Evaluate an AI tool against the specific work it will do, the data it will handle, and the way it will be configured—not its product label or a vendor’s general assurances. A sound review maps the data path, checks technical controls and contractual terms, assesses applicable legal duties, tests the intended workflow, and sets conditions for approval and ongoing monitoring.
Start with the use case, not the product category
The same AI service can present very different risks depending on who uses it, what information they submit, what the system can access, and whether its output is merely a draft or influences a consequential decision. Define the proposed deployment before reviewing a vendor.
- Purpose and users: What task will the tool perform, and which employees, contractors, or other people will use it?
- People and decisions affected: Could its output affect a person’s access to a service, employment, finances, safety, or other consequential interest? Will a person review the output before acting on it?
- Information: What data may enter through prompts, files, connectors, telemetry, or support interactions, and what information may leave in outputs or downstream actions?
- Deployment: Which product, plan, settings, integrations, operating locations, and user groups are in scope?
- Failure and fallback: What harm could result from disclosure, a wrong output, an outage, or an unintended action? What is the fallback if the tool is unavailable or must be disabled?
Set data rules that employees can follow: what is prohibited, what needs approval, and what may be used under specified controls. Distinguish low-impact drafting from tools that make or materially influence decisions. NIST describes its AI Risk Management Framework (AI RMF) as voluntary and adaptable to an organization’s goals, resources, and priorities; its generative AI profile addresses risks across the AI lifecycle.
Trace data through the service and its contracts
Draw the path from the user’s input through the model, vendor service, integrations, storage, support, and eventual deletion. For each data category, obtain answers for the exact product and proposed configuration. A general privacy statement may not describe the terms that apply to a particular plan, setting, or support interaction.
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- Collection: What prompt text, uploaded files, connector content, outputs, usage information, and telemetry are collected?
- Retention and deletion: How long is each category kept? Can an administrator configure retention? What happens to backups, legal holds, and data after account termination?
- Training and improvement: Is customer content used to train or improve models or services? Does the answer change by product tier, setting, or support channel?
- Location and transfers: Where is data processed and stored? Which subprocessors receive it, and what transfer terms apply?
- Human access: Can vendor personnel or support staff view customer content? Under what circumstances, and is that access logged?
- Incidents: What notification, investigation, and cooperation duties apply if the service or customer data is affected?
Record the answer, the source of evidence, its date, and any configuration or contract condition it depends on. Resolve material gaps before sensitive data is used. Privacy review should also consider whether information could be re-identified, whether outputs could reveal sensitive inferences, and whether the workflow amplifies tracking or surveillance. NIST identifies these as AI-related privacy concerns.
Review the service boundary, integrations, and AI-specific threats
Assess the security of the vendor service and every connection it makes to organizational systems. AI security is not limited to protecting prompts: confidentiality, integrity, and availability matter for outputs, models, training data, and supporting hardware and software as well.
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- Identity and permissions: Check identity federation, multi-factor authentication, role-based access, service accounts, and tenant separation. Confirm that users and integrations receive only the permissions the workflow needs.
- Data and audit controls: Review encryption in transit and at rest, key handling, audit logs, backup and recovery, and vulnerability management.
- Integrations: Inventory plugins, APIs, agents, connectors, and connected data stores. Establish what each can read or change and whether those actions are logged.
- Operations: Examine incident response arrangements, service availability and recovery evidence, and how the vendor communicates material changes.
- Model-related behavior: Consider prompt injection through supplied content, unintended disclosure, unsafe tool use, supply-chain issues, and unusual or adversarial inputs. Test the relevant risks in the proposed workflow rather than assuming they apply—or do not apply—based on a product description.
For example, if an assistant can read internal documents and invoke tools, assess both the documents it can retrieve and the actions it can take. Restrict access, test how untrusted content affects downstream behavior, and define a human review or stop mechanism where the consequences warrant one. General AI security guidance does not establish the likelihood of a particular exploit in a particular product.
Determine which privacy and compliance duties apply
Legal obligations depend on the deployment: the data and purpose, affected people, geography, sector, and the organization’s role. Identify those facts before concluding that a tool or workflow is compliant. Consider lawful purpose and basis, notice, minimization, retention, access, individual rights, cross-border processing, and impact-assessment duties where applicable. This is a scoping framework, not a complete jurisdiction-by-jurisdiction legal checklist.
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Under the consolidated text of Regulation (EU) 2024/1689, the EU AI Act generally applies from August 2, 2026, with exceptions and staged dates: some provisions apply earlier and certain high-risk system obligations later. Determine the system’s classification and the organization’s role, then check the dates and obligations that apply to that particular deployment in the current consolidated text. Do not assume every AI tool is high risk.
The AI Act does not replace applicable EU personal-data law. Its text states that EU personal-data protection law continues to apply to personal data processed in connection with rights and obligations under the Act. Assess that law separately where personal data is involved.
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For other jurisdictions and regulated sectors
Identify the current law, regulator guidance, and contractual duties relevant to the organization, sector, data, and use. If those obligations have not been established, treat compliance as an open review item rather than relying on a generic claim that a tool is compliant.
Use frameworks to structure the review, not certify a vendor
Frameworks can help organize governance and risk work, but using one does not demonstrate that a specific product meets an organization’s contractual, privacy, security, or legal requirements. NIST states that its AI RMF is voluntary; ISO/IEC 42001 specifies requirements for an organization’s AI management system.
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| Reference | What it provides | How to use it in a tool review |
|---|---|---|
| NIST AI RMF 1.0 | A voluntary framework released January 26, 2023, to help organizations incorporate trustworthiness into AI design, development, use, and evaluation. NIST reports that it is being revised and that a critical-infrastructure profile concept note was released April 7, 2026. | Use it to organize risk work around the organization’s context and lifecycle. It is not a vendor security certificate or a legal guarantee. |
| NIST AI 600-1 Generative AI Profile | A cross-sectoral companion released July 26, 2024, applying the AI RMF to generative AI and suggesting actions to govern, map, measure, and manage risks across lifecycle stages. | Use it to identify generative-AI-specific questions, including governance, testing, content provenance, and incident disclosure. It does not prove that a product has passed those checks. |
| ISO/IEC 42001:2023 | An international standard specifying requirements for establishing, implementing, maintaining, and continually improving an AI management system within an organization. | Use it as a governance structure for organizations that develop, provide, or use AI. It does not establish that a particular vendor’s product meets every purchaser’s needs. |
| EU AI Act, Regulation (EU) 2024/1689 | EU rules for AI systems and general-purpose AI models, including prohibitions, high-risk requirements, and transparency rules, with staged applicability. | Assess the system, organizational role, and applicable dates and duties for the specific EU deployment; assess applicable personal-data law separately. |
Compare evidence and set approval conditions
Use a comparison sheet for each shortlisted tool rather than relying on labels such as “enterprise,” “private,” or “secure.” Ask for documentation and commitments that match the proposed product, plan, settings, integrations, and use. A policy statement is not by itself evidence that a control operates as needed.
Useful comparison fields include:
- Use-case fit, scope, and affected users
- Data collection, retention, deletion, and model-improvement terms
- Processing and storage locations, subprocessors, and transfer terms
- Identity, permissions, tenant separation, encryption, and key controls
- Integrations, auditability, incident response, and recovery
- Testing, monitoring, and change-notification evidence
- Contractual commitments, remedies, availability, and total cost
For each answer, note who owns the review, when the evidence was obtained, unresolved issues, and the condition required for approval. Evidence may include current control documentation, contract language, configuration details, and results relevant to the intended workflow. If a vendor cannot substantiate a material claim, record the gap and decide whether to limit the data or functionality, require a contractual commitment, postpone approval, or reject the use.
Test before deployment and monitor after approval
Run a controlled evaluation with representative, appropriately protected data. Test normal use as well as realistic failure and misuse cases. Check what the service logs, what connected tools can access or change, how outputs are reviewed, and whether the workflow can be stopped or rolled back. Document results against the risks and approval conditions defined for the use case.
Assign an accountable owner and set monitoring triggers and a reassessment cadence. Reopen the review after a material change to the model, terms, configuration, integrations, data use, or applicable law. Approval should apply to a defined use and configuration, not automatically extend to new use cases or broader access.
Additional safeguards for operational technology
For operational technology and critical infrastructure, use specific safety controls rather than treating the review as an ordinary office-software assessment. A December 3, 2025 NSA release summarizing joint guidance from NSA, CISA, the Australian Signals Directorate’s Australian Cyber Security Centre, and partner organizations says operators should use AI only when benefits clearly outweigh risks, establish governance with testing and monitoring, include a human in critical decisions, and use fail-safe mechanisms. It also notes that separating OT data from an AI system may be appropriate.
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