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Do not paste sensitive information into an AI tool until you can explain its complete data path. Identify every prompt, file, connector, account identifier and inferred result; then verify who can access each item, where it is processed and stored, whether it is used for training or service improvement, how long it remains, and how deletion works. If the provider cannot answer those questions for the exact product, plan and region, use less-sensitive test data or choose another tool. Treat confidential, personal, health, financial, legal and proprietary information as off-limits until a documented privacy and security review approves the use.
What does it mean for an AI tool to protect your data?
“Secure” is not a complete privacy answer. Protection covers the whole lifecycle: collection, transmission, processing, storage, access, model evaluation, backups, deletion, incident response and retirement. The Australian Government’s National AI Centre cautions that “When you use AI, the system processes your data and in some cases stores it.” That processing may include more than the text you type.
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- Prompts, pasted text and conversation history.
- Uploaded documents, images, audio, video, spreadsheets and metadata.
- Connected drives, browsers, email, calendars, code repositories, plug-ins and APIs.
- Account details, device identifiers, IP addresses, billing records and support tickets.
- Outputs, summaries, embeddings, safety logs, abuse reports and other data inferred from your input.
For each item, record the business purpose and the consequence if it is exposed, altered, retained too long or incorporated into a model. This inventory becomes the basis for questions to the provider and for your own approval decision.
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How do I map an AI tool’s data flow before trying it?
Map every route by which information can enter, move through or leave the service. The National AI Centre identifies direct entry, file uploads, integrations and data generated or inferred by the system as separate paths; check each one rather than assuming the chat box is the whole product.
1. List inputs and their purpose
Write down the minimum data needed to answer your question. A request to rewrite a paragraph may need only the paragraph, not a customer name, account number or full case file. Include hidden context such as system prompts, retrieval indexes, conversation memory and files automatically attached by an integration.
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2. Trace destinations
Ask whether content is sent to the provider’s own infrastructure, a model-hosting partner, a safety or moderation service, a support system, an analytics platform or a connected application. Identify every subprocessor and the country or region in which it operates. “Stored in the cloud” is not a location or an access explanation.
3. Identify people and systems with access
Separate ordinary service access from exceptional access. Determine whether provider employees, contractors, security investigators, customer administrators, integration owners or automated services can view content, and whether access is logged, limited by role and approved for a defined purpose.
4. Mark outbound flows
Connectors can transmit content without a second paste action. Check what an extension, plug-in, API, workflow or enterprise search connector can read, what it can write, and whether an administrator can disable it. Verify whether outputs are copied into your own systems, provider logs or third-party applications.
5. Record the result
Keep a diagram or table showing the data item, purpose, destination, access group, processing region, retention period and deletion action. Date the record and identify the exact plan and policy version; providers can change these terms.
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Read the privacy notice, customer terms, security documentation, data-processing addendum and plan-specific settings together. A broad statement such as “we use industry-standard security” does not tell you whether prompts are retained, reviewed by people or used to improve a model.
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Training and service improvement
Look for separate treatment of prompts, uploads, outputs, feedback, abuse reports and telemetry. Determine whether inputs may be used to train, fine-tune, evaluate or improve any model; whether the control is opt-in, opt-out or unavailable; and whether an administrator can enforce it for every user. Ask whether data already used in a training dataset can be removed.
Human review and support
Find out when staff or contractors may inspect content, such as abuse investigations, quality checks or support tickets. Check whether human review can be disabled, what approval and logging controls apply, and whether sensitive content is excluded from routine review.
Subprocessors and legal disclosure
Obtain the current subprocessor list and notice period for changes. Ask how the provider responds to subpoenas, government requests and emergency disclosures, and whether you receive notice where law permits.
International transfers and residency
“Regional hosting” may not prevent remote support access or processing in another country. Ask where data is collected, processed, replicated and backed up; which cross-border transfers occur; and which contractual safeguards cover them for your jurisdiction.
Plan and contract scope
Consumer, free, team and enterprise plans can have different retention, training, audit and contractual terms. Save the version that applies to your account, not a generic page for another edition.
How can I minimise or de-identify data before sending it?
Data minimisation is the safest control because information that never leaves your environment cannot be exposed by the provider. The UK Information Commissioner’s Office says the amount of personal data should be limited to what is necessary for the purpose.
Remove unnecessary identifiers
- Replace names with stable labels such as
Customer-A. - Delete phone numbers, email addresses, account and policy numbers, precise addresses and exact dates unless they change the answer.
- Crop faces, signatures, barcodes, document headers and other identifying details from images.
- Summarise a long record instead of uploading the original when the detail is not needed.
Understand pseudonymisation
Pseudonymisation replaces direct identifiers but keeps a separate key that can reconnect the record to a person. It reduces exposure, yet the data remains personal data under data-protection rules if re-identification is reasonably possible. Store the key separately, restrict access to it and do not send it to the AI service.
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Do not confuse it with anonymisation
True anonymisation requires a higher threshold: a person should not be identifiable by the provider or by a reasonably likely combination of information. Rare job titles, dates, locations and distinctive events can re-identify a supposedly anonymous case. Test the transformed data for linkability before relying on the claim.
Start with a low-risk trial
Use synthetic, public or already-approved sample data to test accuracy, connectors, logs and deletion. Move to real data only after the provider’s answers and your internal review satisfy the risk owner.
What security evidence should I request?
Ask for evidence tied to the exact product and plan, not a corporate security slogan. The ICO notes that there is “no ‘one-size-fits-all’ approach to security.” Match controls to the harm that would follow from disclosure, alteration or unavailability.
| Control area | Evidence or capability to verify |
|---|---|
| Authentication | Strong authentication, single sign-on or multifactor enforcement, session controls and recovery safeguards. |
| Authorisation | Role-based permissions, least-privilege administration and separate controls for users, support staff and integrations. |
| Encryption and keys | Encryption in transit and at rest; who can decrypt; how keys are managed, rotated and separated between customers. |
| Monitoring | Access and administrator audit logs, alerting, retention of logs and customer ability to review them. |
| Vulnerability management | Testing cadence, patch process, vulnerability-disclosure channel and independent penetration-test or assessment scope. |
| Incident response | Detection, containment, customer notification commitments, investigation support and post-incident reporting. |
| Resilience and deletion | Backup protection, recovery controls, secure deletion and evidence that deletion reaches caches and replicas. |
| AI-specific threats | Controls for prompt leakage, model inversion, membership inference, malicious or poisoned data and unsafe retrieval content. |
| Supply chain | Subprocessor oversight, supplier assessments, provenance tracking and change-notification procedures. |
A joint cybersecurity guidance sheet issued in May 2025 by the NSA, CISA, FBI, ASD ACSC and NCSC describes data security as paramount across the AI lifecycle and highlights encryption, digital signatures, provenance tracking, secure storage and trust infrastructure. Ask the provider to explain which of those controls apply to your data path and what independent evidence supports the claim.
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How do I verify retention and deletion?
Retention is not one timer. Ask separately about active conversations, uploaded files, outputs, safety and abuse logs, support tickets, analytics, caches, backups and data incorporated into training or evaluation sets.
- Request exact periods. Get the duration for each data class, the start point for the clock and any extension triggered by a support case, legal hold or abuse investigation.
- Check configuration. Determine whether an administrator can disable history, training, human review, integrations and memory independently. Confirm which setting is enabled for your plan.
- Run a deletion test. Create a harmless test record, delete it through the documented process and request confirmation of removal from the interface, logs, backups and connected systems. Record what the provider can and cannot verify.
- Ask about derived data. Find out whether embeddings, safety classifications, summaries or model-training artifacts remain after the source file is deleted, and whether they can be removed.
- Document exceptions. Preserve the provider’s wording about legal holds, fraud prevention, security logs and backup expiry. “Best effort” without a scope or timeframe is a red flag.
ICO guidance says retention should be linked to a purpose and that data no longer needed should be deleted. Apply the same rule to copies in your own exports, ticketing systems and backups.
How should I assess the AI tool’s whole lifecycle and the consequences of failure?
Review the service at design, collection, testing, operation, monitoring, updates and retirement. A tool that is acceptable for a public marketing draft may be unsuitable for an employment decision, medical summary, financial assessment, legal analysis or public-service determination.
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High-impact uses need a formal gate
Require a documented privacy and security review, a named risk owner, approved data classes, human oversight and a way to suspend use quickly. Test whether a person can challenge or correct an output, and ensure the decision maker can work without the model during an outage or suspected compromise.
Check updates and changed terms
Ask how the provider announces model changes, new subprocessors, altered retention or revised training practices. Reassess when the plan, region, connector permissions, model or contract changes; an approval for one configuration does not automatically cover the next.
Plan for an incident
Define who receives the alert, which accounts or connectors are disabled, how affected records are identified, and when regulators, customers or employees must be notified. Include a procedure for rotating credentials and preserving evidence without spreading the exposed data further.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What questions should I send the provider?
Send specific questions that require product-, plan- and region-level answers. Keep the responses with your approval record.
- Are prompts, uploads and outputs stored? For how long, in which regions and in which backups?
- Are customer inputs used to train, fine-tune, evaluate or improve any model? Is the control opt-in, opt-out or unavailable?
- Which employees, contractors, subprocessors or connected applications can access content, and what controls and logs govern that access?
- Can the customer disable training, integrations, human review and retention separately?
- What is the deletion process for conversations, files, logs, caches, backups and data already incorporated into a model?
- Which security certifications, independent assessments, penetration tests, vulnerability-disclosure process and incident-notification commitments apply to this exact product and plan?
- Where is data processed, what cross-border transfers occur and which contractual safeguards apply?
- How are prompt leakage, privacy attacks, model inversion, membership inference and malicious or poisoned data represented in the threat model?
- Who owns the risk decision, how are changes communicated and how can use be suspended quickly?
If an answer points to a generic policy, ask the provider to identify the paragraph, setting or contract clause that governs your account. An unanswered question is itself a risk finding.
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How do I compare two or more AI tools fairly?
Use the same questions, test data and date for every candidate. Record the exact plan, region, policy version and contract so a later change is visible.
| Comparison axis | What to record for each tool |
|---|---|
| Input and output retention | Retention periods and separate treatment of chats, files, outputs, logs, caches and backups. |
| Training and improvement | Whether customer content is used for training, fine-tuning, evaluation or service improvement, and the available control. |
| Deletion scope | Customer action, provider timeframe, backup handling and treatment of derived or model-integrated data. |
| Residency and transfers | Processing, storage and backup regions; remote access locations; contractual transfer safeguards. |
| Subprocessors | Named suppliers, access purpose, change notice and objection or termination rights. |
| Encryption and key management | Transport and storage encryption, key custody, rotation and customer-managed-key options where offered. |
| Administration | Authentication, role controls, integration permissions, audit logs and ability to segregate users or data. |
| Assurance | Applicable certifications, independent assessments, test scope and evidence date. |
| Incident handling | Detection, notification deadline, investigation support and suspension procedure. |
| AI privacy controls | Mitigations for leakage, inversion, membership inference, poisoned data and unsafe retrieval. |
| Contract terms | Plan-specific privacy commitments, liability, audit rights and change-notification language. |
Do not select a tool because it wins one row. A strong encryption claim cannot compensate for unrestricted training use, unknown subprocessors or an inability to delete backups.
Which red flags mean I should stop?
- The policy is vague about training, service improvement, human review or retention.
- The provider will not identify storage regions or subprocessors.
- Deletion is described only as “best effort,” with no scope, timeframe or backup explanation.
- Security claims have no product scope, assessment date or supporting evidence.
- A free or consumer plan is being used for regulated information without a suitable contract.
- Integrations can silently transmit content or cannot be disabled by an administrator.
- Staff cannot explain what data the tool receives, where it goes or who can access it.
- “Encrypted” is offered without explaining who can decrypt, how keys are managed, or how support, logs, backups and connected services are protected.
Escalate to your privacy, security, legal or compliance owner instead of trying to interpret an ambiguous promise. For regulated or high-impact work, pause the use until the owner accepts the residual risk in writing.
What should I keep in the approval record?
Save the data-flow map, provider answers, policy and contract versions, plan and region, enabled settings, test-deletion result, risk owner, approved data classes and review date. Note which connectors are allowed and who can change them. Schedule a new review after a material product, model, subprocessor, region, contract or regulatory change.
This record lets you prove why a particular use was permitted and gives you a fast rollback path if the provider changes its terms or an incident occurs.
Frequently Asked Questions
Can deleting a chat remove information already used to train a model?
Not necessarily. Ask whether the provider keeps separate training, evaluation or abuse datasets and whether a deletion request reaches them; some services may only stop future use. Require the answer in the contract or plan documentation rather than assuming chat deletion has retroactive effect.
Is pseudonymised data safe for health or financial work?
Pseudonymisation lowers exposure but does not make the record anonymous when a key or other data can reconnect it to a person. Treat it as personal or regulated data unless your privacy and compliance owner confirms that the transformation and provider contract meet the applicable rules.
Can I use a consumer AI account for confidential company information?
Only if your organisation has approved that exact plan, settings and contract for the data class involved. Consumer terms may allow different retention, training, support access or international transfers, so a business label alone is not a substitute for checking the actual terms.
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Repeat it whenever the provider changes its model, plan, region, subprocessors, connectors, retention or contract, and after a security or privacy incident. Even without a visible change, set a periodic review date because online policies and settings can change.
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