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If you paste a customer record, internal source code, or confidential draft into a hosted AI assistant, treat that information as disclosed to the provider for processing. That does not by itself mean the provider trains on it, keeps it for a particular period, or sends it to a human reviewer. Those are separate questions—and the answers depend on the service, account, settings, and feature you use.
What does “the prompt leaves your network” mean?
A hosted AI service processes your request on infrastructure operated by the provider or its service partners. The prompt—and any text or files you attach—must reach that service to be processed. For security planning, treat submission as a data disclosure to the provider, even when the provider says it does not use that content to train models.
This is a boundary-crossing assumption, not a claim that every provider retains prompts, uses them for training, or exposes them to people. It helps teams make the decision before sending sensitive information: is this particular service and configuration appropriate for this data?
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Training and model improvement policies vary by product and account type. A no-training default is not the same as a promise that the provider never processes or stores the content. OpenAI and Anthropic illustrate why the exact service scope matters:
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- OpenAI consumer services: OpenAI says it may use content from services for individuals, including ChatGPT and Codex, to train models. Its Help Center article describes an opt-out path; review the current settings and wording for the product you use. OpenAI: How your data is used to improve model performance.
- OpenAI business and API products: OpenAI says inputs and outputs are not used for training by default. OpenAI business data.
- Anthropic commercial products: Anthropic says, “By default, we will not use your inputs or outputs from our commercial products to train our models.” Its policy describes exceptions such as feedback or opting in. Anthropic: Is my data used for model training?.
These are provider statements about specified products, not a universal rule for hosted AI. Check whether the policy applies to your account, workspace, or API use, and whether your organization has changed the relevant settings.
Does “not used for training” mean the prompt is not stored?
No. Training use, abuse monitoring, and application state are different data-handling purposes. A provider can exclude content from model training and still keep logs or state needed to operate a service, subject to the applicable policy and controls.
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OpenAI API abuse-monitoring logs
OpenAI’s API documentation says abuse-monitoring logs may include prompts and responses. By default, those logs are generated for API feature usage and retained for up to 30 days, unless OpenAI is legally required to retain them longer. The documentation describes qualifications and controls, including eligibility for Zero Data Retention; this is an OpenAI API policy, not a retention rule for every provider, product, or endpoint. OpenAI API data controls.
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API application state
Some API endpoints and features can store application state separately from abuse-monitoring logs. The endpoint and feature determine what is stored and how the relevant controls apply. Consult the documentation for the exact capability you plan to use rather than assuming that a general no-training policy or a Zero Data Retention designation covers every feature. OpenAI API: Your data.
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What happens when you submit feedback?
Feedback can create a separate disclosure path. Anthropic says that when a user submits feedback, it stores the related conversation in its secured backend for up to 10 years. That figure applies to the conversation associated with feedback; it does not establish how long ordinary commercial prompts are retained. Anthropic Privacy Center.
Consider what a feedback action, bug report, or support request includes before sending it. A conversation attached to feedback may be handled differently from an ordinary prompt, so do not assume that the prompt’s usual training or retention treatment answers the feedback question.
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How to compare AI services before sending sensitive data
A “private” or “not used for training” label does not answer every data-handling question. Compare the precise service and account configuration across these separate axes:
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|---|---|---|
| Service and account scope | Is this a consumer account, organization workspace, API, or a particular model and region? | Policies can attach to a specific product, plan, or region. |
| Training and improvement | What is the default? Can the account opt out or in? Does feedback change the treatment? | Excluding data from training does not settle storage or feedback handling. |
| Abuse monitoring | Can prompts or responses be logged, for how long, with what exceptions, and what controls are available? | Logs can exist for purposes other than model training. |
| Application state | Does the endpoint or feature store state, and what does its documentation say about that storage? | API behavior can vary by feature even within one platform. |
| Retention-control eligibility | Can this organization use controls such as Zero Data Retention, and do they cover this feature? | A named control may require eligibility or approval and may not apply to every capability. |
| Feedback and support | What conversation or files are included when users send feedback, bug reports, or support requests? | User-initiated submissions can follow separate handling and retention practices. |
A practical check before submitting a prompt
- Identify the exact destination. Confirm the provider, product, account type, workspace, endpoint, and feature—not just the model name.
- Read the applicable data terms. Verify training settings, abuse-monitoring retention, application-state behavior, feedback handling, and any exceptions for the relevant account.
- Check control eligibility. Confirm that the settings or retention controls your organization expects are active and apply to the capability being used.
- Minimize what you send. Remove unnecessary customer identifiers, secrets, credentials, and confidential details; use redacted or synthetic examples when they can do the job.
- Use an approved route for sensitive work. If the product’s scope or controls are unclear, do not put the sensitive material into the prompt until your organization has verified an acceptable service and configuration.
- Review feedback separately. Before reporting an issue or rating a response, check whether the conversation or attached files will be submitted with it.
Provider policies and feature behavior can change. The OpenAI and Anthropic statements above were checked on October 5, 2026; verify current terms and settings before relying on them. These provider documents describe product policies, not jurisdiction-specific legal advice.
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