The Tool Desk
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What does “the prompt leaves your network” mean?
With a hosted AI service, your device sends the prompt—and any attached text or files—to the provider’s service so it can generate a response. For practical security decisions, assume the provider receives the material. A no-training setting does not make the request stay on your company network, and it does not establish that the content cannot be logged or stored.
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This framing is about the data flow, not a claim that every provider retains every prompt or uses it to train models. The provider’s policies and technical controls determine what happens after processing.
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“Is my data used for model training?” is not the same question as “Is my prompt stored?” Content may be excluded from model training while still being processed, logged for abuse monitoring, or retained as application state by a particular feature. Feedback can create yet another handling path.
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- Training or improvement: whether content may be used to improve models, and whether that depends on settings, opt-in, or feedback.
- Abuse monitoring: whether prompts and responses can be included in logs used to detect misuse or protect the service, and how long those logs are kept.
- Application state: whether an endpoint or feature stores data to support its operation, independent of training.
- Feedback and support: what conversation or files are attached when you send feedback, report a problem, or contact support.
These categories should not be collapsed into a single “private” or “not private” label. A provider’s statement about one category does not settle the others.
How official policies illustrate the differences
The following examples come from provider documentation accessed on October 5, 2026. They describe specific products and pathways, not a universal rule for hosted AI.
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| Provider and scope | Training or improvement | Retention and other handling |
|---|---|---|
| OpenAI consumer services, including ChatGPT and Codex | OpenAI says content may be used to train models; its guidance describes an opt-out path. See How your data is used to improve model performance. | This consumer training statement does not establish a general retention period. Check the current product settings and terms for the account in use. |
| OpenAI business products and API | OpenAI says business and API inputs and outputs are not used for training by default. See OpenAI business data. | For API use, default abuse-monitoring logs can include prompts and responses and are retained for up to 30 days, unless law requires longer retention. Eligibility for controls and endpoint behavior vary; see API data controls and OpenAI API documentation on your data. |
| Anthropic commercial products | Anthropic says inputs and outputs from commercial products are not used to train its models by default, subject to explicit feedback or opt-in pathways. See Anthropic’s model-training FAQ. | When a user submits feedback, Anthropic says it stores the related conversation in its secured backend for up to 10 years. That figure applies to the feedback pathway, not ordinary commercial prompts. |
OpenAI’s API documentation states: “By default, abuse monitoring logs are generated for all API feature usage and retained for up to 30 days, unless we are legally required to retain the logs for longer.” This is an API-specific statement, not a retention promise for every OpenAI product or other provider.
What to check before sending sensitive material
Before adopting an AI tool for work or entering confidential information, establish the scope of the exact service. Provider policies can differ between an individual account, an organization workspace, and an API integration; API behavior can also vary by endpoint or feature.
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- Identify the service and account. Confirm the provider, product, plan, organization configuration, and any region-specific terms that apply.
- Read the training rule. Determine whether inputs and outputs are used for model improvement by default, whether an opt-out or opt-in changes that, and whether feedback is treated separately.
- Check monitoring and retention. Look for whether prompts or responses can appear in abuse-monitoring logs, the stated retention period, exceptions, and any controls your organization is eligible to use.
- Check feature-level storage. For API integrations, verify the exact endpoint and feature rather than assuming one API-wide rule. OpenAI’s API documentation distinguishes abuse-monitoring logs from application state and describes endpoint-specific behavior.
- Review feedback and support flows. Find out whether submitting feedback or a support request includes the conversation or files, and how that material is handled.
- Minimize what you send. Remove unnecessary identifiers, use synthetic or redacted examples where possible, and follow your organization’s approved-tool and data-handling rules.
What no-training and retention controls do—and do not—tell you
A no-training default answers a limited question: whether the provider uses covered inputs and outputs to train models under the stated conditions. It does not mean the provider never receives the prompt, nor does it establish that all copies, logs, or feature state are immediately deleted.
Likewise, a named control such as Zero Data Retention should be evaluated against its eligibility requirements and the particular API capabilities in use. Do not assume it covers every endpoint or feature simply because an organization has access to the control. Check the provider’s current documentation and confirm the configuration for the actual integration.
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A practical rule for teams
Classify a hosted AI prompt as a disclosure to the provider, then assess what is permitted after disclosure. If the data is too sensitive to share under the applicable provider terms and your organization’s policy, do not submit it; use a less sensitive example or an approved workflow instead. Provider documentation is not a substitute for jurisdiction-specific legal advice.
Policies and product controls change. The examples above reflect official OpenAI and Anthropic documentation accessed October 5, 2026; verify current terms, settings, and endpoint behavior before relying on them.
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