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Assess an AI vendor as a connected system and supply chain, not as a standalone model. Start by defining the use and potential impact, map every data flow and subprocessor, test privacy and security controls with dated evidence, negotiate precise data-use and incident terms, record residual risk, and monitor the service for as long as it is connected to your business.
This approach answers the questions that matter in practice: whether prompts and files are retained or used for training, where processing occurs, who can access the environment, how an incident will be handled, and whether the vendor can support deletion, audits, and an orderly exit.
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What does an AI vendor security assessment actually cover?
An assessment covers the whole service you will operate, including the model, application, APIs, infrastructure, data, people, subprocessors, support channels, and integrations. A polished model card or a SOC 2 report cannot by itself tell you how your tenant is configured, what happens to support tickets, or whether a prompt is copied into a debugging system.
Judge the vendor against your use case. A system that drafts internal notes has a different risk profile from one that ranks loan applicants, controls machinery, or produces medical advice. The same vendor may be acceptable for one workload and unsuitable for another because the data, decisions, jurisdiction, and consequences differ.
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- Business purpose: what the system does and why it is needed.
- People and decisions affected: employees, customers, patients, applicants, or the public, and whether a human makes the final decision.
- Connected systems: identity providers, file stores, ticketing tools, production databases, browsers, plug-ins, and automation.
- Data classes: public, internal, confidential, personal, sensitive, regulated, or secret information.
- Failure consequences: disclosure, manipulation, biased output, unsafe action, service outage, legal breach, or loss of evidence.
- Geography and law: countries of processing, transfers, customer locations, and retention obligations.
Use a written risk decision rather than a single pass/fail score. No authoritative comparative failure rate or universal threshold exists; set your threshold from data sensitivity, impact, legal duties, and your organisation’s risk appetite.
How do I define scope and impact before contacting a vendor?
Write a one-page scope statement before sending a questionnaire. It prevents a vendor from answering a generic enterprise form while the real deployment quietly uses higher-risk data or permissions.
Record the intended use
Describe the task, users, expected volume, connected systems, model features, and whether outputs are advisory or action-triggering. State what the system must never do, such as making a final employment decision or sending an external message without human approval.
Classify the information
List every input and output category, including attachments, images, audio, system prompts, retrieval indexes, generated files, logs, and support conversations. Mark direct identifiers, account credentials, health or financial details, trade secrets, and information about children or other vulnerable groups. If a field is not necessary, remove it before the request is sent.
Assess consequences
Consider confidentiality, integrity, availability, fairness, explainability, and safety. Ask what happens if the model leaks a record, invents an instruction, changes an output, becomes unavailable during a deadline, or produces a systematically biased result. Identify the person who owns each consequence and the controls that reduce it.
Use a lifecycle, not a one-time gate
The NIST Risk Management Framework describes a comprehensive, flexible, risk-based approach. Apply its sequence to the vendor relationship:
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- Prepare: establish governance, stakeholders, assets, threat assumptions, and risk tolerance.
- Categorize: classify the system and information by impact.
- Select: choose controls proportionate to that impact.
- Implement: configure the service, integrations, identity, retention, and safeguards.
- Assess: test controls and examine independent evidence.
- Authorize: approve, conditionally approve, restrict, or reject the use; name the risk owner.
- Monitor: watch incidents, changes, evidence, performance, and residual risk throughout the contract.
NIST SP 800-37 Rev. 2 (December 2018) describes assessment, authorization, and continuous monitoring as lifecycle activities. Treat the vendor as part of your system boundary once its API, plug-in, or staff can reach your information.
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How can I map every data flow and service dependency?
Draw the path of data from the person or system that creates it to every place it can be stored, transformed, viewed, or copied. Include ordinary operation and exceptional paths such as retries, backups, abuse investigations, support access, and disaster recovery.
Trace these artefacts
- Prompts, system instructions, uploaded documents, images, audio, and retrieved context.
- Model outputs, tool calls, generated files, citations, and feedback signals.
- Application logs, API gateway logs, safety logs, telemetry, analytics, and error traces.
- Temporary files, caches, indexes, backups, replicas, and export archives.
- Human support sessions, ticket attachments, screen recordings, and vendor training material.
- Model-improvement, evaluation, fine-tuning, and abuse-detection pipelines.
- All subprocessors, cloud regions, transfer routes, administrators, and support locations.
Use a diagram prompt that forces complete answers
For each interaction, draw: user or source system -> customer application -> identity and policy layer -> AI endpoint -> model and retrieval stores -> tools or downstream systems. Label every hop with data fields, purpose, region, encryption state, retention period, human access path, subprocessor, backup path, deletion trigger, and transfer mechanism. Draw separate arrows for prompts, outputs, logs, telemetry, support access, training use, and incident investigation.Ask the vendor to validate the diagram and identify anything it cannot disclose. An answer such as “the platform is secure” is not a map. Require a named owner and a date for each unresolved flow.
Does the AI company train on my data?
Do not infer the answer from a plan name, a marketing statement, or a model’s public training description. Obtain a written statement for your exact service, account type, API route, and support arrangement.
Ask what “use” means
- Are prompts, uploads, outputs, ratings, and error reports used to train or fine-tune shared models?
- Are they used for abuse detection, quality review, evaluation, or human labelling even when they are not called training data?
- Can a support engineer or subcontractor view content, and under what approval and logging rules?
- Do opt-out controls apply retroactively, to backups, and to data already copied into evaluation sets?
- Is customer-controlled fine-tuning isolated from other tenants and from the vendor’s general model-improvement pipeline?
Separate retention from training
A vendor can promise not to train on content and still retain it in request logs, safety systems, backups, or support tickets. Request the retention period for each store, the deletion trigger, the backup expiry process, and evidence that deletion is applied to indexes, caches, replicas, and derived datasets.
Make the answer testable
Have the contract state the permitted purposes in plain language, prohibit unapproved secondary use, identify the account setting that enforces the restriction, and require notice before the setting or policy changes. Ask for an export or audit record showing which retention and training controls were active for your tenant during a chosen period.
What privacy and minimisation checks should I perform?
Minimisation is both a design control and a procurement test. The Information Commissioner’s Office says: “The first step you should take towards compliance with data minimisation is to understand and map out all the ML processes in which personal data might be used.”
Reduce data before it leaves your boundary
- Remove names, account numbers, exact locations, credentials, and free-text details that are not needed for the task.
- Use tokenisation or pseudonymisation with the re-identification key kept under your control.
- Send only the relevant document sections and set limits on attachment size and history.
- Keep retrieval indexes, system prompts, and evaluation sets free of unnecessary personal data.
- Use synthetic or redacted records for development, demonstrations, and support reproduction.
Check rights and legal support
Ask how the vendor helps you locate, correct, export, restrict, or delete information about a person; how it handles a request when data is embedded in an index or fine-tuned model; and what evidence it supplies for completion. Clarify which party is controller, processor, or another role for each processing purpose, and who answers notices, complaints, and regulator questions.
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Protect training and test material
Request the provenance, access rules, retention, and deletion process for data used to test prompts, evaluate safety, tune a customer model, or investigate abuse. A vendor should be able to distinguish your content from public training material and explain how accidental inclusion is detected and corrected.
What security evidence should an AI vendor provide?
Request evidence that is specific to the service and current enough to describe the environment you will use. A questionnaire is an evidence request, not proof that controls operate.
| Control area | Evidence to request | What to verify |
|---|---|---|
| Architecture and tenancy | Current architecture diagram, trust boundaries, data-flow map, tenant-isolation design, and service inventory | Your plan, region, API path, and integrations are in scope; shared components and administrative paths are identified |
| Identity and access | Authentication options, single sign-on and multifactor support, role catalogue, privileged-access procedure, access reviews, and termination process | Least privilege, separate customer and operator roles, time-limited elevation, and logged administrative actions |
| Encryption and keys | Encryption in transit and at rest, key-management architecture, rotation practice, and customer-key options | Which stores are covered, who can decrypt, how keys are isolated, and what happens when a key is revoked |
| Logging and detection | Event catalogue, retention, tenant access to logs, alert triage, and time synchronisation | Prompts, exports, privilege changes, policy changes, and unusual access can be investigated |
| Vulnerability and patching | Secure-development policy, dependency management, scanning, remediation targets, and recent penetration-test scope | AI gateways, plug-ins, orchestration code, model-serving systems, and administrative planes are included, not just a public web front end |
| Incident response | Response plan, severity definitions, notification workflow, exercises, forensics process, and customer communications template | There is a named contact, evidence-preservation process, and support for your legal and regulatory duties |
| Resilience and recovery | Availability design, backup and restore tests, recovery objectives, regional failure plan, and dependency map | Backups receive the same access and deletion controls and the service can be exited without losing your records |
| Independent assurance | SOC 2 or ISO 27001 certificate/report, statement of applicability, control period, scope, exceptions, and remediation plans | The exact product, region, subprocessors, and control objectives you rely on are covered and the report is recent |
Send a concrete evidence request
Ask the vendor to provide, for the proposed service and account type, its current architecture and data-flow diagrams; processing locations and transfer mechanisms; subprocessor register; retention and deletion schedule; training-use policy; role and privileged-access descriptions; encryption and key-management summary; vulnerability and penetration-test executive summaries with scope and dates; incident plan and notification commitments; backup and recovery test summary; independent assurance reports with exceptions; model, dataset, prompt, and dependency inventories; change-notification procedure; and a response to every unanswered item with an owner and due date.
For sensitive or high-impact use, require a controlled review of the relevant reports under confidentiality terms rather than accepting a logo or certificate alone. Record report dates, scope boundaries, qualifications, and open findings.
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AI security extends beyond conventional infrastructure. The model, prompts, datasets, orchestration code, plug-ins, and suppliers can change one another’s risk.
Require inventories and change control
Request inventories for models and versions, training or fine-tuning datasets, system prompts, retrieval sources, open-source and commercial dependencies, tools, and subprocessors. Ask how the vendor approves changes, tests them, records provenance, and notifies customers when a material change affects behavior, location, permissions, or retention.
Test abuse and prompt-injection handling
- How are untrusted documents prevented from overriding system instructions?
- Can a retrieved page cause an external action, data exfiltration, or privilege escalation?
- Are tool calls allow-listed, rate-limited, sandboxed, and subject to human approval for consequential actions?
- How are jailbreaks, poisoned data, model extraction, credential theft, and denial-of-service attempts detected and contained?
- Can administrators disable a model, connector, or tool without destroying evidence needed for an investigation?
Apply the supply-chain rule across the lifecycle
National Cyber Security Centre guidance states: “You assess and monitor the security of your AI supply chains across a system’s life cycle, and require suppliers to adhere to the same standards your own organisation applies to other software.” Put that requirement in the contract and flow it down to subprocessors. Verify that a vendor’s “managed” component still has an owner, patch process, vulnerability channel, and end-of-life plan.
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What should an AI vendor security questionnaire include?
Organise questions by decisions you must make, not by a vendor’s marketing categories. Ask for a yes-or-no answer, an explanation, the control owner, the evidence location, and the date for every material item.
| Question set | Sample questions | Required follow-up |
|---|---|---|
| Data use | What inputs, outputs, logs, feedback, and support material are retained? Are any used for training, evaluation, abuse detection, or human review? | Policy text, tenant setting, retention schedule, deletion evidence, and exception process |
| Location and access | Where are processing, storage, backups, and support performed? Which personnel and subprocessors can access content? | Region list, transfer mechanism, access roles, approval and logging records |
| Isolation | How are tenants, customer keys, indexes, fine-tuned models, and tool permissions separated? | Architecture description, test scope, and results or assurance covering isolation |
| Model behaviour | How are accuracy, bias, explainability, unsafe output, and human review addressed for this use? | Evaluation method, known limitations, escalation path, and change history |
| Resilience | What happens during an outage, model rollback, region loss, or supplier failure? | Recovery objectives, test date, dependency list, export format, and exit procedure |
| Incident handling | How are suspected exposure, prompt injection, malicious output, and control failure detected and reported? | Severity definitions, clock start, notification channel, evidence preservation, and customer support obligations |
| Governance | Who owns security, privacy, model risk, subprocessor approval, and customer complaints? | Named functions, review cadence, audit rights, and escalation contacts |
Do not let a completed questionnaire close the assessment. Sample answers, test a low-risk integration, inspect evidence, and mark each answer as verified, partially verified, not applicable with justification, or unresolved.
How should I compare two or more viable AI vendors?
Compare the residual risk of the complete deployment, not a feature list or an assurance badge. Use the same questions, evidence dates, and workload assumptions for every candidate.
| Comparison axis | Questions to answer | Decision implication |
|---|---|---|
| Retention and training | Are restrictions contractual and technically enforced? Can data be deleted from logs, backups, indexes, and derived sets? | Prefer the option with the clearest purpose limitation and verifiable deletion |
| Processing and support geography | Which regions, transfers, subprocessors, and support locations are involved? | Reject a route that conflicts with legal, customer, or residency requirements |
| Access and isolation | Are customer and operator privileges separated, logged, reviewed, and limited? | Weight direct control over keys, connectors, and administrative access |
| Evidence quality | Are reports independent, recent, correctly scoped, and clear about exceptions and remediation? | Discount broad certificates that omit your product, region, or control path |
| AI transparency | Can the vendor identify models, datasets, prompts, dependencies, limitations, and material changes? | Choose the service that makes behaviour and supply-chain change observable |
| Integration safety | Can you enforce least privilege, rate limits, approval gates, and monitoring on tools and APIs? | Limit or redesign automations that can act without a human checkpoint |
| Business impact | What accuracy, bias, explainability, and human-review controls fit the decision? | Do not approve a high-impact use merely because infrastructure controls are strong |
| Contract and exit | Are incident duties, availability, remedies, portability, deletion, and change notice enforceable? | Prefer a provider that lets you leave without stranded data or unsafe downtime |
Keep a written rationale for trade-offs. A lower price or better benchmark does not compensate for an unacceptable exposure of regulated data or an unreviewable automated decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should be in an AI vendor contract?
Put the operational answer—not a link to a policy that can change unilaterally—in the agreement and its security or data-processing schedule.
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- Subject matter, duration, business purpose, data types, categories of people, and processing locations.
- Roles and documented instructions, confidentiality duties, and a ban on unrelated secondary use.
- Whether prompts, outputs, logs, feedback, support material, and derived data may be used for training, evaluation, safety, or human review.
Control the chain
- Named or clearly bounded authorised subprocessors, their functions and locations, and advance notice of material additions or changes.
- Flow-down security, confidentiality, deletion, incident, and audit obligations to every subprocessor.
- Notice and an objection or termination remedy when a material change creates unacceptable risk.
Specify security and incidents
- Agreed technical and organisational measures for identity, isolation, encryption, key management, logging, vulnerability handling, secure development, resilience, and backups.
- Incident definitions covering confidentiality, integrity, availability, malicious prompts, unsafe tool actions, and control failures.
- Notification channel, clock start, update frequency, evidence preservation, root-cause information, cooperation with regulators and affected people, and allocation of response costs.
- Service levels for availability and support where the use warrants them, with remedies for repeated failure.
Preserve rights and exit
- Assistance with access, correction, deletion, restriction, portability, and investigations.
- Deletion or return of customer data at the end of the service, including logs, temporary files, indexes, replicas, backups, and derived datasets, with a completion record.
- Audit or review rights proportionate to risk, including access to independent reports, exceptions, remediation status, and targeted testing.
- Export formats, transition support, notice of model or interface retirement, and a safe shutdown process for connected tools.
Have counsel align these clauses with applicable privacy, sector, employment, safety, and records-retention duties. A contract cannot make an unsafe architecture safe, but it can make the vendor’s obligations measurable and enforceable.
How do I document the authorization and residual-risk decision?
After evidence review, record what remains uncertain and who accepts it. Do not hide unresolved items inside a questionnaire percentage.
| Decision-record field | What to write |
|---|---|
| Use and impact | Purpose, users, affected people, decision significance, data classes, jurisdictions, and connected systems |
| Controls relied on | Specific vendor and customer safeguards, configuration settings, human-review gates, and evidence dates |
| Open gaps | Missing scope, untested isolation, unclear retention, pending subprocessor review, or contract language not yet agreed |
| Residual risk | Plain-language description of the remaining disclosure, manipulation, outage, bias, or legal exposure |
| Owner and treatment | Named risk owner, mitigation, deadline, trigger for reapproval, and whether production access is restricted |
| Decision | Authorize, authorize with conditions, pilot only, defer, or reject, with the reason |
For a high-impact use, keep production access blocked until the required evidence and contract terms are complete. If you authorize with conditions, turn each condition into a dated control task and make failure a trigger to suspend or narrow the integration.
How often should an approved AI vendor be monitored?
Monitoring starts on the first production request. Set a cadence that matches impact and change velocity, and add event-driven reviews for incidents or material changes.
| Cadence | Checks |
|---|---|
| Every request or session | Enforce data minimisation, identity, connector permissions, rate limits, human approval, and logging; block prohibited content and destinations |
| Monthly or another risk-based interval | Review access, unusual usage, retention settings, incidents, model behaviour complaints, and unresolved control tasks |
| At each material model, API, region, subprocessor, or policy change | Re-run the data-flow and impact analysis, inspect release notes and tests, and decide whether authorization still holds |
| At least annually, and more often for high-impact uses | Refresh assurance reports, penetration-test scope, risk assessment, legal analysis, disaster-recovery evidence, and contract contacts |
| After an incident or near miss | Preserve evidence, contain access, assess affected data and decisions, notify as required, correct the cause, and record whether the service remains authorized |
Keep an inventory of model versions, prompts, datasets, dependencies, connectors, subprocessors, and owners so that a change can be traced to affected workloads. NIST’s lifecycle model treats monitoring as a continuing responsibility, not a renewal formality.
What do SOC 2, ISO 27001, and questionnaires prove?
They can support diligence, but none answers your exact data-use and deployment questions without scope and control evidence. A SOC 2 report may cover a service boundary that excludes the model, support tooling, or a region you will use. An ISO 27001 certificate confirms a management system within a stated scope; it does not promise that your tenant is configured correctly or that the vendor will not use prompts for a secondary purpose. A questionnaire records the vendor’s answers, not independent operation of every control.
Read the scope, period, control objectives, testing method, exceptions, complementary customer controls, and remediation notes. Match them to your architecture and contract, then test the controls that matter most to your data and impact.
What are the most common assessment mistakes?
- Assessing only the model: the application, logs, tools, support desk, and subprocessors often handle more data than the model endpoint.
- Accepting “no training” as “no retention”: separate training, logging, abuse review, backups, and support copies.
- Relying on a certificate without scope: verify product, region, period, exceptions, and customer responsibilities.
- Sending real sensitive data during a pilot: use synthetic or redacted records until controls and terms are approved.
- Ignoring change: a new model, connector, region, or subprocessor can alter risk without a new vendor name.
- Using a blanket questionnaire score: weigh each gap against the actual data, impact, and compensating controls.
- Leaving exit until renewal: test export, deletion, backup expiry, and connector shutdown before production dependence grows.
- Approving automation without human limits: require allow-listed actions, least privilege, rate limits, monitoring, and an emergency disable path.
Frameworks that help structure this work include ISO/IEC 23894:2023, published in February 2023; NIST SP 800-37 Rev. 2, published in December 2018; and the Data & Trusted AI Alliance Cyber Readiness Companion, published in October 2025. Use them to organise evidence and decisions, not as substitutes for deployment-specific testing.
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Frequently Asked Questions
Can I run an AI vendor pilot with production data?
Use synthetic, masked, or tightly minimised records until the vendor’s retention, training-use, access, and deletion terms are verified. If a production pilot is unavoidable, isolate it in a separate tenant, remove direct identifiers, disable unnecessary connectors, set an explicit end date, and obtain written approval from the data owner.
How should a small company handle a vendor that will not share a penetration-test report?
Ask for an executive summary, test date and scope, material findings, remediation status, and an independent assurance report under confidentiality terms. Compensate for missing detail with narrower permissions, stronger customer-side monitoring, a limited pilot, and a contract right to review additional evidence after an incident or material change.
What changes require a fresh AI vendor assessment?
Reassess when the model or API behavior changes materially, a new connector can read or write data, processing moves to another region, a subprocessor is added, training or retention terms change, or the system begins influencing a higher-impact decision. Minor bug fixes can follow normal change monitoring if they do not alter these boundaries.
Should open-source model components be assessed differently?
Assess the same data flows and operational controls, then add maintainer trust, license obligations, provenance, vulnerability response, update authenticity, and the ability to pin or roll back versions. “Open source” does not identify who receives telemetry, who patches a dependency, or who is liable when the component is embedded in a hosted service.
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