Do not paste sensitive information into an AI service until you can answer seven questions: are you authorised to disclose it, what is the minimum data the task needs, which exact product and account will receive it, whether it can be used for training or improvement, how long it is retained, who or what else can access it, and whether your contract and jurisdiction allow the transfer. If any critical answer is unknown, pause and ask the responsible privacy, security or legal owner—or use a materially minimised version with no identifying or confidential details.
Why “Is this AI safe?” is the wrong first question
An AI provider can have different rules for a consumer chatbot, an organisation-managed workspace, an API endpoint and an AI feature built into another application. The model, plan, feature, connector and account configuration can change the answer again. A statement such as “not used for training” answers only one part of the decision. It does not automatically tell you whether prompts are retained, copied into application state, inspected for abuse, accessed by support staff, or sent to a connected service.
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Assess the route your data will actually take. The relevant questions are separate:
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|---|---|---|
| Authority and purpose | Who owns the data, what task requires it, and whether law, contract, policy or consent permits this disclosure | Permission to use data internally does not necessarily permit sending it to an outside processor |
| Training or improvement | Whether this product and account use inputs or outputs to improve models, and whether an opt-in or opt-out applies | Training use is distinct from storage, monitoring and human access |
| Retention and deletion | What is stored, for how long, where, and what deletion exceptions apply | A “temporary” prompt may still appear in logs, abuse systems or application state |
| Access and onward processing | Which employees, contractors, subprocessors, connectors or plugins can receive content | Every additional route expands the exposure and the number of contracts to check |
| Security and governance | Authentication, roles, audit logs, encryption, approved configuration and incident handling | A sound policy can be defeated by a shared account or an unapproved integration |
| Jurisdiction and contract | Processing region, data-transfer terms, sector obligations and the provider agreement | Residency and regulated-data requirements may limit otherwise useful services |
1. Establish authority and a specific purpose
Identify whose information it is
Classify the material before opening the AI tool. It may be your own information, an employee’s, a customer’s, a patient’s, a student’s, a partner’s or a client’s. Ownership, custody and permission are not interchangeable. A document stored in your company drive can still be subject to a client contract or a statutory confidentiality duty.
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Write the intended purpose in one sentence, such as “summarise this public meeting transcript” or “identify errors in this de-identified code sample.” A narrow purpose helps you decide what is necessary and gives an approver something concrete to evaluate.
Check the permission that covers this route
- Read the workplace, client and information-classification policies that apply to the data.
- Check contracts for confidentiality, data-processing, subcontracting, residency and use restrictions.
- For personal or regulated information, confirm the applicable legal basis, consent language and sector rules.
- For highly confidential or regulated material, obtain approval from the designated privacy, security or legal owner for the specific product, account and feature—not merely for “AI” in general.
Approval for one workspace or model does not automatically authorise a browser extension, a plug-in or an AI feature inside another application. Record the approved route and its boundaries.
2. Minimise what leaves your control
Send only the fields needed for the task
Start with the output you need and work backwards. If you need a tone edit, send the relevant paragraph rather than the entire customer record. If you need code debugging, remove secrets, production data and unrelated files. If you need a classification, send the minimum features that determine the class.
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Redact and substitute
Replace names, account numbers, email addresses, exact dates, addresses, credentials and internal project names with consistent placeholders such as CLIENT_A or ACCOUNT_17. Remove embedded comments, tracked changes, hidden spreadsheet tabs, document metadata and screenshots that reveal more than the visible text. Keep a private mapping only when the task truly requires you to restore the original values.
Understand the limit of redaction
Redaction lowers exposure; it is not a guarantee against re-identification. A rare job title, an unusual incident date, a location and a distinctive narrative can identify a person when combined. Generalise or remove contextual clues that are not needed. Do not assume that replacing a name is enough for medical, legal, HR or investigative material.
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Before sending, inspect the exact text, attachment list and enabled tools. Many incidents occur because a user uploads the whole folder instead of the one page needed.
3. Identify the exact service, account and feature
Do not rely on the model name alone
Confirm the provider, product surface, model, plan, endpoint and account that will process the request. A consumer chatbot signed in with a personal account can have different controls from an organisation-managed workspace. An API call can have different logging and retention terms from the provider’s chat interface. An AI assistant embedded in a document, help-desk or coding product may send data through both the host application’s systems and the model provider.
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| Surface | Checks to perform before use |
|---|---|
| Consumer chatbot | Current consumer terms, training/improvement setting, chat history, file retention, account sharing and export or deletion behaviour |
| Organisation-managed workspace | Administrator controls, approved domains, roles, audit logs, retention policy, user off-boarding and the organisation’s contract |
| API | Endpoint-specific data controls, abuse-monitoring logs, application-state behaviour, key management, project access and configured retention |
| Embedded assistant or connector | Host application’s terms, model provider terms, connector permissions, destination systems, subprocessors and whether content is indexed or cached |
Check the route in the actual interface or request configuration, not in a generic marketing page. A plan may include several models and features with different data handling.
4. Separate training use from every other form of data use
Ask explicitly whether prompts, files, outputs and feedback can be used for model training or service improvement, and whether the setting is an opt-in, an opt-out or a default. Save the terms or administrator setting that applied when you made the decision; defaults can change.
“Not used for training” does not mean “not retained.” A service may still keep content for abuse detection, safety monitoring, troubleshooting, billing, legal compliance or application features. It may also allow authorised personnel or contractors to review a limited sample under its policies. Treat each purpose as a separate question and obtain a separate answer.
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For example, the provider’s business page describes no-training-by-default for its listed business products, together with encryption and organisational security features. That is a provider statement scoped to those products, not a promise that every consumer app, API route or connected tool has the same treatment. Confirm the current terms for your exact plan and feature.
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The Federal Trade Commission has warned that AI companies must honour privacy and confidentiality commitments, including statements about training and other data use. A broad claim is not a substitute for reading the terms that govern your account.
5. Check retention, deletion and application state
Ask what is stored and where
- Are prompts, uploaded files, generated outputs, feedback, IP addresses and account metadata stored?
- What is the default retention period for each category?
- Are backups, safety systems, abuse logs or legal holds excluded from normal deletion?
- Can an administrator set a shorter period, and does that setting cover this model and feature?
- Which country or region processes and stores the data?
Interpret “zero retention” carefully
Current API data-controls documentation states that abuse-monitoring logs may be retained for up to 30 days by default, subject to stated exceptions. It also states that Zero Data Retention and Modified Abuse Monitoring require prior approval, and that some endpoints or capabilities may still store application state. This is a provider-specific API policy period, not a general rule for AI services and not a description of consumer chat history.
A zero-retention label may cover request content while a feature still stores a conversation, file, vector index, cached result or other application state. Ask the provider or administrator to identify every storage layer and exception that applies to the endpoint you will use.
Other providers make similarly scoped distinctions. Anthropic’s documentation for its covered models distinguishes consumer plans from specific organisational zero-data-retention configurations and describes model-specific exceptions. Verify the current terms, eligible routes and model coverage rather than transferring a setting from one product to another.
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Test deletion expectations
Find the procedure for deleting chats, files, projects, logs and account data, and learn whether deletion is immediate or scheduled. If your organisation needs a certificate, audit evidence or contractual deletion commitment, confirm that it is available. Deleting a visible conversation does not prove that backups, safety records or a downstream connector have already been erased.
6. Review access, monitoring and onward processing
Who can see the content?
Read the provider’s description of access for support, safety, abuse investigation, legal compliance and security operations. Determine whether human review is possible, under what controls, and whether contractors are involved. Internally, use individual accounts, multi-factor authentication, least-privilege roles and audit logs. Never put sensitive work into a public, shared or personal workspace merely because it is convenient.
What else receives it?
Disable or avoid connectors, plugins, browsing tools, code execution, cloud storage links and automation routes unless each destination is approved. A connector can transmit the prompt, retrieved documents, identifiers and generated output to another company. Check subprocessors, hosting providers, telemetry systems and any default indexing or caching. NIST’s Generative AI Profile (2024) states: “Third party GAI integrations may give rise to increased intellectual property, data privacy, or information security risks, pointing to the need for clear guidelines for transparency and risk management regarding the collection and use of third-party data for model inputs.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Check contracts, jurisdiction and organisational controls
For business use, review the data-processing agreement, confidentiality terms, security addendum, subprocessors list, breach-notification deadline, audit rights and limits on provider use. Check whether the provider offers the sector-specific commitments your data requires. Confirm processing and support regions, cross-border transfer mechanisms and any customer-controlled encryption or key-management options.
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8. Record the decision so it can be reviewed
For workplace use, keep a short decision record. It should be understandable to the next administrator or auditor without relying on memory.
| Record | Example entry |
|---|---|
| Approved service and route | Organisation workspace, named model, specific feature or API endpoint |
| Data categories | De-identified support text; no credentials, regulated identifiers or client secrets |
| Purpose and owner | Quality review; accountable business and privacy owners named |
| Training and improvement | Current setting or contractual clause, with date checked |
| Retention and deletion | Configured period, logs, application-state exceptions and deletion procedure |
| Access and integrations | Roles, audit logs, human review, subprocessors and connectors approved or disabled |
| Jurisdiction and contract | Processing region, data-processing agreement and sector terms |
| Review trigger | Recheck when the model, plan, endpoint, connector, terms or policy changes |
Recheck the record after a product update, a change in account ownership, a new connector or a revised privacy setting. A decision that was appropriate for one model version may not cover a new feature.
How to compare two AI services without oversimplifying
If you are choosing between services, compare the same route and configuration on every axis. Do not rank them on a single “privacy” badge.
| Comparison axis | Evidence to request |
|---|---|
| Training and user controls | Default, opt-in/opt-out mechanics, administrator enforcement and scope by model |
| Retention and deletion | Prompt, file, output, metadata and backup periods; exceptions; deletion evidence |
| Safety and abuse monitoring | Log duration, human review conditions and whether controls differ by endpoint |
| Application state | Conversation history, files, indexes, caches and feature-specific storage |
| Human and contractor access | Support, security, legal and abuse-review access and safeguards |
| Connected tools and subprocessors | Connector permissions, downstream processors, hosting regions and change notices |
| Authentication and governance | Single sign-on, multi-factor authentication, roles, audit logs and administrator controls |
| Contract and residency | Data-processing terms, sector commitments, transfer mechanism and processing location |
Ask for written answers that identify the exact plan, model, feature and route. If a cell cannot be established, mark it “not stated” and treat the uncertainty as a risk rather than filling it with an assumption.
What to do when an answer is missing
- Stop before pasting the sensitive material or uploading the file.
- Ask the approved internal owner for a product-specific decision, or ask the provider for the missing control and its scope.
- If the task cannot wait, create a materially minimised sample containing no direct identifiers, secrets or confidential context.
- Do not rely on a screenshot, a sales statement or a generic “enterprise” label as proof that the route is approved.
- If data was sent to an unapproved route, preserve the relevant account and prompt details, notify your privacy or security contact promptly, and follow the organisation’s incident procedure. Do not assume that deleting the chat reverses every copy.
Practical decisions for common scenarios
Editing a customer email
Copy only the wording that needs editing. Replace the customer’s name, order number, address and case history. Use an approved account, confirm the improvement setting and avoid an automatic connector to the customer relationship system.
Debugging production code
Remove API keys, tokens, proprietary algorithms, customer records, hostnames and internal comments. Reproduce the error with synthetic values. Confirm that the coding assistant does not index the repository or send files to an unapproved subprocess.
Summarising an HR, legal or medical document
Obtain the responsible specialist’s approval first. De-identify the document, retain only the passages necessary for the question, verify the processing region and contract, and document the exact model and retention configuration. If those controls cannot be established, do not upload the original.
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Send sensitive data only when authority, minimisation, product identity, training use, retention, access, downstream processing, security and contractual fit are all understood for the exact route. Consumer, business and API controls can differ, and even a confirmed no-training setting leaves separate questions about logs, safety review, application state and deletion. When one critical answer is unknown, pause or use a genuinely de-identified version.
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