AI chatbots can process more than the words you type: depending on the product and feature, they may also handle responses, uploaded files, voice, images, screen content, or information from connected apps. Providers may retain some data for service operation, safety, or product improvement, and eligible interactions may be used to train models under product-specific terms and controls. Retention and training are separate: turning off a training-related setting or deleting visible chat history does not necessarily erase every retained copy or stop safety processing.
The examples below cover consumer ChatGPT, Claude, and signed-in Gemini Apps based on the providers’ official disclosures available on October 3, 2026. They do not establish a rule for every chatbot, account type, region, or workspace.
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What data can a chatbot receive?
At a minimum, a chatbot processes the prompt you submit and the response it generates. What else it receives depends on the app, the feature you use, and the permissions you grant. Google’s Gemini Apps notice, for example, lists typed or spoken prompts and shared files, videos, screens, photos, imported chats, and browser-page content. These examples show why a conversation can contain more information than its text box suggests; they are not a universal inventory for every provider. Google’s Gemini Apps Privacy Notice
- Typed or spoken content: Questions, instructions, and information included in a prompt.
- Attachments and captured content: Files, images, video, or screen content where the product supports those features.
- Connected-app context: Content or derived information accessed through an integration, subject to the app and the permissions in use.
- Responses and feedback: The chatbot’s output and any rating or feedback you submit may also be handled under the provider’s terms.
Before using a feature with sensitive material, check its permissions and the provider’s notice for that specific input type. A policy about ordinary text prompts may not fully describe how voice, files, or connected services are handled.
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How retention differs from model training
Retention asks how long data is kept and for what purpose. Training asks whether data may be used to improve or train models. A provider may retain a conversation to deliver a response, handle feedback, or protect the service even when that conversation is excluded from model training. Likewise, deleting a chat from the visible history does not necessarily undo separate safety-review or retention processes.
Google illustrates the distinction for Gemini Apps: when Keep Activity is off, future chats are not used to train models unless the user submits feedback, but they are still retained for 72 hours for responding, feedback processing, and safety purposes. That is a Google rule for this setting, not a general chatbot retention period. Google’s Gemini Apps Privacy Notice
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How the three providers’ published practices compare
These disclosures apply to different products and account contexts, so they are not a like-for-like privacy ranking. Check the live notice and controls for your own account, region, and plan.
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|---|---|---|
| ChatGPT, including OpenAI business offerings | OpenAI says ChatGPT Business, ChatGPT Enterprise, and the API do not use inputs and outputs for training by default. OpenAI describes model-improvement practices separately from general service-data handling and says certain interaction data may be retained; it also says it takes steps to reduce personal information in training datasets. OpenAI model development and training | ChatGPT data controls include options related to model training, chat history, export, and account deletion. Managed workspaces may have organization-level retention and access settings. The linked controls page describes ChatGPT options; applicable settings can vary by workspace. ChatGPT data controls |
| Claude consumer chats and coding sessions | Anthropic’s consumer privacy pages describe when chat or coding-session data may be used to improve models, including user controls and exceptions such as content flagged for safety review. Anthropic on model-training use | Anthropic says users can delete conversations and that a deleted chat will not be used to train future models. For consumer chats or coding sessions allowed for model improvement, Anthropic says data may be kept in de-identified form in training pipelines for up to five years. This is a conditional training-pipeline period, not a general promise about how long Claude keeps chat history. Anthropic on consumer retention |
| Signed-in Gemini Apps | With Keep Activity on, saved activity may be used to provide, develop, and improve Google services, including generative-AI training, with help from human reviewers. With Keep Activity off, future chats are not used to train models unless the user submits feedback. Google’s Gemini Apps Privacy Notice | With Keep Activity on, the default activity auto-delete period is 18 months; Google says it can be changed to three months, 36 months, or no auto-delete. Chats reviewed by human reviewers are disconnected from the account and may be kept for up to three years, including after activity is deleted. With Keep Activity off, future chats are retained for 72 hours for response, feedback processing, and safety. These periods refer to the stated Gemini settings and purposes, not every Google service or account. Google’s Gemini Apps Privacy Notice |
Connected apps can contribute derived information
Connecting another service can expand what a chatbot uses to answer a prompt. Google says summaries, excerpts, generated media, and inferences from connected content may be used to answer prompts and train models when Keep Activity is on. It also says it does not train generative-AI models directly on the full Gmail inbox, Drive, Contacts, Calendar, Wallet/payment services, or imagery and audio in a Google Photos library. That distinction does not mean connected-app information is never involved: Google says derived summaries, excerpts, generated media, and inferences may be used. Google on connected-app personalization
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For any connected service, assess both the source content and what the chatbot may derive from it. Review the connection and its permissions separately from chat-history and training controls.
What to check before sharing sensitive information
- Identify the exact product and account. Consumer app, business workspace, API, school account, and signed-out session may be governed by different settings or terms. OpenAI, for example, explicitly distinguishes its business offerings from other model-improvement practices. OpenAI model development and training
- Check the input types the feature handles. Look beyond typed prompts to uploads, voice, images, screens, browser content, and connected apps where applicable. Google’s Gemini Apps Privacy Notice
- Find the training or improvement control. Confirm whether it is opt-in or opt-out for your product and account, and whether exceptions apply, such as feedback or safety review. Controls are not interchangeable with chat history or deletion.
- Read the retention and deletion terms. Check what happens to visible history, data retained for service or safety purposes, and any content already selected for review or model-improvement workflows.
- Review integrations and workspace policies. Disconnect services you do not need, inspect their permissions, and ask a workspace administrator about organization-level retention and access settings where relevant.
- Recheck current notices and settings. Provider policies, controls, regional rules, and interface labels can change; the linked official notices are the best place to verify the terms currently shown for your account.
If a piece of information would create unacceptable risk if stored, reviewed, or exposed through an account or integration, the prudent choice is not to submit it. No single setting should be treated as a guarantee that data is never retained or processed for safety.
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Why there is no universal chatbot data rule
The official disclosures for ChatGPT, Claude, and Gemini Apps demonstrate materially different controls, retention statements, and account distinctions. They do not establish an industry-wide collection amount, a share of conversations used for training, or which provider is “most private” across every use case. Microsoft Copilot, Meta AI, xAI Grok, Amazon Nova, and other services require their own current policy checks; this comparison does not cover them. Apply the same questions to any chatbot: what it receives, what settings govern model improvement, what remains after deletion, and whether separate safety or reviewer retention applies.
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