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Evaluate an AI tool as a complete data-processing system, not just as a chat window. Before approving it, trace prompts, uploads, outputs, logs, support access, integrations, exports, backups, subprocessors and deletion. Verify whether content is used for model training or human review; who can access it; how it is encrypted; where it is processed; how long each data type is retained; and whether deletion reaches backups and derived datasets. Start with synthetic or redacted data, record the evidence and unresolved risks, and assign an owner and review date. For public generative-AI services, the OAIC’s guidance updated 17 January 2025 recommends not entering personal or particularly sensitive information as a best practice.
What does a proper AI privacy and security evaluation cover?
A useful evaluation follows data from the moment a person or application submits it until every copy is deleted or lawfully retained. The evaluation should cover the model service, application interface, API, telemetry, abuse monitoring, customer support, subprocessors, backups and exports—not only the vendor’s headline privacy statement.
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- Purpose: Why is an AI system needed, and what is the minimum information required?
- Data: What prompts, files, images, audio, outputs, feedback, account details, device data and logs are collected?
- Processing: Which model, application, region and subprocessors handle each item?
- People: Which vendor employees, administrators, support agents, workspace members or connected applications can view content?
- Controls: Are encryption, authentication, tenant isolation, access logs, retention settings and deletion mechanisms available?
- Governance: Who approves use, responds to incidents and rechecks the decision after material changes?
The OAIC advises commercial-AI due diligence, privacy by design, a Privacy Impact Assessment and regular review throughout the product lifecycle. Treat those as an operating process rather than a one-time procurement form.
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How should I define the use case and minimise data before testing an AI tool?
Reduce the information sent to the model before you compare vendors. Data minimisation limits exposure and often improves the quality of the prompt by removing irrelevant details.
Classify the information
| Class | Examples | Default treatment |
|---|---|---|
| Public | Published documentation, public web copy | Usually suitable for a public tool, subject to confidentiality and copyright rules |
| Internal | Unpublished plans, internal procedures, non-public analytics | Use only with an approved workspace and documented controls |
| Confidential | Customer records, contracts, source code, financial or employee information | Require contractual assurances, access controls, regional review and an explicit owner |
| Highly sensitive | Health, identity, authentication, regulated or trade-secret data | Keep out of publicly accessible AI services; use a specifically assessed environment or do not use AI |
Prepare a minimum-data version
- Remove names, email addresses, account numbers, exact dates and free-text identifiers.
- Replace remaining identifiers with consistent tokens such as
customer_17so the model can follow relationships without seeing identity. - Crop files to the pages or fields needed for the task; strip hidden metadata and comments.
- Decide whether the model needs the original text at all. A summary, schema or synthetic example may be enough.
- Define what the user must never paste, upload or connect, and put that rule beside the input control.
CMS guidance similarly says not to enter personally identifiable, health, confidential or proprietary information into publicly accessible AI platforms. It also calls for independent validation of outputs and continual staff education about the risks.
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Does the AI tool train on my chats, files or outputs?
Do not infer the answer from a “private” label. Ask separately whether prompts, uploaded files, generated outputs, feedback, metadata or support transcripts are used for training, fine-tuning, evaluation, abuse detection or product improvement.
Questions to put to the vendor
- Is training use off by default, or does it require an account setting, contract or enterprise plan?
- Does an opt-out cover prompts, uploads, outputs, feedback, metadata, human review and every subprocessor?
- Does the setting apply to existing data, or only to new submissions?
- Are data used for safety testing, debugging or abuse monitoring retained under different rules?
- Can the vendor remove your content from training or evaluation datasets, and how is that request verified?
Ask for the applicable plan and regional terms, not a marketing summary. A promise that content is “not used to train models” may still permit short-term abuse monitoring, human review, logging or retention by a service provider. Record the exact wording, version and date, then check whether administrators can prevent users from changing the setting.
Who can see conversations and uploaded files?
Map access by role and by event. The relevant question is not only whether a vendor employee can read a conversation, but whether workspace administrators, support personnel, subprocessors, integration providers or another tenant can access it and under what approval process.
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Access controls to verify
- Single sign-on and multi-factor authentication for every administrator.
- Role-based permissions that separate ordinary users, content reviewers, billing staff and security personnel.
- Least-privilege defaults, time-limited support access and a record of why access was granted.
- Tenant isolation that prevents one customer’s prompts, files or indexes from appearing in another customer’s environment.
- Customer-managed encryption keys where the sensitivity and contract justify them.
- Exportable logs showing sign-ins, administrator actions, content access, sharing, downloads, integrations and policy changes.
Check the sharing model inside the product. Links that allow anyone with a URL to view a conversation, public prompt galleries, shared team folders and connected drives can bypass assumptions made from the vendor’s central security page. Test the least-privileged user and an administrator separately; they should not see the same controls or content.
What security evidence should an AI vendor provide?
Security controls must match the nature, scope, context, purposes and risks of processing. The ICO states that there is no one-size-fits-all approach, so request evidence that addresses your actual deployment rather than collecting badges.
| Area | Evidence to request | What to test yourself |
|---|---|---|
| Encryption | Encryption in transit and at rest; key-management description; customer-key options | Confirm the setting applies to prompts, files, logs, backups and exports, not only the web page |
| Identity | SSO, MFA, session controls and administrator separation | Disable a test user, revoke a session and verify access ends promptly |
| Isolation | Architecture and testing for tenant, workspace and retrieval isolation | Use two test workspaces and attempt cross-workspace search or sharing |
| Vulnerability management | Penetration-test summary, disclosure channel, patch process and remediation targets | Check whether findings are current and whether the scope includes the AI application and APIs |
| Incident response | Notification commitments, contact route, investigation and evidence-preservation process | Confirm who receives notice and whether the promise is contractual |
| Availability and recovery | Backup, restoration and outage procedures | Ask how recovery copies are protected and eventually deleted |
Joint guidance from the NSA, CISA, FBI and partner agencies dated 15 April 2024 frames secure AI deployment around confidentiality, integrity and availability, plus the ability to protect, detect and respond to malicious activity. Use that model to check whether a vendor discusses detection and recovery, not just perimeter security.
Where is my AI data stored and who are the subprocessors?
Draw a data-flow map before approval. Include the user device, browser or mobile app, API gateway, model provider, prompt and file stores, logs, abuse-monitoring system, support platform, connected applications, exports and backups.
Record location and transfer details
- Country or region where prompts and files are processed.
- Separate locations for primary storage, logging, disaster recovery and support access.
- Every subprocessor that can receive content or account data, with its function and location.
- International-transfer mechanism and the party responsible for compliance.
- Whether the customer can select a region or block a new region.
- How much notice you receive before a material subprocessor, model or policy change.
Do not treat a regional data-center option as proof that every operation stays there. Inference, telemetry, support tickets, fraud detection and backups may follow different paths. The ICO recommends documenting movements and storage of personal data and maintaining clear audit trails; use that documentation as a condition of approval.
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How long does the AI tool keep prompts, files, logs and backups?
Obtain a separate retention period for each data class. “We delete your data” is incomplete unless it explains active content, temporary files, logs, abuse-monitoring records, support copies, backups and data already used in derived datasets.
| Data type | Retention question | Evidence to capture |
|---|---|---|
| Prompts and conversations | When are they removed after user or account deletion? | Plan terms and an observed deletion test |
| Uploads and intermediate files | Are extracted text, thumbnails, OCR output and temporary files separate? | Storage description and deletion scope |
| Generated outputs and feedback | Are ratings, corrections or copied outputs retained for improvement? | Training and feedback policy |
| Security and audit logs | How long are sign-in, access, export and policy-change events kept? | Log-retention setting and administrator view |
| Abuse monitoring and support | Can restricted staff view content, and for how long? | Access procedure and support terms |
| Backups and disaster recovery | When do deleted records age out of immutable or offline copies? | Backup rotation and deletion statement |
| Training or evaluation data | Can data remain in a derived dataset after account deletion? | Removal or opt-out commitment |
The ICO calls for deleting intermediate files when they are no longer required and specifying retention periods for data kept for retraining or debugging. If a vendor gives only a broad account-deletion promise, mark the unaddressed categories as an open risk rather than assuming they follow the same clock.
Can I verify that deletion is permanent?
Run a controlled deletion test with synthetic or redacted data. A successful disappearance from the chat list does not prove that exports, logs, caches, subprocessors or backups are gone.
- Create a test workspace and submit uniquely identifiable synthetic strings in a prompt, an attachment and a generated output.
- Export the workspace, inspect administrator views and note every location where the strings appear.
- Delete the individual items, then delete the account or workspace according to the documented process.
- Submit a formal deletion request and record the request ID, date, scope and expected completion time.
- After the stated period, repeat searches in the user interface, exports, administrator logs and connected applications.
- Ask the vendor what remains in backups, legal holds, abuse-monitoring stores or derived datasets, and how those copies expire.
- Save screenshots, responses and exceptions in the decision record; do not use real personal information for the test.
Deletion may be delayed by legal holds, security investigations or backup rotation. The important control is a precise explanation of what is removed immediately, what is queued, who can still access it and when the final copy expires.
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Use the same use case, data classification and questions for every candidate. Weight each axis by the consequences of exposure or loss; a tool with fewer features may be safer for a sensitive workflow than a more capable tool with vague retention.
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| Comparison axis | What a strong answer looks like | Warning sign |
|---|---|---|
| Training and secondary use | Specific defaults, scope, opt-out coverage and subprocessor treatment | Unqualified “private” language |
| Retention and deletion | Separate periods, tested deletion and backup explanation | One blanket retention sentence |
| Access and administration | SSO, MFA, roles, least privilege and exportable access logs | Shared administrator accounts or no content-access record |
| Encryption and keys | Coverage for content, logs, backups and exports, with documented key control | Encryption described only for web traffic |
| Residency and subprocessors | Named functions, regions, transfer terms and change notice | Unbounded “global infrastructure” wording |
| Incident response | Contractual notice, contact route and investigation commitments | Marketing assurance without a timeline or duty |
| Change transparency | Versioned policies, model or prompt-change records and notice rights | Terms can change without meaningful notice |
| Integration permissions | Scoped tokens, revocation, read/write separation and activity logs | Permanent broad access to drives, mail or code |
Do not average scores mechanically. Set disqualifying requirements first—for example, a regional restriction, customer-managed keys or a prohibition on training use—then compare the remaining tools on usability and cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What practical verification process should an organisation follow?
Combine documentation review with a small, instrumented test. The following sequence creates evidence that procurement, security and privacy staff can revisit.
- Send a questionnaire: Request privacy terms, security architecture, subprocessor list, retention schedule, deletion procedure, incident terms and independent assurance reports.
- Build a test workspace: Use synthetic or redacted data and the same identity, roles, integrations and region planned for production.
- Inspect settings: Check training, sharing, history, export, retention, region, human-review and administrator controls; record defaults and who can change them.
- Exercise the controls: Test login protection, role boundaries, sharing links, API tokens, export, deletion and revocation. Review audit events as both user and administrator.
- Validate the contract: Match product behavior to the applicable plan, data-processing terms, subprocessor commitments and incident-notification language.
- Document the decision: Record evidence, unresolved issues, permitted data classes, compensating controls, risk acceptance, approval owner and review date.
- Reassess: Repeat after a material product, policy, model, region or subprocessor change, and after any security or privacy incident.
The UK Code of Practice from the Department for Science, Innovation and Technology (2024) calls for documentation of training-data sources, retention time, review frequency, failure modes and audit logs for security-relevant prompt or configuration changes. Those records make later reassessment possible.
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What governance should surround an approved AI tool?
Technical settings cannot compensate for uncontrolled use. Publish a short internal standard that states what data classes are allowed, who approves exceptions, when human review is mandatory and where incidents are reported.
- Name a business owner and a security or privacy reviewer.
- Require staff to use managed accounts rather than personal accounts for work.
- Block unsanctioned browser extensions, plug-ins and connectors that can copy page content or retain tokens.
- Require independent checking of important outputs before they affect a person, payment, access decision or regulated record.
- Teach staff how prompt injection, accidental disclosure, malicious files and misleading outputs can occur.
- Keep a current inventory of models, versions, regions, integrations and approved data classes.
- Set a review trigger for policy changes, new subprocessors, major model updates, incidents or a change in the use case.
NSA AISC’s AI Data Security release dated 22 May 2025 highlights provenance tracking and integrity verification during storage and transport. For sensitive workflows, preserve where an input came from, which model and configuration processed it, what controls were active and who approved the resulting action.
What are the clearest red flags?
- The vendor cannot state whether prompts, uploads or outputs are used for training or human review.
- Retention is described only as “as long as necessary,” with no periods for logs, backups or intermediate files.
- Deletion removes the user interface entry but the vendor will not explain exports, subprocessors, caches or derived data.
- There is no MFA or role separation for administrators, or no record of content access.
- Subprocessors and processing regions are undisclosed, or can change without notice.
- Security claims rely on logos or certificates without scope, date, covered service or exceptions.
- Connected applications receive broad, permanent read/write permissions.
- The service encourages users to upload confidential material to a public endpoint.
- The vendor will not support a synthetic-data deletion test or provide an incident contact.
How should I make the final approval decision?
Approve an AI tool only when the documented controls fit the sensitivity and consequence of the use case. A complete decision record should state the data allowed, prohibited and masked; training and secondary-use settings; access and encryption controls; locations and subprocessors; retention and deletion results; contractual gaps; compensating measures; the accountable owner; and the next review date.
If a material answer remains unknown, treat it as a risk requiring explicit acceptance—not as evidence that the tool is safe. For personal or particularly sensitive information, the conservative choice is to keep it out of publicly accessible generative-AI services unless a separately assessed environment and governance process address the risk.
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Frequently Asked Questions
Can a SOC 2 or ISO certificate prove that an AI tool is safe for confidential data?
No. An assurance report covers a defined service, period and control scope, which may exclude the model provider, subprocessors, optional integrations or your chosen plan. Read the exceptions and bridge letters, then test the product settings and retention behavior that matter to your use case.
Should I use an AI browser extension with company accounts?
Treat an extension as a separate processor because it can read page content, cookies, clipboard data or authentication tokens. Review its permissions, update mechanism, storage location and uninstall behavior, and approve only extensions that have a documented business need and revocable access.
How often should an approved AI tool be reassessed?
Set a calendar review at least annually, then reassess immediately after a material model, policy, subprocessor, region or integration change. An incident, new data category or expansion from experimentation to production should also reopen the assessment.
What should a small team do if a vendor will not answer detailed security questions?
Limit the tool to public or synthetic information, disable integrations and prohibit uploads until the missing answers are resolved. Record the limitation and choose a different service for confidential work rather than relying on informal assurances.
Can deleting a conversation remove it from a model that already learned from it?
Not necessarily. Account deletion may remove stored records while a separate training or evaluation dataset follows another process. Require the vendor to state whether your content can enter derived datasets and what removal or opt-out mechanism applies to those datasets.
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