The Tool Desk
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Use the audit below before putting personal, business or regulated information into an AI service. It separates training, storage, deletion, memory, feedback, files and third-party processing so you can verify each one instead of relying on a single “privacy” label.
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Start with the six questions that determine your exposure
- Is the content used for model improvement? Find out whether use is on by default, whether you can opt out or must opt in, and whether the control covers prompts, outputs, uploaded files, feedback and conversations sent for safety review.
- How long is each data type retained? Chat history, temporary sessions, API logs, files, retrieval indexes, caches, voice or video sessions and abuse-monitoring records may all have different clocks.
- What does deletion actually remove? Deleting a chat, clearing activity, disabling training and deleting an account are separate actions. Look for backup, legal, security and safety exceptions.
- Does temporary or incognito mode change every rule? It may prevent history and training use but still permit safety retention. Saving a temporary chat can convert it to ordinary history.
- Who controls the data? Consumer accounts, business workspaces, API organizations and cloud-hosted deployments can have different defaults, administrators and processors.
- Is “zero data retention” enabled for this exact request? ZDR can be limited to particular endpoints, models and features, and may not cover grounding, files, caches, tools or a cloud provider’s own logs.
Record the answers with the provider name, product, plan, sign-in state, workspace owner, region, date and access method. A screenshot or dated note is useful because settings and terms change.
Training controls do not delete existing conversations
A model-improvement toggle answers whether eligible new content may be used to improve models. It does not normally erase saved chats, remove files or purge copies already held for safety, legal or operational reasons. Check whether the setting applies only to future prompts, the whole account or individual conversations, and whether it follows you across devices.
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Also check what happens when you submit a rating, report a bug or otherwise send feedback. A feedback action can attach the entire related conversation for review even when a general training opt-out is enabled. Treat feedback as a separate data-sharing event.
Run a complete settings audit
1. Identify the exact product and account
- Write down whether you are using a consumer app, a business or education workspace, an API, a developer console, a managed agent or a cloud-marketplace deployment.
- Confirm the account, organization and workspace administrator. An administrator’s retention or memory policy can override what an individual user expects.
- Note the region and any sector-specific agreement. Contractual controls may differ from public consumer settings.
2. Locate model-improvement or training settings
- Open the provider’s privacy or data-controls page.
- Read the scope: new conversations only or historical content as well; prompts and outputs only or also files, images, audio, memory and feedback.
- Turn the setting off or opt in only when your policy permits it.
- Save a dated screenshot. Check another signed-in device to confirm the account-wide behavior.
3. Audit history, deletion and backups separately
Find the chat-history page and the deletion explanation. Determine whether deletion is immediate in the interface but completed in backend systems later, and whether backups, fraud investigations, legal holds or safety reviews extend retention. Check archived chats, project spaces and shared conversations; they may follow different rules.
4. Test temporary or incognito behavior on paper before relying on it
- Does the conversation appear in history?
- Does it create or update memory?
- Is it excluded from model improvement?
- How long can a safety copy remain?
- What happens if you save, export or share it?
- Can existing custom instructions, plugins or integrations still receive the prompt?
5. Check memory and personalization
Memory is not the same as chat history. A temporary conversation may be excluded from history while still reading existing saved memories, custom instructions or connected tools. Turn personalization off at the start of a sensitive session if the product allows that choice, and review and delete stored memories separately.
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List uploaded files, retrieval or grounding sources, context caches, voice and video sessions, connectors, browser tools, plug-ins, exports and support tickets. Each can have its own retention period and may send content to another processor. “The chat is not used for training” does not mean every attached file or tool log disappears immediately.
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7. For work and API use, verify the contract and configuration
Ask the administrator which policy applies to the organization, endpoint, model and feature. Confirm whether ZDR is actually enabled, not merely advertised as available. Identify whether a cloud host, integration vendor or managed service receives the request and which party is responsible for deletion and security logging.
Current OpenAI and ChatGPT controls
Consumer ChatGPT
In ChatGPT, the current control is Settings > Data controls > Improve the model for everyone. OpenAI states that when this is off, new conversations are not used to train OpenAI models. Chats can still remain visible in chat history, and the setting applies to the signed-in account across devices. Turning it off is therefore not a deletion request; delete individual chats or the account separately.
Temporary chats
Temporary chats stay out of history, do not create or update memories and are not used to improve OpenAI models while they remain temporary. OpenAI may retain a copy for up to 30 days for safety purposes. Saving a temporary chat turns it into a regular chat. A personalized temporary chat can still use existing memories, custom instructions and plugins unless personalization is disabled when the chat starts.
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Thumbs-up or thumbs-down feedback can cause the entire associated conversation to be used for training. Do not submit feedback from a sensitive conversation unless your organization has approved that disclosure.
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Business, education, healthcare and API use
OpenAI says content from ChatGPT Business, Enterprise, Edu, ChatGPT for Healthcare workspaces and the API Platform is not used by default to improve models. Organizations can choose to share data where that option is available, and workspace policies can govern retention, access, memory and compliance. Verify the organization’s actual settings and agreement rather than applying consumer ChatGPT assumptions to an API key or shared workspace.
Current Anthropic and Claude controls
Consumer chats and incognito mode
Anthropic’s consumer privacy information says chats and coding sessions may be used to improve Claude when a user allows it, when a conversation is flagged for safety review or when the user otherwise explicitly opts in. Incognito chats are not used to improve Claude even when Model Improvement is enabled.
Feedback is a specific exception: Anthropic can store the entire related conversation in a secured backend for up to five years. That period describes feedback-related retention, not the ordinary lifetime of every consumer chat.
Commercial products and API deletion
Anthropic’s published standard commercial policy says API inputs and outputs are automatically deleted from backend storage within 30 days. Longer retention can apply to particular features, agreed arrangements such as ZDR, usage-policy enforcement or legal compliance. Deleting a saved commercial-product conversation removes it from history immediately and from backend systems within 30 days, subject to those exceptions.
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Flagged policy violations can be retained for up to two years for the related inputs and outputs, while trust-and-safety classification scores can be retained for up to seven years. These are safety records, not ordinary chat-history periods.
Anthropic API ZDR scope
Anthropic’s API ZDR arrangement is enabled per organization and limited to eligible features and endpoints. The described arrangement does not cover consumer plans, Console, Managed Agents and several other product paths; some models and features may also require retention. Check the current eligibility table for your exact endpoint. If you call the API through a cloud platform, establish which company processes the request and which retention policy applies.
Current Google Gemini Developer API controls
Paid Services and model improvement
Google says prompts and responses sent through Gemini API Paid Services are not used to improve Google products. That statement addresses product improvement, not every form of storage. Grounding, stateful APIs, files, caches and live sessions can retain data for operational reasons.
Storage exceptions to check
| Feature or path | Stated handling | What to verify |
|---|---|---|
| Search or Maps grounding | Prompts, context and output can be retained for 30 days. | Whether your request uses grounding and which data is included in the stored record. |
| Interactions API | State is stored by default unless the request sets store to false. |
That the parameter is present on every request where storage is not wanted. |
| File API | Uploaded assets remain until deleted or they expire. | Deletion and expiration for each file, not just the conversation that referenced it. |
| Context caches | Explicit caches follow their configured time-to-live. | The TTL, cache contents and whether a later request can reuse them. |
| Live API | Session state can persist for up to 24 hours when a session handle is generated. | Whether your integration creates a handle and when it is invalidated. |
| Abuse monitoring | Google describes limited prompt logging for abuse monitoring. | What limited logging covers and which policy governs access and deletion. |
How to evaluate “zero data retention” claims
ZDR is a scoped configuration, not a promise that no system anywhere sees or records a request. Ask for the eligible endpoint and model list, required request flags, excluded features, safety and abuse logs, billing records, backups, support access and processor responsibilities. Grounding, file uploads, caches, live sessions and tool calls may sit outside the ZDR path.
For an API organization, document the exact project, key, endpoint, model, region and date on which ZDR was enabled. Recheck after changing models or adding a feature. If a cloud host fronts the request, obtain its retention terms as well as the underlying model provider’s terms.
Retention and training comparison
| Service context | Training or improvement rule | Notable retention detail | Important exception |
|---|---|---|---|
| ChatGPT consumer | New conversations are excluded when Improve the model for everyone is off. | Temporary-chat safety copy may remain up to 30 days; ordinary chats can remain in history until deleted. | Feedback may submit the full conversation for training. |
| ChatGPT Business, Enterprise, Edu, Healthcare and API | Not used by default; organization sharing choices may apply. | Depends on workspace policy or API agreement. | Do not infer consumer settings or ZDR from the product name alone. |
| Claude consumer | Improvement use depends on user choice, safety review or explicit opt-in; incognito chats are excluded. | Feedback-related conversations may be stored up to five years. | Safety-flagged content can follow separate rules. |
| Claude commercial/API | Contract and product policy govern use. | Standard API backend deletion is within 30 days. | Longer retention can apply to ZDR arrangements, policy enforcement or legal compliance; flagged inputs/outputs up to two years and safety scores up to seven years. |
| Gemini API Paid Services | Prompts and responses are not used to improve Google products. | Grounding data 30 days; Live API session state up to 24 hours; files and caches follow their own rules. | Interactions API stores state unless store=false; limited abuse logging may occur. |
Common mistakes and the safer correction
- “I opted out, so my old chats are gone.” No. Disable training and delete history as separate actions.
- “Incognito means zero retention.” No. Safety copies, feedback records and tool or processor logs can remain.
- “The API policy applies to the consumer app.” No. Product and account tier determine the rule.
- “ZDR covers every feature.” No. Confirm endpoint, model, feature and configuration eligibility.
- “Deleting the visible chat deletes the uploaded file.” Not necessarily. Check file storage and expiration directly.
- “A temporary chat cannot read personal context.” It may still use existing memory, custom instructions or plugins unless personalization is disabled.
- “A provider can remove my text from a model that already trained on it.” The policies above describe future use and storage controls; they do not promise removal from previously trained model weights.
A practical decision rule for sensitive information
- Classify the data under your organization’s rules before sending it.
- Prefer a business agreement or API arrangement with documented controls over an unapproved consumer account.
- Use the narrowest feature set: avoid unnecessary memory, feedback, uploads, grounding, connectors and persistent sessions.
- Enable the applicable opt-out or ZDR setting and verify it for the exact endpoint.
- Delete chats, files, caches and account data separately when the work is complete.
- Keep a record of the provider policy, setting state, retention window and any exceptions.
If the provider cannot answer which system stores the data, for how long, and which party can access it, treat the service as unsuitable for confidential or regulated content until your security or legal owner approves it.
Frequently Asked Questions
Can a provider remove a prompt that was already incorporated into model training?
The settings described here control future use and retention; they do not establish that previously trained model parameters can be identified and removed. Ask the provider about its model-governance and legal process if this distinction matters.
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Why can a deleted chat still appear in a legal or safety record?
Deletion from user-visible history can be separate from records retained for abuse prevention, policy enforcement, legal compliance or backups. Check the product’s stated exceptions and deletion timeline.
What should a company document after enabling a privacy setting?
Record the organization, product, endpoint, model, feature flags, region, administrator, date and a screenshot or policy version. Repeat the check after adding grounding, files, tools or a different model.
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