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Assess an AI vendor as a connected system and supply chain, not as a model in isolation. Start by defining the use and potential impact, map every prompt, upload, output, log, backup, support path and subprocessor, then verify controls with dated evidence. Confirm exactly whether your data is retained or used for training, negotiate precise security and incident terms, record residual risk, and monitor the service for the life of the contract.

A security questionnaire, SOC 2 report or ISO 27001 certificate can support diligence, but none answers your organization’s specific data-use, deployment and access questions without checking scope, exceptions and operating evidence.

What does a thorough AI-vendor assessment cover?

The unit of analysis is the whole service: model, application code, datasets, integrations, people, cloud infrastructure, subprocessors and the workflows that rely on outputs. A vendor can have strong perimeter security while exposing sensitive information through retained prompts, support tickets, telemetry, a poorly controlled plug-in or a fourth-party service.

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Use a risk-based lifecycle. NIST describes its Risk Management Framework as “A Comprehensive, Flexible, Risk-Based Approach.” For an AI purchase, that means linking the intended use and affected people to evidence, an authorization decision and continuing oversight rather than treating procurement as a one-time checklist.

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Lifecycle activity What to produce for this AI service
Prepare Business purpose, users, connected systems, data classes, jurisdictions and owners.
Categorize Impact of outage, disclosure, manipulation, incorrect or biased output, and legal or regulatory consequences.
Select Shortlist vendors against data-use, security, assurance, model and contract requirements.
Implement Configure least privilege, retention, logging, human review, deletion and safe integration settings.
Assess Test the configured service and examine independent reports, exceptions and remediation evidence.
Authorize Approve, reject or condition use; name the risk owner and document residual risk.
Monitor Track incidents, model or subprocessor changes, control performance and periodic reassessment.

This sequence follows the lifecycle in NIST SP 800-37 Rev. 2 (December 2018). ISO/IEC 23894:2023 (published February 2023) provides a risk-management reference for AI, while the Data & Trusted AI Alliance Cyber Readiness Companion (October 2025) adds current supply-chain considerations.

How do I scope the use and its impact before contacting vendors?

Write a one-page use case before sending a questionnaire. The scope determines what evidence is relevant and how much residual risk the organization can accept.

Record the intended use

  • Describe the business purpose and expected users, including administrators, developers, contractors and automated agents.
  • List every decision the output can influence. Separate assistive drafting from decisions about employment, credit, health, safety, access or legal rights.
  • Identify systems connected through APIs, plug-ins, browser extensions, file stores, identity providers, ticketing tools and data warehouses.
  • Classify data entering, leaving or being generated by the service: public, internal, confidential, personal, special-category, regulated, proprietary or security-sensitive.
  • List countries in which data subjects, users, infrastructure and support staff are located, and the jurisdictions that may receive transfers.

Describe consequences, not just likelihood

Assess the effect of an outage, unauthorized disclosure, malicious manipulation, prompt injection, model failure, discriminatory output and loss of audit records. Note who could be harmed, how quickly harm would occur, whether a human can catch it, and whether operations can continue without the vendor. This impact statement gives procurement, privacy, security and the business a common decision basis.

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How do I map where AI data goes?

Trace the complete data and service chain rather than stopping at the vendor’s public API endpoint. The Information Commissioner’s Office advises understanding and mapping all machine-learning processes in which personal data might be used; that includes transient processing and operational data, not only training datasets.

Inventory each data object and handling event

Stage to map Questions to answer
Prompt and upload Which fields and files are sent, by which users or automations, and are they filtered or redacted first?
Inference and intermediate files Are prompts, embeddings, uploaded documents, cached context or temporary artifacts persisted? For how long and in which systems?
Output Where are responses stored, who can export them, and can generated content contain confidential or personal information?
Logs and telemetry Do application, abuse, analytics, debugging or performance logs contain prompts, outputs, identifiers or metadata? Who can query them?
Backups and disaster recovery What copies exist, where are they located, how long do they remain and how does deletion propagate to them?
Support access Can vendor staff or contractors view content? Is access time-limited, approved, recorded and limited to the minimum data?
Model improvement Is customer content used for training, fine-tuning, evaluation, abuse detection or human review, and what setting or contract term controls each use?
Subprocessors and transfers Which providers host models, storage, monitoring, support or content moderation, and what countries receive data?

Use a diagram prompt that exposes hidden paths

Ask the vendor to complete a diagram with these nodes and trust boundaries:

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User or system → client application → API gateway → model or retrieval service → output store
↘ logs and telemetry
↘ abuse review or human support
↘ backups and disaster recovery
↘ subprocessors and cross-border transfers

For every arrow, require the data fields, encryption state, retention period, access roles, location, subprocessors and deletion behavior. Ask the vendor to mark optional components that can be disabled, because an apparently unused feature may still receive metadata.

How do I minimize personal and confidential data?

Data minimization is a design decision, not a promise made after deployment. The ICO’s procurement guidance says the first step toward compliance is to understand and map every machine-learning process in which personal data might be used.

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Reduce what leaves your environment

  • Remove direct identifiers and unnecessary free-text fields before sending a prompt.
  • Pseudonymize records with a separately protected lookup table when the model does not need names or contact details.
  • Send the smallest document excerpts and the shortest retention window that still supports the task.
  • Keep high-risk source data in systems you control and expose only narrowly scoped retrieval results.
  • Disable conversation history, analytics, human review and model-improvement options when the use case does not require them.

Check the full data lifecycle

Ask how the vendor protects training and test data, intermediate files, embeddings, caches, logs and backups. Require a method and timeline for deletion or return, including residual copies created for disaster recovery. Confirm how the vendor supports access, correction, deletion and other rights requests, and who handles requests involving generated outputs or derived profiles.

What should an AI vendor security questionnaire request?

A questionnaire should request evidence and precise answers, not merely yes/no assertions. Set a response owner for each topic and ask for report dates, scope, system boundaries, exceptions and remediation status.

Security architecture and access

  • Provide the current architecture and trust-boundary diagram, including model hosts, storage, administrative planes, support tooling and subprocessors.
  • Describe identity federation, phishing-resistant authentication options, privileged-access management, service accounts, tenant isolation and access reviews.
  • State encryption in transit and at rest, key ownership, rotation, separation of duties and any customer-managed-key option.
  • Show what is logged, how logs are protected from alteration, retention periods, alerting and customer access to relevant records.

Software and infrastructure assurance

  • Explain vulnerability discovery, patch deadlines, dependency and container scanning, penetration-testing scope, findings and remediation.
  • Describe secure development, code review, release approval, secrets management and change control for models, prompts, datasets and infrastructure.
  • Provide independent assurance reports or certifications with the audit period, in-scope services and locations, control exceptions and management responses.
  • Explain incident response, customer notification triggers, evidence preservation, communications roles, backup restoration and disaster-recovery testing.

AI-specific evidence

  • Supply inventories for models, datasets, prompts, dependencies, external tools and subprocessors, with owners and versions.
  • Explain how prompt injection, data poisoning, insecure output handling, abuse and model-access attacks are detected and contained.
  • Describe evaluation for accuracy, bias, robustness and explainability, including where human review is mandatory and how users can challenge an output.
  • Give notice periods and rollback procedures for material model, dataset, dependency, feature, location or subprocessor changes.

Ask for evidence that demonstrates operation over time, such as access-review samples, patch metrics, incident exercises, deletion records and penetration-test remediation. A questionnaire can identify gaps, but a completed form is not proof that controls operate.

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How can I verify whether the vendor trains on my data?

Do not accept a broad statement such as “we do not train on customer data” without defining customer content and every related use. Ask separately about prompts, files, outputs, embeddings, metadata, abuse monitoring, human review, fine-tuning, evaluation sets and product analytics.

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Require the answer to identify the default setting, available opt-out or contractual restriction, retention period, personnel or supplier access, and whether data can be used after account closure. Confirm that the restriction applies to all relevant products, regions, support channels and subprocessors, not only the primary model endpoint. Test the configured setting with a non-sensitive record and retain the vendor’s written confirmation.

How should I evaluate AI supply-chain and model risks?

AI security extends beyond the vendor’s own code. The UK National Cyber Security Centre advises assessing and monitoring AI supply chains across a system’s lifecycle and requiring suppliers to meet the same standards applied to other software.

Inspect the dependency chain

  • Identify foundation-model providers, hosted inference, vector databases, content filters, observability tools, cloud regions, support contractors and fourth parties.
  • Ask how each supplier is assessed, what security obligations flow down, and how a supplier incident reaches you.
  • Require notification before adding or materially changing a subprocessor, with a practical objection or exit route where risk cannot be accepted.
  • Check software bills of materials or equivalent dependency inventories, signing and provenance controls, and the process for urgent vulnerability fixes.

Control model and integration exposure

  • Limit model and administrative access by role, workload, network and purpose; apply rate limits and separate development from production credentials.
  • Validate outputs before they trigger code execution, payments, records changes, messages or high-impact decisions.
  • Monitor for prompt injection, anomalous extraction, excessive tool calls, data exfiltration and sudden quality or bias changes.
  • Keep a human accountable for consequential decisions and define a safe fallback when the model or vendor is unavailable.

What should the AI vendor contract contain?

Translate questionnaire answers into enforceable duties. The agreement, data-processing terms and security schedule should align; a marketing page or policy that conflicts with the contract is not a dependable control.

Define processing precisely

  • State the subject matter, duration, purpose, data types, categories of affected people, documented instructions and each party’s role.
  • Prohibit unrelated sale, advertising, profiling, model training or human review unless expressly authorized for the defined purpose.
  • Require confidentiality for personnel and contractors, least-privilege access, appropriate encryption, tenant isolation and secure disposal.
  • List approved subprocessors, processing locations and transfer mechanisms; require advance notice of material changes and a response to objections.

Set operational obligations

  • Specify incident-notification deadlines, the facts to be supplied, evidence preservation, cooperation with investigation and support for required notices.
  • Require assistance with access, deletion, correction, restriction and other rights requests, plus audit information needed to demonstrate compliance.
  • Define deletion or return at termination, including backups, caches, logs, embeddings and copies held by subprocessors.
  • Set service levels for availability, incident response and critical control failures where the use case warrants them, with meaningful remedies.
  • Reserve review, audit or independent-assessment rights proportionate to risk, and require notice of material architecture, model, location or control changes.

How do I make the authorization and residual-risk decision?

Approval should name an accountable owner and record what remains uncertain after safeguards. Do not convert a questionnaire into a universal pass score: no authoritative material establishes a vendor-failure rate or a universal threshold.

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Use a decision record

  1. Summarize the use, affected people, data classes, jurisdictions, integrations and impact rating.
  2. List required controls and the evidence reviewed, including report period, scope, exceptions and open remediation.
  3. Record unresolved gaps, their plausible consequence, likelihood judgment, compensating controls and the person accepting the risk.
  4. Set conditions such as no personal data, limited pilot users, human approval, blocked integrations, shorter retention or a deadline for missing evidence.
  5. Choose approve, approve with conditions, defer pending evidence, or reject; state the next review date and triggers for earlier review.

Keep production access restricted until the evidence and configuration meet the decision’s conditions. A low-impact drafting tool may tolerate a different residual risk from a system that determines eligibility, safety actions or regulated records.

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How should I compare two vendors that appear viable?

Compare like-for-like scope and configuration. A certification covering a hosting environment does not automatically cover the AI feature, subprocessors or your selected region.

Comparison axis Evidence-based decision test
Retention and data use Which content is retained, for how long, for which purposes, and can the restriction be enforced contractually?
Locations and access Can you identify processing countries, transfers, subprocessors and support access for the chosen edition?
Technical controls Are identity, tenant isolation, encryption, key management, logging and incident response demonstrated for the in-scope service?
Independent assurance Are reports recent, relevant to the service, clear about exceptions and accompanied by remediation?
Transparency and change Does the vendor inventory models, datasets, prompts and dependencies and provide usable change notice?
Integration safety Can permissions, rate limits, tool execution, environment separation and monitoring be limited to the task?
Model impact Are accuracy, bias, explainability, human review and fallback controls appropriate to the decision?
Contract and exit Are breach timelines, availability commitments, remedies, deletion, portability and exit assistance explicit?
Residual risk Which vendor leaves less unowned risk for this data sensitivity and business impact, not merely the higher certification count?

How do I monitor the vendor after approval?

Make monitoring a standing control owner’s job. Review security incidents, near misses, access anomalies, deletion requests, availability, control metrics, model behavior and complaints on a cadence proportionate to impact.

Trigger an out-of-cycle review when

  • The vendor changes a model, dataset, dependency, subprocessor, processing country, retention behavior or support process.
  • There is a breach, material vulnerability, unexplained output shift, prompt-injection event, privacy complaint or failed recovery test.
  • Your organization connects a new system, expands users, adds sensitive data or lets outputs drive a higher-impact decision.
  • An assurance report expires, gains a material exception or no longer covers the service and region you use.

At each scheduled review, reconcile the data-flow diagram with the current configuration, sample access and deletion records, confirm training-use settings, check open remediation and refresh the residual-risk decision. Keep an exit plan with export format, deletion verification, replacement workflow and a safe way to disable integrations.

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What are the most common assessment mistakes?

Relying on a certificate as a verdict

SOC 2 or ISO 27001 can provide useful independent evidence, but scope, period, exceptions and control operation matter. Request the report and map its controls to your exact AI feature, region and data flows.

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Asking only whether data is used for training

Training is one possible use. Retention, abuse detection, human review, analytics, embeddings, backups and subprocessors can expose data even when model training is prohibited. Ask about each handling event separately.

Ignoring support and recovery copies

Support engineers, contractors, logs and backups often sit outside the primary product description. Include them in the diagram, access review and deletion requirement.

Accepting vague change language

“We may update the service” is not a risk control. Define material changes, notice timing, objection rights, rollback or exit assistance, and the evidence you receive after a change.

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Scoring without impact context

A single score hides the difference between a low-impact writing assistant and a system handling sensitive records or consequential decisions. Tie acceptance thresholds to data sensitivity, legal duties, harm and the organization’s risk appetite.

Frequently Asked Questions

Can a small company assess an AI vendor without a dedicated security team?

Yes. Assign one business owner and one technical or privacy reviewer, use the data-flow and evidence-request checklists, and escalate only high-impact gaps. A narrowly scoped pilot with synthetic or redacted data can validate deletion, access and integration behavior before production approval.

What proof should I request that deletion actually works?

Ask for the documented deletion workflow, retention schedule, backup treatment, subprocessor obligations and a recent deletion record or test result for the relevant service. Confirm the time to complete deletion and how the vendor reports exceptions or legal holds.

How can I test for prompt-injection risk safely?

Use a non-production tenant and benign test documents containing instructions that attempt to override system rules or exfiltrate connected data. Verify that retrieval, tool permissions, output validation, logging and human approval prevent the test from reaching protected systems.

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Who should own the residual-risk decision?

The owner of the business process should accept business impact, with security, privacy, legal and procurement providing documented review. The record should name that person, the conditions of approval and the events that force reconsideration.

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