The safest prompt contains the least sensitive data needed to get a useful answer. Remove names, contact details, account numbers, credentials, private code, and identifying combinations before you send or save anything. Replace unavoidable identifiers with neutral labels, keep the mapping outside the AI tool, and use only a service and account authorized for that data.
Privacy settings still require interpretation. Turning off model improvement does not automatically erase chat history, memory, application state, abuse-monitoring logs, administrator access, or connected-source data. Treat minimization, authorization, retention review, and careful agent permissions as separate controls.
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Start with data minimization, not a privacy toggle
Before writing a prompt, ask what the model actually needs to perform the task. If a generalized example, synthetic record, or placeholder will work, use it instead of the real material. The Office of the Privacy Commissioner of Canada recommends anonymized or de-identified information where possible and reasonable, and says sensitive or confidential information should be entered only where authorized. Its guidance states: “Where personal information (and, in particular, sensitive or confidential information) must be entered into a prompt, only do so where authorised.” Read the commissioners’ principles at priv.gc.ca.
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Minimization protects both a prompt that is sent and a prompt that is later saved in history, a team workspace, a reusable template, an application database, or a support export. It also limits the harm if an agent, administrator, integration, or compromised account can access the conversation.
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Classify what you are about to paste
| Information in the draft | Default treatment | Safer substitute |
|---|---|---|
| Names, email addresses, phone numbers, street addresses | Remove when identity is irrelevant | [CLIENT], [EMPLOYEE], or a fictional contact |
| Account, customer, ticket, invoice, or order numbers | Remove or replace | [ACCOUNT_ID] or a short non-identifying label |
| Passwords, API keys, session cookies, recovery codes, private certificates | Never include | Dummy values such as REDACTED_TOKEN |
| Private source code or configuration | Send only the smallest relevant excerpt to an approved tool | A reduced reproduction with secrets and proprietary names removed |
| Health, financial, employment, legal, or regulated records | Use only with explicit authorization and an approved environment | Synthetic or aggregated examples |
| Rare combinations of dates, locations, job titles, or events | Generalize combinations that could identify a person | Broader ranges, altered dates, or fictional scenarios |
Several harmless-looking fields can identify someone when combined. A job title, small town, exact incident date, and unusual medical condition may be more revealing together than any one field alone. Remove or broaden combinations unless each detail is necessary.
A six-step pre-send routine
- Identify the data class. Mark whether the prompt contains personal information, customer or employee data, confidential business content, credentials, private code, or regulated records. If you cannot classify it, pause and ask the data owner or administrator.
- Reduce the task to its required inputs. Delete names, exact addresses, full documents, unrelated correspondence, hidden metadata, and unnecessary dates. Quote only the paragraph, row, or error needed for the answer.
- De-identify consistently. Replace each person or organization with a stable label such as
[CLIENT_A]. Keep the name-to-label mapping in a separate, access-controlled location if you must restore names later. Do not paste the mapping into the same conversation. - Remove secrets before transmission. Search pasted text and code for passwords, keys, tokens, cookies, connection strings, private certificates, and recovery codes. Replace them with dummy values. A secret that was accidentally exposed should be rotated under your organization’s incident process.
- Verify authorization and environment. Confirm that the specific service, account, workspace, plan, and connected sources are approved for the data class. Being able to paste information into a tool is not authorization.
- Review the saved result. Before clicking save, sharing, creating a template, or enabling memory, check the prompt, attachments, generated answer, and conversation title for identifiers and secrets. Remove or redact unnecessary history.
Example: turn a real support request into a safe one
An unsafe draft might say:
Explain why Maria Jensen at 14 Oak Street cannot log in. Her account is 847219, her temporary password is P@ss-4831, and the full server log follows.A minimized version could say:
A customer in Canada receives HTTP 401 after a password reset. The account is active. Here are the 12 log lines around the failure, with names, addresses, IDs, tokens, and hostnames replaced by [REDACTED]. List likely causes and a diagnostic sequence.The second prompt preserves the error pattern and region needed for troubleshooting while removing identity and authentication material.
Keep credentials and private code out entirely
Passwords, API keys, bearer tokens, cookies, recovery codes, private certificates, and database connection strings are authentication secrets, not ordinary context. Do not ask an AI tool to store them, explain them, or “temporarily” process them. Use dummy strings in examples and test code in a safe environment.
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For private source code, provide the smallest self-contained reproduction. Remove repository URLs, internal hostnames, customer data, comments that reveal strategy, and credentials embedded in configuration. Preserve line numbers and error text when they are needed for diagnosis, but do not upload an entire repository merely because it is convenient.
Authorization is separate from technical access
A company policy should define which information classes may be processed by which AI services and accounts. Approval should cover the actual workspace and integrations, not just a brand name. Confirm whether the account is personal or organization-managed, whether administrators can access content, which region handles data, and whether connected drives, mailboxes, browsers, or ticket systems are in scope.
When authorization is unclear, ask the data owner, privacy officer, security team, or workspace administrator. Do not infer permission from a colleague’s practice, a free trial, or a service’s ability to accept the paste box input. The Canadian commissioners’ principles recommend appropriate-use policies and technical measures to prevent inappropriate use; organizations can translate those principles into classification rules, approval workflows, and monitoring.
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Understand what each privacy control actually changes
“Not used for training” answers only one question: whether a provider may use content for model improvement under that setting. It does not answer whether a conversation appears in history, remains in application state, creates a memory, is available to administrators or support staff, is copied into abuse-monitoring logs, or is retained to meet legal or security obligations.
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| Control or question | What to verify |
|---|---|
| Model-improvement use | Are prompts and responses eligible to improve models, and does feedback change that treatment? |
| Conversation history | Does the chat appear in the account, workspace, browser, or export history? |
| Memory and personalization | Can details be stored and reused in later chats? Can they be inspected and deleted? |
| Application state | Does an app, plug-in, agent, or workflow retain files, variables, indexes, or cached results? |
| Retention and deletion | How long are prompts, attachments, logs, and backups kept, and what does deletion remove? |
| Human and administrator access | Can support, security, compliance, or workspace administrators view content? |
| Connected-source permissions | Can the AI read mail, files, websites, code repositories, or other systems beyond the prompt? |
| Contract and residency | What contractual commitments, processing regions, and data-residency options apply to this plan? |
Read the provider’s current terms and your organization’s contract for each item. A temporary-session label or an improvement opt-out is not a universal erasure promise.
OpenAI consumer controls: what the documented settings say
OpenAI’s current consumer help documentation says individual users can turn off Improve the model for everyone in ChatGPT Data Controls or opt out through the Privacy Portal. It says either route is sufficient for ChatGPT conversations and related tasks, but settings can change, so check the current page before relying on them: OpenAI’s data-use help article. The same documentation cautions that if an individual submits feedback, the associated conversation may be used to improve services even after opting out. Do not submit feedback containing sensitive material unless that use is authorized.
Temporary Chats
OpenAI describes Temporary Chats as not used for model improvement while temporary, absent from history, and not creating or updating memories. Saving a Temporary Chat converts it to a regular chat. Those are product-specific behaviors, not a promise that every AI service treats temporary sessions the same way. Avoid entering information merely because a session is labeled temporary; first confirm retention, deletion, support access, and connected-tool behavior for the product you are using.
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OpenAI says inputs and outputs for ChatGPT Business, Enterprise, Edu, and the API are not used to improve models by default. Its enterprise privacy information describes organizational control over retention for certain plans and connected internal sources: OpenAI enterprise privacy. “By default” does not replace your organization’s configuration review. Confirm the workspace policy, administrators, enabled connectors, user provisioning, and deletion process.
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For the API, OpenAI’s platform documentation distinguishes abuse-monitoring logs from application state. It says abuse-monitoring logs may include prompts and responses and are retained for up to 30 days by default, subject to exceptions where longer retention is required by law or reasonably necessary to protect the service or others. That period is a service-policy limit, not a guarantee that every copy of application data disappears after 30 days. Read the current controls at OpenAI Platform data controls.
OpenAI’s security and privacy overview is another reference for the provider’s stated controls: openai.com/security-and-privacy. Use primary documentation and your contract to determine what applies to your region, plan, endpoint, and integrations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare another AI service for sensitive work
Do not rank services by a single “training” statement. Build a question-by-question record for the exact product and plan:
- Model improvement: Are prompts, outputs, uploaded files, and feedback excluded by default or only after an account setting?
- Retention: What logs, backups, abuse-monitoring records, and application state are kept, for how long, and under what exceptions?
- Deletion: Can users or administrators delete conversations, attachments, memories, indexes, and exports? How quickly do deletions propagate?
- Access: Which support, security, compliance, and workspace roles can view content, and are access events recorded?
- Connected sources: What can an agent or plug-in read or write, and can permissions be limited per task?
- Contract and location: What data-processing terms, residency choices, and breach-notification commitments apply?
Microsoft’s documentation on enterprise data protection in Microsoft Copilot and Microsoft Copilot Chat illustrates the kind of product-specific commitments to examine: Microsoft Learn. Its statements do not automatically apply to other Microsoft products, consumer accounts, or unrelated AI services.
Agents create an additional access and instruction risk
An AI agent may read a web page, email, document, ticket, or repository in addition to your typed prompt. OpenAI defines prompt injection as social engineering in which third-party content embeds instructions intended to redirect the AI. A malicious page could tell an agent to disclose a file, forward an email, change a record, or ignore your original task. See OpenAI’s explanation of prompt injections.
Reduce the blast radius
- Grant only the mailbox, folder, repository, or site needed for the task; avoid broad “all files” permissions.
- State a narrow objective and explicitly forbid sharing secrets, changing permissions, or following instructions found in untrusted content.
- Separate reading from acting where the product allows it. Require a human confirmation before sending messages, changing records, publishing content, or making purchases.
- Inspect the agent’s proposed recipients, files, links, and mutations before confirming consequential actions.
- Use sanitized test data when evaluating a new agent or connector.
These steps reduce exposure; they cannot guarantee that an agent will never reveal data. Treat external content as untrusted input, even when it appears inside a familiar document or internal message.
Controls organizations can enforce
Individual caution works best when backed by policy and technical guardrails. An organization can define data classes such as public, internal, confidential, and restricted; list approved AI services for each class; require managed accounts; and prohibit secrets and regulated records in general-purpose prompts.
Possible implementation categories include data-loss-prevention filters, enterprise browser controls, upload restrictions, connector allowlists, redaction libraries, audit logs, and periodic access reviews. The Canadian privacy commissioners recommend appropriate-use policies and technical measures, but there is no universally suitable vendor or configuration. Test filters against realistic examples: a key hidden in a code block, a customer name in an attachment, an image containing an address, or a rare combination of quasi-identifiers.
Training should explain the difference between anonymization and simple masking. Replacing a name while leaving an exact address, timestamp, and unusual role may still identify someone. It should also teach staff to inspect generated summaries and saved templates, because sensitive details can be copied into a title, instruction, or example even after the original attachment is deleted.
What to do after sensitive data is saved
- Stop further distribution. Do not share the conversation, export, link, or generated answer while assessing the exposure.
- Classify the material. Record what was included, which account and workspace received it, and whether it contained credentials, personal information, regulated records, or confidential business data.
- Contain secrets immediately. Revoke or rotate exposed credentials using the relevant process. Disable compromised integrations if necessary.
- Use the product’s deletion controls. Remove the chat, file, memory, template, or connector record where possible, while recognizing that deletion may not cover logs, backups, screenshots, exports, or recipients.
- Notify the responsible team. Contact security, privacy, legal, or the data owner according to your incident policy. They can determine notification and evidence-preservation duties.
- Prevent recurrence. Update the prompt template, browser or DLP rule, connector scope, and user guidance that allowed the disclosure.
Do not claim that a deletion button proves universal erasure. Ask the provider or administrator what the control removes and what retention exceptions apply.
A practical decision rule before you click Save
Save a prompt only when all four answers are yes:
- Necessity: Every included detail is needed for the task.
- Authorization: The data owner and your policy permit this service, account, and connector.
- Control: You understand model-improvement use, history, memory, application state, retention, deletion, and access.
- Reversibility: You could explain the saved content to the affected person or organization and respond if it were later exposed.
If any answer is no, stop, generalize the prompt, use synthetic data, or move the task to an approved environment. Privacy settings are useful layers, but the most reliable protection is never putting unnecessary sensitive information into the prompt in the first place.
Quick Recap
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