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A small IT team can assess an AI vendor without a large compliance department by using a repeatable, evidence-based workflow: map the service and data flows, classify the business impact, verify security and privacy controls, limit exposure, put requirements in the contract, run a constrained pilot, and monitor changes. NIST’s AI Risk Management Framework (AI RMF) and Generative AI Profile provide the governance structure; Federal Trade Commission (FTC) guidance turns vendor-security expectations into practical contract and oversight actions; UK Information Commissioner’s Office (ICO) guidance explains minimisation and processor terms; and CISA’s April 15, 2024 joint guidance focuses on confidentiality, integrity, availability, vulnerability mitigation, and response. The result should be a documented decision with explicit residual risks and an exit plan—not a one-time questionnaire.
What does an AI vendor security assessment need to establish?
The assessment should establish four things: what the service receives and produces, what could go wrong, which controls reduce those risks, and whether your organisation can enforce and verify those controls. AI adds uncertainty because a provider may use opaque models, change model versions, rely on multiple subprocessors, or reuse customer content for training and evaluation. NIST warns that a supplier’s risk tolerance may not match the acquiring organisation’s, so accepting a vendor’s standard terms without analysis is not due diligence.
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How do we inventory an AI service and its data flows?
Start with a one-page inventory before sending the vendor a questionnaire. The inventory defines the service boundary and exposes unknown destinations or users that a generic security review can miss.
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| Inventory field | What to record | Why it matters |
|---|---|---|
| Provider and product | Legal entity, product name, model or API, edition, tenant and region | Different models, regions and editions can have different retention and security terms. |
| Business owner | Accountable department, process owner and technical administrator | Someone must approve the use case, answer incidents and remove access. |
| Users and integrations | Employees, contractors, customers, applications, plug-ins and service accounts | Every identity and integration expands the attack surface. |
| Input data | Prompts, files, images, code, telemetry, identifiers and metadata | Prompts and logs can contain sensitive information even when uploaded files do not. |
| Output and onward flow | Where results go, who receives them, and whether they enter another system | Outputs may become records, decisions or training data in a downstream process. |
| Subprocessors and hosting | Model providers, cloud hosts, support vendors, locations and transfer routes | Security, legal jurisdiction and deletion obligations can differ across the chain. |
| Business dependency | Whether the service is optional, operationally important or mission-critical | Critical uses require continuity and fallback arrangements before approval. |
NIST recommends maintaining an inventory of third parties with access to organisational content and an approved-provider list. Treat an AI plug-in, embedded assistant or API call as a vendor relationship even when it appears inside another application.
How should a small team classify the use case and impact?
Classify the data and the consequences separately. A low-sensitivity prompt can still create high impact if a wrong answer controls payment, safety, employment, access or legal advice. Consider disclosure, manipulation, outage, and harmful or inaccurate output.
Suggested impact bands
| Band | Typical use | Minimum handling |
|---|---|---|
| Low | Public information summarisation or drafting with no personal or confidential data | Approved account, basic access control, human review and ordinary incident reporting. |
| Moderate | Internal material, limited personal data, software code or customer-support drafts | Documented data rules, stronger authentication, restricted users, logging, retention limits and a tested fallback. |
| High | Regulated records, sensitive personal data, confidential intellectual property, financial decisions or mission-critical workflows | Security and privacy evidence, contractual restrictions, technical isolation, formal approval, pre-production testing, continuous monitoring and an explicit stop condition. |
For each band, write a short harm statement: “If this data is disclosed, altered, unavailable, or used to produce a wrong output, the likely effect is …” Include legal, contractual, safety, financial, reputational and operational consequences. If you cannot describe the consequence, the use case is not ready for approval.
What evidence should we request from an AI vendor?
Ask for evidence that maps to the risks in your inventory, not a logo page or an undifferentiated questionnaire. CISA’s joint guidance (April 15, 2024) frames the objective as improving the confidentiality, integrity and availability of AI systems, with vulnerability mitigation and protection, detection and response.
Evidence request checklist
- Architecture and data-flow diagrams: Show the application, model, storage, logging, support access, subprocessors, regions and transfer paths. Ask which paths are optional and can be disabled.
- Security assurance: Request current independent attestations or assessments, the covered service and locations, exceptions, test dates and remediation status. A certificate without scope and date is not enough.
- Identity and tenant isolation: Ask how administrators and support staff authenticate, how tenants are separated, how privileged access is approved and reviewed, and whether customer-managed identity integration is available.
- Encryption and keys: Clarify encryption in transit and at rest, key ownership, rotation, hardware-security-module use where relevant, and what happens to encrypted backups after deletion.
- Logging and detection: Establish which administrative, authentication, data-access and model events are logged, how long logs are kept, who can view them and how customers export them.
- Vulnerability and secure development: Request the vulnerability-disclosure process, penetration-test summary, dependency and open-source management, patch targets, code-review practices and protections against prompt injection or malicious files.
- Incident history: Ask for material security and privacy incidents, affected services, dates, root causes, customer notification and corrective action. If the vendor declines details, record the limitation.
- Resilience: Obtain recovery objectives, backup design, regional redundancy, status communication, service-level commitments and an export method that works during an outage.
- Model governance: Ask how model versions, system prompts, safety controls, evaluations and content-provenance mechanisms are changed and documented.
Request an evidence-refresh commitment. A review based on a two-year-old assessment can be misleading when the provider has changed its model, hosting region or subprocessors.
Will the AI vendor train on our data or use it for another purpose?
Do not infer the answer from a marketing statement such as “your data is private.” Ask whether customer content is used to train, fine-tune, evaluate, test, monitor, improve or otherwise develop any model, and whether the answer differs for prompts, uploaded files, outputs, telemetry, abuse-monitoring data and human support tickets.
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Questions that force a usable answer
- Which data elements are collected at each stage, including metadata and diagnostic logs?
- Is secondary use enabled by default, optional, or prohibited for this product and account type?
- Can the restriction be made contractual and apply to subprocessors and model providers?
- Are humans allowed to view content for support, safety review or quality evaluation? Under what approval and masking rules?
- Are de-identified or aggregated derivatives retained, and what makes re-identification unlikely?
- Does the policy change when a customer enables a custom model, retrieval index, plug-in or fine-tuning feature?
Keep the vendor’s written answer with the approved configuration. If a dashboard switch disables training, record who can change it, whether the switch applies retroactively, and how you will detect an accidental reset.
What should we ask about retention, deletion and backups?
Retention is a lifecycle, not a single number. Obtain separate periods for active content, prompts and outputs, application logs, abuse-monitoring copies, support systems, backups, disaster-recovery replicas and legal holds.
Deletion terms to verify
- What event starts the retention clock: request, account closure, object deletion or contract termination?
- Which systems are covered, and which copies are excluded until a backup cycle expires?
- Can the vendor provide deletion or return evidence, including a completion date and affected locations?
- What happens to data placed under a legal hold or preserved for an investigation?
- How are customer-managed keys, indexes, vector stores, cached files and derived embeddings removed?
- Can the customer set a shorter period or disable history and diagnostic storage?
ICO guidance notes that machine-learning data is often copied from its original context and stored in multiple formats and locations, including with third parties. Map those movements, delete unnecessary intermediate files, and use de-identification or privacy-enhancing techniques where practical.
How can we minimise exposure before approving the vendor?
Minimisation is the most reliable control for a small team because it reduces both breach impact and the amount of vendor behaviour you must trust.
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- Define the minimum fields. Remove names, account numbers, exact dates, secrets, credentials, access tokens and unrelated attachments. Replace identifiers with consistent tokens when the task does not require identity.
- Separate environments. Use a test tenant or project for experimentation. Keep production records and administrative functions outside the AI workspace unless the high-impact review explicitly permits them.
- Restrict identities. Use single sign-on or strong multifactor authentication, groups tied to job roles, short-lived service credentials and separate administrator accounts. Deny bulk export unless the workflow requires it.
- Constrain integrations. Approve only the repositories, mailboxes, storage locations and plug-ins needed for the use case. Review scopes before enabling retrieval or autonomous actions.
- Control transfer files. Store temporary uploads in an approved location, set an automatic expiry, and delete local copies and exports after the task.
- Require human review. Keep a person accountable for decisions, external communications, code merges, financial actions and any output affecting an individual.
These measures align with ICO recommendations to document data movement, remove unnecessary intermediate files and consider de-identification. They also make a later vendor change less disruptive because fewer systems depend on the service.
Which security controls matter most for an AI service?
Prioritise controls that protect confidentiality, integrity and availability across the full path from prompt to output. A model’s accuracy does not compensate for weak account security or an inability to remove customer data.
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| Control area | Questions for the vendor | What to verify internally |
|---|---|---|
| Authentication and authorisation | Are multifactor authentication, SSO, role separation, session controls and API-scoped credentials supported? | Use least privilege, separate service accounts and rapid offboarding. |
| Isolation | How are tenants, projects, retrieval indexes, caches and fine-tuning artefacts separated? | Do not mix datasets or grant a shared account wider access than needed. |
| Encryption and key management | What is encrypted, who controls keys, and how are keys rotated and revoked? | Protect local exports and secrets; never place credentials in prompts. |
| Integrity and provenance | How are model versions, system prompts, datasets and plug-ins changed and authenticated? | Pin approved versions where possible and test material changes. |
| Detection and response | What events trigger alerts, how quickly are customers notified, and can logs be exported? | Route alerts to an owner and rehearse account revocation and data containment. |
| Availability and recovery | What are recovery objectives, dependencies, status channels and export options? | Maintain a manual or alternate process for critical work. |
Ask specifically about prompt injection, malicious documents, data exfiltration through tool use, insecure plug-ins, model supply-chain changes and unauthorised support access. The right answer is a layered control set, not a promise that the model itself cannot be attacked.
What security and privacy clauses belong in the AI contract?
Put the controls in the agreement, order form, data-processing addendum or security schedule that actually governs the service. FTC guidance says that if specific security standards matter, they should be stated precisely and treated as non-negotiable; it also advises defining how vendors may use, share, sell, retain and delete data.
Core contract provisions
- Purpose limitation: Permit processing only to deliver the named service and documented support. Prohibit sale, advertising use, unrelated profiling and model training or improvement unless your organisation gives explicit written permission.
- Data definition: Cover prompts, files, outputs, embeddings, telemetry, identifiers, logs, support records and derived data, not only “customer content.”
- Roles and instructions: Identify controller and processor responsibilities, the subject matter, duration, purpose, data types and categories of people, as recommended by the ICO.
- Security measures: Attach measurable requirements for access control, encryption, isolation, secure development, vulnerability management, logging, resilience and personnel confidentiality.
- Subprocessors: Name current subprocessors, locations and functions; require advance notice of additions or material changes and provide an objection or termination right where appropriate.
- International transfers: Specify hosting and transfer regions, legal mechanisms, supplementary safeguards and notification of a change in location.
- Retention and deletion: State periods, deletion triggers, backup treatment, legal-hold rules, return format and evidence of completion at termination.
- Incident assistance: Define what counts as a breach, vulnerability, outage or unauthorised use; set a notification deadline, continuing updates, evidence preservation, cooperation and cost responsibilities.
- Audit and evidence: Permit reasonable assessments, independent reports, targeted questionnaires, remediation tracking and evidence refreshes when the risk or service changes.
- Change control: Require notice of material model, system-prompt, training-use, security, subprocessor or hosting changes, with a right to test, restrict or terminate the affected use.
- Exit and continuity: Require export in a usable format, transition assistance, deletion confirmation, access revocation and a workable plan if the vendor fails or becomes unavailable.
Do not accept a clause that allows the vendor to change data use unilaterally while leaving your only remedy as continued payment. Escalate conflicts between the contract and the vendor’s online policy before production access is granted.
How should a small team run a safe pilot?
A pilot converts vendor assertions into observable behaviour while limiting the cost of being wrong. Use synthetic or low-sensitivity data first and define acceptance criteria before enabling production connections.
- Write the pilot boundary. List approved users, data fields, integrations, model features, regions, maximum retention and prohibited actions.
- Create test cases. Include normal requests, sensitive-string detection, malicious files, prompt-injection attempts, access-control checks, deletion requests, export, outage and unexpected model changes.
- Set pass/fail criteria. Examples include no use of prohibited data for training, correct role separation, complete deletion evidence, alert delivery, acceptable response quality and a functioning manual fallback.
- Log observations. Record model version, configuration, data categories, user, date, output disposition, incidents and vendor responses. Avoid placing real secrets in the test set.
- Review with the process owner. Confirm that a human can detect and correct harmful output and that the workflow does not silently expand data collection.
- Approve, narrow or stop. Document exceptions and residual risk. Remove test accounts, files and integrations that are not part of the approved design.
NIST’s Generative AI Profile calls for ongoing monitoring, dynamic risk assessments and contingency processes for third-party failures. Treat the pilot as the beginning of that operating cycle, not as a substitute for it.
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Assign monitoring tasks to named owners and trigger an out-of-cycle review when the service or risk changes. A small team can use a short register instead of a large governance platform.
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Events that should trigger reassessment
- New model, system prompt, autonomous action, plug-in, API scope or retrieval source.
- New subprocessor, hosting region, transfer route or support arrangement.
- Change to training, evaluation, retention, deletion or abuse-monitoring practices.
- Security incident, material vulnerability, unexplained data-access event or prolonged outage.
- Expansion from low-impact drafting to regulated, personal, financial or mission-critical data.
- Acquisition, ownership change, financial distress or announced product retirement.
At each review, confirm that the inventory, approved users, contract evidence, data settings, logs, fallback procedure and vendor contacts are still accurate. Revoke access promptly when a person, integration or business process no longer needs it.
How should we compare two AI vendors?
Compare vendors against the same use case and impact band. Model quality and price matter, but they should not hide weak control over data or an impractical exit.
| Comparison axis | Evidence to score |
|---|---|
| Data use and retention | Training and secondary-use restrictions, configurable retention, deletion evidence and backup handling. |
| Security assurance | Scope and freshness of attestations, vulnerability history, access controls, encryption and tenant isolation. |
| Subprocessors and geography | Named entities, hosting regions, transfer safeguards, change notice and objection rights. |
| Transparency and auditability | Architecture diagrams, logs, model-change notices, incident communication and assessment rights. |
| Resilience and exit | Recovery commitments, status communication, export format, transition support and termination deletion. |
| Model quality and oversight | Use-case test results, human-review points, version controls, provenance and harmful-output handling. |
| Operational fit and total cost | Identity integration, administration effort, training, monitoring workload, support and fallback expense. |
Use weighted scoring only after setting mandatory gates. A vendor that fails a non-negotiable requirement—such as prohibited training use, no deletion path or no incident notification—should not win because it scores higher on convenience.
What should we do if the vendor refuses answers or a control fails?
Refusal is itself a risk signal, but it does not always require immediate rejection. Separate an unavailable document from an unacceptable practice and ask for a compensating control.
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- Missing evidence: Limit the use case and data sensitivity, require an executive risk acceptance, and set a deadline for evidence refresh.
- Unacceptable data use: Disable the feature, negotiate a binding restriction, or select a service whose terms match the use case.
- Security weakness: Reduce privileges and integrations, add monitoring, require remediation milestones, and prohibit production data until closure.
- Incident or outage: Revoke credentials, stop transfers, preserve logs, notify affected stakeholders under your response plan, switch to the fallback process and request vendor facts and containment actions.
- Unexpected model change: Freeze the integration, compare outputs with the approved test set, reassess impact and resume only after the owner signs off.
Record the decision and rationale. A short, honest exception record is safer than an undocumented assumption that the vendor’s standard service is acceptable.
What is a workable operating model for a small IT team?
Divide responsibilities so that no single person silently approves data use, configures access and accepts the risk. A practical model is an IT or security lead for controls, a privacy or legal adviser for data roles and contract terms, and a business owner for purpose, output quality and fallback operations. One person may hold more than one role in a small organisation, but the approvals and evidence should remain distinct.
- Before purchase: Complete the inventory, impact classification, evidence request and contract checklist.
- Before production: Apply minimisation, configure identity and retention settings, complete the pilot and document acceptance criteria.
- During use: Review access and logs, track incidents and changes, and verify that users follow the approved data rules.
- At renewal or material change: Refresh evidence, compare the service with alternatives, test export and fallback, and confirm deletion or continued necessity.
- At termination: Disable accounts and integrations, export required records, obtain deletion evidence and update the approved-provider list.
This lightweight cycle implements NIST’s supplier-risk and monitoring principles without requiring a large governance programme.
What is the bottom-line decision rule?
Approve an AI vendor only when you can describe the complete data path, show that the impact is acceptable, verify controls with current evidence, enforce the important requirements contractually, and operate a fallback if the service or model changes. If any of those conditions is missing, narrow the data and permissions or postpone production use. The discipline of recording that decision is as important as the questionnaire itself.
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Do SOC 2 or ISO certificates prove an AI vendor is safe?
No. A certification or attestation covers a defined scope, control period and service boundary; it may not include the model, subprocessors, training use or the exact tenant you plan to use. Read the scope, exceptions, testing date and complementary customer responsibilities, then verify the controls that matter to your data flow.
Should a small company ban public AI chatbots outright?
A blanket ban can drive unapproved workarounds. A clearer approach is to prohibit confidential, personal, regulated and credential-bearing data in unapproved services while providing an approved, lower-risk workflow with training, reporting and a path for exceptions.
How often should an AI vendor review be repeated?
Set a regular review interval appropriate to the impact, then review immediately after a material model, subprocessor, hosting, data-use, incident or business-process change. High-impact services generally need more frequent evidence refreshes than low-impact drafting tools.
What records should we retain to demonstrate due diligence?
Keep the inventory, impact rationale, vendor answers, evidence scope and dates, contract and amendments, configuration screenshots or exports, pilot results, approvals, incidents, change decisions and deletion confirmations. Store versions so you can show what was known and approved when a decision was made.
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