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An AI vendor security review should establish whether the service is safe for a defined use—not whether the vendor can produce a certificate or complete a questionnaire. Document the use case and data, trace every system and provider that handles them, verify ordinary and AI-specific controls with scoped evidence, then record an approval decision with conditions and reassessment triggers.
Start with the use case and the consequences of failure
Set the review depth from the actual deployment and its risks. Before evaluating a vendor, record what the system will do, who will use it, what data it will handle, and what could happen if it produces a wrong, exposed, or manipulated result.
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- Purpose and boundaries: State the business purpose, intended uses, and uses that are prohibited or out of scope.
- People and decisions: Identify user groups, affected people, decisions influenced, and the consequences of failure. Specify where human review is required.
- Architecture and autonomy: Identify whether the offering is a hosted API, embedded feature, fine-tuned model, retrieval-augmented system, agent, or self-hosted component. Document what the system can do without human approval.
- Data and geography: Classify prompts, files, retrieval content, outputs, and feedback; identify sensitive or regulated information and the jurisdictions involved.
- Accountability: Name the review owner, business owner, and approver, and set the required level of verification.
Use the NIST AI Risk Management Framework (AI RMF) to organize governance and context, not to claim that a vendor is certified. NIST describes AI RMF 1.0 as voluntary and says it is being revised; its Generative AI Profile was released July 26, 2024. NIST’s Playbook suggests actions aligned with Govern, Map, Measure, and Manage, but says it is “neither a checklist nor set of steps to be followed in its entirety.” Adapt it to the system and your organization.
What should an AI vendor security review include?
Assess the vendor as a supplier and the AI-enabled product as a system. A useful review covers the following areas, with depth matched to the exposure you documented.
#1 Best Overall
Vendor, ownership, and supply chain
Request an inventory of the legal entity, ownership and control, operating locations, hosting providers, model providers, subprocessors, open-source or downloaded models, and critical service dependencies. Ask what is known about model and dataset provenance, and record material gaps or limits. Assess resilience, capacity, backup and recovery, continuity arrangements, and whether you can transition away from the service.
NIST SP 1326, published in July 2026, frames ICT-supplier due diligence around foreign ownership, control, or influence (FOCI), provenance, resilience, foundational cyber practices, and supply-chain tiers. It complements AI-specific review; it does not replace it.
Data lifecycle, privacy, and permitted use
Draw the complete data flow, from collection to deletion. Include prompts, attachments, API payloads, retrieval corpora, embeddings, fine-tuning inputs, outputs, feedback, telemetry, support access, logs, backups, and transfers to downstream providers. For each data type, establish who can access it, where it is processed, how long it is retained, and how deletion propagates.
Ask these questions separately; a general statement that data is “protected” does not answer them:
- Is customer data retained, used to train or improve models, reviewed by people, or used in abuse monitoring?
- Which settings control training and retention, and can the customer disable them?
- Which subprocessors receive data, including downstream model providers, and do the same restrictions apply to them?
- What are the retention periods, deletion steps, backup expiry periods, and regional processing options? What exceptions apply?
- What encryption is used in transit and at rest? How are tenant separation, key ownership and rotation, role-based access, privileged access, and secrets handled?
- Can the customer export data and obtain evidence of deletion? Does deletion cover retrieval indexes, embeddings, and other derived copies where applicable?
Match controls and contract terms to the data classification, legal obligations, jurisdiction, and use case. Do not assume a universal retention period or training rule.
Baseline cyber security
Review the same foundational controls you would expect of any technology supplier: security governance and accountability; identity and access management; privileged operations; secure development and change control; vulnerability management and patching; cloud and network configuration; secrets; logging and monitoring; incident response; backup and recovery; and business continuity.
Request independent assurance reports and check the covered entity and service, review period, criteria, exceptions, remediation status, and any bridge letter relevant to a gap between reporting periods. Compare the report’s scope with the exact AI product, deployment option, and subprocessors being purchased. A framework mapping or audit can reduce duplicated questions, but it only supports conclusions within its stated scope, period, criteria, and exceptions; seek implementation evidence for material controls it does not cover.
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Review the AI system’s lifecycle and behavior in the architecture you will actually use. Ask for the approved model inventory, version pinning or change notification, release testing, rollback process, and deprecation plan. Determine how the vendor handles prompt injection, data leakage, unsafe tool calls, model extraction or abuse, poisoning, and output validation; ask for controls and evidence rather than relying on a list of threat names.
Input, retrieval, and memory
- How are trusted system instructions separated from untrusted prompts, files, and retrieved content?
- For retrieval systems, how are source permissions enforced when content is indexed and again when it is retrieved?
- How are tenant boundaries enforced in indexes, vector stores, and memory? How does deletion propagate to indexed or derived content?
- Can prompts or retrieved material cause the model to reveal data beyond the requesting user’s authorization?
Tools, agents, and integrations
- Are tools restricted by explicit allowlists and least-privilege permissions?
- Are the agent’s identity and permissions separated from the user’s, and can access be limited by task?
- Which consequential actions require human approval, and can the reviewer inspect what the agent is about to do?
- Are tool calls auditable, monitored, and attributable to a user or service identity?
Outputs, monitoring, and model changes
Ask how outputs are constrained and validated before they reach downstream systems or users. Establish what is logged, how unexpected behavior is detected and escalated, and whether logs themselves can contain sensitive data. Agree how you will be told about material model, hosting, subprocessor, training, retention, or control changes that could alter the risk of your deployment.
What security evidence should I request from an AI provider?
Request evidence that matches each material claim and the product configuration under review. Useful evidence includes:
- A current architecture and data-flow diagram, plus a component and subprocessor inventory.
- Security policies or control descriptions covering access, secure development, change management, vulnerability handling, incident response, and continuity.
- Independent assurance reports with their scope, dates, exceptions, and remediation status.
- A threat model and summaries of security testing, penetration testing, red-team work, or adversarial evaluation. Ask for test dates, scope, exclusions, severity of findings, remediation, and retest evidence.
- Evidence of data-use and retention settings, deletion procedures, access restrictions, and relevant downstream-provider commitments.
- For AI-specific controls, test results or design evidence for model lifecycle, retrieval authorization, tenant isolation, tool permissions, output handling, and monitoring.
Validate evidence rather than counting documents. Confirm that dates are current enough for the risk, the assessed entity and service match what you are buying, important deployment options and subprocessors are in scope, and exceptions have an owner and remediation plan. For material controls outside an audit’s scope, ask for implementation evidence. Agree which customer tests are allowed and how to run them without exposing another tenant or production data.
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Use verification standards proportionately
OWASP AISVS 1.0, released in June 2026, is a vendor-neutral, testable standard intended for procurement, assessment, penetration testing, and audits. It contains 191 requirements across 12 chapters and three appendices. OWASP lists 51 requirements at Level 1, 95 at Level 2, and 45 at Level 3 for that edition. Level 1 is a baseline; Level 2 targets production, customer-facing systems, sensitive data, or consequential decisions; Level 3 is intended for high-assurance, critical-infrastructure, safety-critical, and regulated settings. Select a level based on your system’s exposure, not the vendor’s marketing. AISVS is intentionally narrow, so assess general application, infrastructure, and supply-chain security in parallel. When using a requirement in a contract or assessment, name the edition and requirement identifier.
OWASP LLMSVS v2.0 provides LLM-specific verification requirements covering secure configuration and maintenance, model lifecycle, real-time learning, memory and storage, integrations, agents and plugins, dependencies, and monitoring. It complements rather than replaces broad risk assessment and application security review. OWASP says automated tool results alone are insufficient and recommends documentation of which controls were tested and the supporting evidence. OWASP does not certify vendors, verifiers, or software under LLMSVS; a claimed “OWASP certified” mark is not an OWASP-issued certification. Neither standard makes a test suite a guarantee of safety or security.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the review shape the contract and ongoing oversight?
Put material controls into terms and operating procedures rather than leaving them as sales assurances. The agreement should identify the approved service and use, data instructions and restrictions, confidentiality, subprocessor notice and objection process, security commitments, incident notification and cooperation, audit or evidence rights, material-change notice, retention and deletion, ownership boundaries for models and data, and termination or transition assistance. Have legal counsel tailor terms to the buyer’s data, jurisdictions, industry, and use case.
Set named incident contacts, investigation cooperation, security-event notification expectations, evidence retention, and recurring review dates. Define reassessment triggers, including a change in model, training or retention defaults, hosting, subprocessors, data sensitivity, user population, autonomy, or the decisions the system influences. Vendor review is a point-in-time decision; those changes can alter the system’s risk after approval.
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Make the decision against the documented use and evidence, not a single certification or questionnaire score. Record the scope, data classification, risk level, evidence and dates, open findings, compensating controls, accountable owner, decision, approval conditions, review or expiry date, and triggers for a new review.
| Decision | When it fits | What to record |
|---|---|---|
| Approve | The reviewed evidence supports the intended use, material controls are in place, and unresolved risks are within the organization’s tolerance. | Approved service configuration and use, evidence relied on, owner, review date, and change triggers. |
| Conditionally approve | Remaining gaps can be bounded with enforceable controls, restricted use, or time-limited remediation without exceeding risk tolerance. | Each condition, responsible owner, deadline, compensating control, and the consequence if it is not met. |
| Reject or defer | Material risks cannot be verified or reduced to an acceptable level, or the vendor cannot meet necessary data, security, or operational requirements. | Unacceptable risks or missing evidence, decision owner, and any requirements for reconsideration. |
For consistent comparisons, use the same criteria across vendors: data use and retention; access and isolation; assurance quality and scope; model and subprocessor provenance; change transparency; AI testing; incident response; resilience and exit; and fit for the intended use.
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