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Evaluate an AI tool as a complete data-processing system, not just as a chat window. Trace prompts, uploads, outputs and metadata from entry through model inference, logs, support, subprocessors, exports, backups and deletion. Confirm whether content is used for training or human review; who can access it; how it is encrypted and isolated; where it is processed; how long each data class is retained; and whether deletion actually propagates. Test the settings with synthetic or redacted information before production, then record the evidence, unresolved risks, approver and review date.
What exactly should you evaluate?
Privacy and security settings are only one part of an AI service’s behavior. The meaningful object of review is the entire data lifecycle: what enters the service, what the model and surrounding systems do with it, who can retrieve it, where copies are stored, and what remains after deletion.
Draw the flow before reading marketing claims. A typical path includes a user device or API client, the vendor’s application, an inference or model provider, prompt and response logs, abuse-monitoring systems, support tools, integrations, exports, backups and subprocessors. A tool can disable model training while still retaining prompts for debugging, exposing them to support staff, or copying them into backups for a separate period.
Separate the data classes
Ask for separate rules for each item rather than accepting a single statement such as “we retain your data.” At minimum, distinguish:
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- Prompts and conversation text.
- Uploaded files, images, audio, code and extracted text.
- Generated outputs and user feedback.
- Account, billing, device and usage metadata.
- Security, abuse-monitoring and diagnostic records.
- Temporary or intermediate files created during processing.
- Administrator exports, integrations and downstream copies.
- Backups and derived datasets used for evaluation, debugging or retraining.
For each class, document its purpose, system of record, access roles, processing location, retention period and deletion method. The ICO advises documenting movements and storage of personal data, deleting intermediate files when no longer required, and specifying retention periods for data kept for retraining or debugging.
How do you decide whether AI should process the information at all?
Start with necessity and minimisation. If the task can be completed without an AI service, or with a local or controlled system, do not send the data to a public platform merely because it is convenient.
Define the permitted data
Write a short classification rule before testing. For example, a team may permit public material and synthetic records, allow internal non-sensitive text only in an approved enterprise workspace, and prohibit personal, health, financial, confidential and proprietary information unless a separately approved control set applies. The rule should name an owner who can approve exceptions.
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- Remove names, direct identifiers, account numbers and unnecessary dates.
- Replace real records with realistic synthetic values for development and evaluation.
- Mask free-text fields that may contain incidental personal information.
- Send only the fields needed for the task, not an entire document or database row.
- Check whether prompts, attachments and conversation history are all required by the feature.
The Office of the Australian Information Commissioner (OAIC), updated 17 January 2025, recommends privacy by design, a Privacy Impact Assessment and ongoing product review. Its best-practice advice is not to enter personal, particularly sensitive, information into publicly available generative-AI tools. CMS guidance similarly says not to enter personally identifiable, health, confidential or proprietary information into publicly accessible AI platforms.
Does the AI tool train on prompts, files or outputs?
Do not infer the answer from a toggle labelled “privacy.” Obtain a precise statement covering every account type, feature, data class and subprocessor.
Questions to ask the vendor
- Are prompts, uploads, outputs, thumbs-up or thumbs-down feedback, and metadata used to train or improve models?
- Is use for abuse detection, quality review, evaluation or debugging separate from model training?
- Does an opt-out apply to historical data, or only to new submissions?
- Does it cover consumer, team, enterprise, API, mobile and embedded accounts?
- Can human reviewers inspect content, under what trigger, and for how long?
- Are data sent to model providers or subprocessors governed by the same restriction?
- Can the vendor use de-identified or aggregated derivatives, and how is de-identification defined?
Save the applicable terms and settings with the date, plan and region. A statement that data is “not used to train our models” may still permit retention for security monitoring or service improvement, so record those purposes separately.
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What counts as a meaningful opt-out?
A meaningful opt-out identifies the scope, effective date and exceptions. Verify whether it applies to existing conversations and files, backups, support tickets, evaluation sets and subprocessors. If the vendor cannot explain propagation to derived datasets, treat the training restriction as incomplete for sensitive work.
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Who can see conversations and uploaded files?
Map every human and machine identity that can access content, then check whether access is limited, logged and reviewed.
People and roles
- Workspace members and delegated administrators.
- Vendor support, trust-and-safety and incident-response staff.
- Engineers with production or debugging access.
- Subprocessor personnel handling hosting, storage, analytics or support.
- Integration accounts, plugins, connectors and API applications.
Ask for least-privilege role definitions, approval for exceptional access, time-limited support access and exportable access logs. Confirm whether administrators can read content by default or only manage billing and configuration.
Identity and tenant controls
Check for single sign-on, multi-factor authentication, enforced password policies, automated provisioning and deprovisioning, session controls and role-based permissions. For multi-tenant services, request an explanation of tenant isolation and independent testing. Customer-managed encryption keys can improve control, but ask what remains accessible to the provider for operations, abuse prevention and recovery.
What security evidence should an AI vendor provide?
Security controls should match the nature, scope, context, purposes and risks of processing; the ICO explicitly says there is no “one-size-fits-all” approach.
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- Encryption in transit and at rest, including which systems and backups are covered.
- Independent assurance reports or certifications and their coverage period.
- Recent penetration-test summary, remediation status and scope.
- Vulnerability-disclosure, patching and dependency-management processes.
- Tenant-isolation architecture and privileged-access controls.
- Incident-detection, containment and customer-notification commitments.
- Business-continuity and disaster-recovery arrangements.
- Audit-log retention, export format and administrator visibility.
Do not treat a certification logo or a generic security page as proof that your chosen plan has the same controls. Match the evidence to the exact edition, region, features and contract. The joint NSA, CISA, FBI and partner guidance issued 15 April 2024 frames secure AI deployment around confidentiality, integrity, availability, vulnerability mitigation, and the ability to protect, detect and respond to malicious activity.
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Check integrity as well as confidentiality
An AI service can expose data without a breach if an attacker alters prompts, retrieved documents, model configuration or tool permissions. Ask how the provider verifies data provenance and integrity during storage and transport. NSA AISC’s AI Data Security release of 22 May 2025 highlights provenance tracking and integrity verification as core safeguards.
Where is data processed, stored and shared?
“Hosted in the region” is not a complete location answer. Processing, logs, support and backups can occur in different countries and can involve separate companies.
Build a data-flow inventory
- List every client, API endpoint, model service and storage system involved.
- Record the country or region for inference, primary storage, logs, backups and support.
- List subprocessors by function and identify which can access content.
- Document international-transfer mechanisms and the vendor’s change-notice process.
- Record integrations that export prompts, files or outputs to other systems.
Ask whether a regional setting is a hard processing boundary or merely a preferred location. Check whether support tickets, abuse reviews and disaster recovery bypass it. Contractual commitments should state the regions, permitted subprocessors, transfer mechanism and notice period for material changes.
How long does the tool retain data, and can you delete it?
Require a retention schedule by data class and test deletion yourself. A single account-deletion promise is not enough.
Retention questions
| Data class | What to confirm | Common failure to avoid |
|---|---|---|
| Prompts and conversations | Default period, configurable limit, legal holds and effect of history settings | Assuming “history off” removes server-side copies |
| Uploads and extracted text | Deletion trigger, file-version copies and processing caches | Deleting the chat but leaving an attachment in a file store |
| Outputs and feedback | Whether ratings, edits and generated files are retained separately | Ignoring feedback used for evaluation |
| Logs and abuse monitoring | Security purpose, access, retention and redaction | Treating diagnostic logs as content-free |
| Backups | Backup cycle, expiry and restore procedure | Claiming immediate deletion while backups persist |
| Training or derived data | Whether source content is copied, de-identified or irreversibly removed | Assuming account deletion reverses model changes |
| Subprocessors and exports | Deletion deadlines and confirmation from each recipient | Deleting only the vendor’s primary record |
Test deletion propagation
Use synthetic records with unique markers. Delete an individual conversation or file, request an account export before and after deletion, inspect administrator views and available logs, and ask the vendor to explain backup expiry and subprocessor deletion. Record what disappeared, what remained for a stated period and the contractual basis for any exception.
Deletion may be delayed by legal holds, fraud prevention or security records. Those exceptions should be narrow, documented, access-controlled and time-limited. If the vendor cannot state when residual copies expire, do not describe deletion as permanent.
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How do you verify settings before production use?
A written questionnaire is useful, but only a controlled test shows how the product behaves in your configuration.
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- Collect authoritative documents. Obtain the privacy notice, security documentation, plan terms, data-processing agreement, subprocessor list, retention schedule and incident-notification terms. Record publication dates and your account edition and region.
- Create a test workspace. Use synthetic or redacted data, a non-production identity and a restricted administrator. Do not test with real confidential records.
- Inspect every control. Review training and sharing settings, history, file retention, export, deletion, integration permissions, SSO, MFA, roles, keys and regional options. Capture the setting’s exact label and date rather than relying on memory.
- Exercise access paths. Have a normal user, administrator and support workflow attempt only the actions each role should allow. Review available audit events for sign-ins, exports, sharing, configuration changes and privileged access.
- Run the deletion test. Add uniquely marked synthetic prompts and files, delete them at the item and account levels, then check the interface, exports, logs and administrator views. Ask how backups, caches, subprocessors and derived datasets are handled.
- Check response channels. Verify the security contact, incident-reporting route, expected notification window and procedure for requesting evidence during an event.
- Write the decision. Record evidence, open issues, compensating controls, risk acceptance, approval owner and review date. State which data classes are allowed and which are prohibited.
How should you compare two or more AI tools?
Compare equivalent plans and use-case configurations, not home-page claims. Weight each dimension by the sensitivity of the data and the consequences of disclosure or alteration.
| Comparison axis | Evidence to score | Why it matters |
|---|---|---|
| Training and secondary use | Scope of opt-out, human review, evaluation and subprocessor restrictions | Determines whether content can influence future systems or be inspected |
| Retention and deletion | Periods for prompts, files, logs, backups and derived data; deletion verification | Limits the window and number of residual copies |
| Access controls | SSO, MFA, roles, least privilege, support access and customer-managed keys | Reduces unauthorized internal and external access |
| Auditability | Exportable events, administrator visibility and configuration history | Shows who did what and supports investigations |
| Security assurance | Independent reports, testing, vulnerability handling and incident commitments | Provides evidence beyond assertions |
| Residency and subprocessors | Processing regions, transfer mechanisms, list and change notice | Controls jurisdiction and downstream exposure |
| Integration permissions | Connectors, scopes, token controls and export behavior | Prevents a safe chat setting being undermined by an attached system |
| Policy and model change transparency | Version history, advance notice and reassessment triggers | Preserves the decision after the product changes |
| Contractual quality | Specific, enforceable commitments rather than marketing language | Defines what the vendor must do and what remedy exists |
Use a simple scale such as meets, partly meets, does not meet or unknown, and attach evidence to every score. An “unknown” is a risk finding, not a neutral result, when the intended data is sensitive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What governance is needed after approval?
Approval is an operating rule, not a one-time purchase decision. Assign a product owner and set a review trigger for material policy, model, feature, subprocessor or processing-location changes.
Keep these controls active
- Train staff on permitted data classes, redaction and prompt-sharing risks.
- Require human validation of outputs before they affect customers, records, payments, access or safety decisions. CMS guidance calls for independent validation and continuing staff education.
- Review access logs and administrator memberships periodically.
- Re-test deletion, exports and integrations after major releases.
- Maintain an incident route for suspected disclosure, prompt injection, compromised credentials or harmful output.
- Version the retention and privacy decision so auditors can see which terms applied at the time.
The UK Code of Practice recommends documenting training-data sources, retention time, review frequency, failure modes and audit logs for security-relevant prompt or configuration changes. Use those records to make reassessment repeatable rather than dependent on one employee’s memory.
What are the most serious red flags?
Stop or restrict the use case when any of these conditions remains unresolved:
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- The vendor will not state whether prompts, files or feedback are used for training, review or debugging.
- Retention periods are described only as “as long as necessary,” with no class-by-class schedule.
- Account deletion has no explanation for backups, caches, subprocessors or derived datasets.
- Administrators or support staff can access content without least-privilege controls or audit records.
- Regional processing is advertised but support, logs or backups are excluded from the boundary.
- There is no credible incident-notification, vulnerability-disclosure or patching process.
- The tool requests broad integration permissions that are not needed for the task.
- Terms can change without notice and there is no reassessment or exit procedure.
Use synthetic data, stricter access controls, local processing or a different service while an issue is open. Do not “accept” an unknown risk by quietly sending real sensitive information.
What should the final decision record contain?
A concise record makes the evaluation defensible and repeatable. Include:
- Use case, business necessity and alternatives considered.
- Allowed and prohibited data classes, with redaction requirements.
- Account type, edition, region and enabled features.
- Data-flow diagram and subprocessor list.
- Training, human-review, retention and deletion findings.
- Identity, encryption, tenant-isolation, logging and incident-response evidence.
- Test results for access, export, deletion and integrations.
- Open issues, compensating controls and accepted residual risk.
- Named approval owner, effective date and next review trigger.
This record should be updated whenever the vendor changes a model, prompt-handling policy, feature, subprocessor or processing location. The OAIC recommends regular lifecycle review, while the ICO notes that security measures must continue to reflect changing processing risks.
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Can I use an AI tool with confidential information if it promises not to train on it?
Not automatically. You still need controls for human review, support access, logs, backups, subprocessors, integrations, jurisdiction, breach response and deletion, and you should use the least data necessary.
What is the difference between deleting a chat and deleting the underlying data?
Removing a conversation from the user interface may leave copies in file stores, diagnostic logs, exports, caches, backups or derived evaluation data. Ask for the retention and deletion behavior of each copy and verify it with uniquely marked synthetic data.
How often should an approved AI tool be reassessed?
Set a scheduled review appropriate to the sensitivity of the use case, and trigger an immediate review after a material change to the model, privacy terms, retention settings, subprocessors, integrations or processing region.
What evidence should I save when a vendor changes its privacy policy?
Keep the old and new versions, publication dates, account plan and region, change notice, setting screenshots or exports, and your updated risk decision. This shows which rules applied to data processed before and after the change.
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