gathered 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 a probability that a particular tool will fail.
Review connected assistants as applications, not chat boxes
For an assistant connected to documents, web search, memory, code execution, email, or cloud APIs, document exactly what it can read and write. Test whether an untrusted document can cause it to reveal data or call a tool. Ensure the application, not the model, enforces authorization and validates every action. OWASP recommends least privilege, trusted-code output validation, human approval for consequential actions, and testing against adaptive attackers.
What should I ask an AI vendor before buying?
Request evidence that matches your use case and deployment. Ask for scope, dates, exclusions, and the specific service or configuration covered; an assurance report or certificate is not a blanket guarantee for every feature.
| Evidence area | Questions to put in writing | What a useful answer contains |
|---|---|---|
| Architecture and data flow | Where do prompts, files, outputs, logs, and retrieval data travel? Which regions and providers are involved? | A current diagram, processing purposes, locations, and a list of subprocessors and embedded models. |
| Privacy and retention | What is retained, for how long, for which purpose, and under which customer-controlled settings? | Separate terms for content, metadata, support data, abuse monitoring, backups, and deletion evidence. |
| Model and version governance | How are models selected, evaluated, changed, deprecated, and rolled back? | Version identifiers, change-notice periods, migration options, and behavior or safety documentation. |
| Security assurance | What independent assessments, penetration tests, vulnerability processes, and access controls cover this service? | Reports or attestations with scope, period, exceptions, and a contact for follow-up. |
| Software and model supply chain | Can you provide an SBOM or equivalent component inventory? Which data sets, plug-ins, and foundation models are used? | Provenance information, update practices, license terms, and notification of material supplier changes. |
| Incidents and vulnerabilities | When and how will you notify us? What support is available for investigation and containment? | Time commitments, channels, severity definitions, evidence preservation, and customer responsibilities. |
| Service levels and resilience | What availability, support response, recovery, rate-limit, and outage terms apply? | Service-level terms, exclusions, status visibility, and a tested fallback arrangement. |
| Rights and exit | Who owns inputs and outputs? Can we export, delete, or migrate data and configurations at termination? | Contract language for permitted use, intellectual property, deletion timelines, backups, portability, and end-of-service assistance. |
NIST’s Generative AI Profile recommends procurement due diligence covering intellectual property, data privacy, security, embedded AI, and ongoing monitoring. It also identifies SBOMs, service-level agreements, and SSAE reports as possible transparency and risk-management controls. Their value depends on whether their scope covers the service, region, tenant type, and configuration you will use.
Check the whole supplier chain
Inventory the primary provider, foundation-model companies, plug-ins, data suppliers, hosting platforms, and integration services. Check relevant incident or vulnerability databases and maintain an approved-provider list if your organization uses one. Put security commitments, data rights, provenance expectations, subcontractor notice, audit cooperation, and incident duties in the contract rather than relying on a sales presentation.
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How do I test an AI tool in the intended workflow?
A demonstration shows what a provider chooses to show. A benchmark measures a defined task under defined conditions. Neither establishes reliability or safety in your environment. Test the complete workflow with representative users, data shapes, permissions, integrations, and downstream handling.
Build a controlled test set
- Define the expected result and an acceptable error threshold for each task. Specify when the system must refuse, escalate, or ask for clarification.
- Select representative examples, edge cases, known difficult cases, and safe adversarial cases. Use synthetic or de-identified data until privacy controls are approved.
- Run normal tasks with the roles and permissions planned for production. Record output quality, uncertainty, latency, cost, availability, and manual effort.
- Run failure tests: ambiguous instructions, missing context, stale documents, malformed files, service interruption, rate limits, and conflicting sources.
- Run security tests: prompt injection in user text and retrieved content, attempts to disclose secrets, cross-user retrieval, unauthorized tool calls, poisoned documents, and unsafe output insertion.
- Verify the surrounding application. Confirm that authorization, schema validation, encoding, sandboxing, approval gates, logging, and rollback work even when the model produces malicious or incorrect text.
- Have subject-matter reviewers assess harmful, biased, legally sensitive, or operationally misleading results. Record disagreements rather than averaging them away.
- Repeat tests after model, prompt, retrieval, permission, integration, or policy changes. Keep the test data, version identifiers, configuration, results, and decision thresholds.
NIST describes test, evaluation, validation, and verification as iterative activities that can begin early and be documented. It also cautions that current methods may be inadequate or mismatched to a deployment context. Treat a failed test as a control or design problem to resolve, not as a score to hide.
Measure workflow consequences
| Measure | What to record |
|---|---|
| Quality | Correctness against a human-reviewed reference, useful completion rate, refusal rate, and severity of errors. |
| Safety and privacy | Leakage attempts, unauthorized retrieval, harmful content, exposure of hidden context, and successful prompt injections. |
| Human oversight | Whether reviewers notice errors, time to review, override frequency, escalation quality, and whether approvals can be bypassed. |
| Operations | Latency, availability, rate-limit behavior, failure recovery, support response, and cost under expected volume. |
| Downstream effects | Incorrect records, unsafe commands, formatting or encoding failures, duplicate actions, and consequences in connected systems. |
How do I compare two or more AI tools consistently?
Use the same scenario, data classes, permissions, evidence requests, and acceptance tests for every candidate. Weight criteria according to the use case rather than awarding points for features that do not reduce your risk.
| Axis | Compare |
|---|---|
| Privacy and data handling | Input and output retention, training use, access, subprocessors, deletion, export, and data-location commitments. |
| Security | Identity, isolation, encryption, logging, incident response, vulnerability handling, integrations, and AI-specific threat controls. |
| Vendor and supply chain | Embedded models, third parties, change notices, assurance scope, provenance, portability, and replacement options. |
| Performance and reliability | Task-specific quality, known limitations, refusal and error behavior, availability, latency, and realistic operating cost. |
| Human oversight | Review points, contestability, approval gates, accountability, fallback work, and the ability to stop an action. |
| Business and contract fit | Permitted use, intellectual-property terms, service commitments, support, portability, implementation effort, and operational burden. |
One practical scoring method is to assign each axis a weight based on impact and data sensitivity, rate the evidence quality separately from the product’s apparent capability, and mark unknowns as unresolved rather than giving them a neutral score. A high capability score cannot compensate for an unacceptable privacy or authorization gap.
How do I decide whether residual risk is acceptable?
Document the decision after testing and supplier review. The record should let another manager understand what was approved, under which conditions, and who can stop it.
Decision record
- Use case, users, affected people, data categories, jurisdictions, integrations, and model or service versions.
- Expected benefits and the alternatives considered, including a non-AI process.
- Evidence reviewed, test results, known limitations, unresolved assumptions, and any vendor exceptions.
- Required controls: data restrictions, access roles, approval gates, logging, retention, monitoring, and user training.
- Residual risks, their potential impact, likelihood rationale, and the accountable risk owner.
- Conditions for approval, pilot limits, expiry date, and triggers for suspension or re-review.
- Fallback procedure for outage, degraded quality, incident, supplier change, or model retirement.
- Secure export and deletion steps when the service is replaced or decommissioned.
Use a simple outcome category
| Outcome | When it fits | Required next step |
|---|---|---|
| Approve | Evidence and tests address the use case, controls are operating, and the owner accepts the remaining risk. | Launch within the documented scope and monitoring plan. |
| Approve with conditions | Benefits are useful but specific gaps remain that can be bounded by data limits, permissions, human review, volume caps, or a time-limited pilot. | Assign owners and deadlines; suspend use if conditions are not met. |
| Defer | Evidence, testing, contract terms, or operational ownership is incomplete. | Resolve named gaps before processing sensitive or high-impact data. |
| Reject | The tool cannot meet a non-negotiable privacy, security, legal, safety, reliability, or exit requirement. | Do not introduce it into the proposed workflow; document the reason. |
When should an approved AI tool be reassessed?
Vendor review is continuous. Reassess when any of the following changes:
- The task expands, a new group is affected, or outputs begin influencing money, safety, access, employment, legal rights, or regulated decisions.
- A model, system prompt, retrieval index, plug-in, data source, region, subprocessor, or permission changes.
- Retention, training use, pricing, service terms, support access, or deletion behavior changes.
- A security incident, vulnerability, privacy complaint, harmful output, unexplained quality shift, or material outage occurs.
- Volume, latency, cost, or automation increases enough to change the business impact.
- Legal, contractual, policy, or sector requirements change.
- The provider announces end-of-service, migration, acquisition, or a change that limits export or replacement.
Maintain an inventory of AI systems and versions, monitor incidents and performance, and assign someone to review provider notices. NIST recommends contingency and incident-response processes for high-risk third-party AI systems, along with safe decommissioning.
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Relying on the phrase not used for training
Why it fails: It says nothing about retention, support access, logs, backups, subprocessors, or retrieval services.
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Correction: Require a complete data-flow and deletion answer for every content and metadata category.
Testing only easy prompts or a polished demo
Why it fails: It hides ambiguity, adversarial input, integration failures, and downstream harm.
Correction: Use representative and adversarial tasks with production-like permissions and record failures, refusals, and recovery.
Treating an audit badge as a product guarantee
Why it fails: Assurance is limited to a scope, period, controls, and configuration. It may not cover the AI feature or your tenant.
Correction: Read the scope and exceptions, then perform use-case testing and obtain contractual commitments.
Letting the model enforce its own permissions
Why it fails: A model can be persuaded, confused, or supplied with hostile instructions.
Correction: Enforce identity and authorization in trusted application code; use the model to propose actions, not to grant itself authority.
Ignoring embedded providers and data sources
Why it fails: A provider may rely on other models, plug-ins, hosting, analytics, or retrieval indexes with different terms and risks.
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Correction: Inventory the supply chain, request change notices, and preserve a replacement path.
Approving a pilot without an exit plan
Why it fails: A team can become dependent before it knows how to export data, revoke access, or continue during an outage.
Correction: Test export, deletion, fallback, and account termination before production approval.
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NIST supplies a governance and lifecycle structure: map the context, measure risks, manage them, and govern accountability throughout the system’s life. It is voluntary and does not certify a tool, determine legal compliance, or prescribe one control set.
OWASP supplies an application-security lens for language-model systems, including prompt injection, sensitive-information disclosure, excessive agency, supply-chain issues, poisoning, unbounded consumption, misinformation, hidden context, vector and embedding weaknesses, and improper output handling. It helps you design questions and tests; it does not rank vendors or guarantee that a product is safe.
Use both as adaptable aids. Your organization still has to decide which risks apply, obtain jurisdiction-specific legal advice where needed, verify evidence, and accept or reject the residual risk for its own deployment.
A practical final gate before production
Approve the tool only when the named owner can answer yes to each applicable question:
- Is the task, affected population, data, authority, and failure consequence documented?
- Is every prompt, file, output, log, retrieval, support, and subprocessor data flow understood?
- Are sensitive-data rules, access controls, approvals, output validation, and logging operating in the actual workflow?
- Have representative and adversarial tests covered quality, privacy, security, human oversight, availability, and downstream effects?
- Do contracts and supplier evidence cover the service and configuration you will use?
- Is there an accountable owner, monitoring plan, incident process, fallback, and decommissioning procedure?
- Has someone explicitly accepted the remaining risk, with conditions and a date for reassessment?
If any answer is no, limit the pilot to safer data and lower-impact tasks or defer deployment. The strongest evaluation does not try to prove that a model can never be fooled; it designs the surrounding system so that a wrong or manipulated model response cannot cause unacceptable harm.
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