When you use an AI coding assistant, some combination of your prompt, selected files, nearby code, cursor position, conversation history, and sometimes wider environment context is sent to a service that generates a response. Whether that data is stored or used to improve models is a separate question, controlled by the product, account type, plan, settings, and organization policy. Treat every assistant as a data-processing system, not as an offline editor, until its documented controls show otherwise.
What an AI coding assistant can receive
The text you type is only one possible input. An assistant may receive the code surrounding your cursor, the file currently open, adjacent files, project metadata, previous conversation turns, and instructions supplied by the extension or hosting platform. The exact boundary changes by feature and configuration.
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Google Cloud’s documentation for Gemini Code Assist Standard and Enterprise explicitly lists developer queries, responses, conversation history, snippets from open and adjacent files, and cursor location as Customer Data. GitHub describes prompt context and, when a feature uses Bing, context that may help formulate a search query. OpenAI Codex has a separate setting controlling whether full environments are included in training-related data. These descriptions are product-specific; they do not prove that every assistant sends the same context.
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A short question such as “Why does this test fail?” can therefore transmit more than that sentence. If the extension gathers the open file and nearby imports, the service may process proprietary algorithms, comments, internal hostnames, or data embedded in fixtures. A generated answer can also include fragments of the supplied code. Context collection is usually designed to improve relevance, but it increases the material that must be governed.
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Before accepting a suggestion, identify which files and folders the feature can read. Exclude credentials, production dumps, private keys, and regulated data from the workspace or configuration where the assistant can reach them. Never paste a secret merely because the model asks for more context.
Training, retention, and telemetry are different
“Used for training” and “stored” describe different events. A provider can decline to train on your content while retaining it for abuse monitoring, debugging, billing, security, or an organization’s audit log. Conversely, a service can describe short-lived processing while collecting separate operational telemetry.
Model training
Training use means input, output, or related context may contribute to improving a model. Eligibility often depends on whether you use a personal subscription, a managed business workspace, an API, or a third-party host. An opt-out normally affects future improvement use; it does not automatically erase previous chats or logs.
Retention and deletion
Retention is how long prompts, responses, conversations, or logs remain available to the provider or your administrator. A product may be stateless for its main request path but still expose optional logging. Deletion windows, legal holds, backups, and account history can follow different rules, so look for the policy governing the exact surface you use.
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Telemetry
Telemetry can record events without preserving the full source file. Gemini Code Assist documentation, for example, identifies request and response events, acceptance or rejection reactions, and accepted-suggestion character counts. Such measurements are not equivalent to storing no data, and turning off model training does not necessarily disable them.
What the major assistants document
| Assistant and account surface | Documented data or control | Relevant qualification |
|---|---|---|
| OpenAI Codex on personal ChatGPT plans | The “Improve the model for everyone” control applies to Codex tasks. Codex also has an “Include environments” setting for training on full environments. | Training control and environment inclusion are separate settings; chat history can still follow the account’s history rules. OpenAI data controls |
| OpenAI API | API data is not used to train models by default. Abuse-monitoring logs may contain prompts and responses and are retained for up to 30 days by default. | Exceptions, approved retention controls, and endpoint features can change the period. OpenAI API data controls |
| Gemini Code Assist Standard and Enterprise | Google says it does not use customer data to train its models without permission. The service is described as stateless, with prompts and responses not stored in Google Cloud by default; logging can be enabled where required. | The same documentation lists code context and operational telemetry, and says regional processing is not guaranteed. Google Cloud security and privacy documentation |
| GitHub Copilot Individual | GitHub says interaction data, including prompts, suggestions, and code snippets, may be used for training and improvement, with an opt-out. | Context may be used in a Bing search when that feature is active. Eligibility and settings can change by plan. Copilot product page and responsible-use documentation |
| Anthropic Claude Code on consumer Free, Pro, or Max accounts | Chats and coding sessions may be used to improve Claude when the user allows it, when a conversation is flagged for safety review, or through another explicit opt-in. The related conversation may be included. | Commercial products and the API have separate terms and retention arrangements. Consumer training policy and API retention documentation |
OpenAI Codex: two controls that are easy to confuse
On personal ChatGPT plans, the account-level “Improve the model for everyone” choice governs whether Codex tasks can be used for model improvement. Codex separately exposes “Include environments,” which concerns training on full environment context. Disabling one does not imply that the other is disabled.
Business workspace data is not used to train by default according to OpenAI’s current data-controls material. That statement applies to the documented business workspace surface, not automatically to a personal account, an API integration, or a product embedding a model through another host.
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Gemini Code Assist: stateless processing does not mean zero collection
Google Cloud states, for Gemini Code Assist Standard and Enterprise, “Google doesn’t use your data to train our models without your permission.” The statement is scoped to that Google Cloud product documentation; it should not be generalized to every Google consumer service.
The same documentation describes the service as stateless and says prompts and responses are not stored in Google Cloud by default. Organizations can enable logging where required. It also identifies Customer Data that can include conversation history and snippets from open and adjacent files, plus cursor location. Telemetry can include request and response events, acceptance or rejection reactions, and accepted-suggestion character counts. Regional processing is not guaranteed, so location requirements need a separate review.
GitHub Copilot: individual and managed use can differ
GitHub’s product page says Copilot interaction data for Individual subscribers—including prompts, suggestions, and code snippets—may be used for training and improvement, and that subscribers can opt out. The current plan and settings determine eligibility, so check the account controls rather than relying on an older blog post or a team member’s configuration.
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GitHub’s responsible-use documentation also explains that context and prompts may be used in a Bing search when the relevant feature uses Bing. If you bring your own model key, the selected provider’s policies govern those prompts and responses. A company-managed Copilot deployment can therefore have materially different controls from a personal subscription.
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Claude Code: do not transfer consumer terms to the API
Anthropic’s consumer policy covers Free, Pro, and Max accounts, including Claude Code use on those accounts. It says coding sessions and chats may be used to improve Claude when the user permits it, after a safety flag, or through another explicit opt-in, and that the entire related conversation may be involved.
Anthropic documents commercial and API retention separately. An organization using an API key or a commercial agreement must evaluate those terms instead of applying the consumer policy by analogy. The account that authenticates the editor is the relevant starting point: a company account, personal account, and third-party integration are not interchangeable.
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- Identify the service surface. Record the extension or command, model provider, account type, plan, and whether a company proxy or “bring your own key” configuration is involved.
- Map the context boundary. Check which open files, adjacent files, repositories, terminals, environment variables, conversation history, and search integrations the feature can access. Treat cursor position and automatically attached snippets as data fields.
- Separate training from retention settings. Locate the model-improvement switch, then locate history, abuse-monitoring, diagnostics, and workspace logging controls. Turning off training answers only the training question.
- Apply administrator rules. In a managed workspace, confirm the organization’s retention, regional-processing, logging, and deletion requirements. An individual preference may not override an administrator policy.
- Remove high-impact secrets. Keep API keys, passwords, private certificates, tokens, customer records, production exports, and unannounced vulnerability details out of prompts and reachable files.
- Test with harmless material. Use a disposable repository to observe what context is attached, whether suggestions cite or quote files, and which telemetry or history indicators appear. A local observation does not replace the provider’s written policy.
- Document the decision. For regulated or proprietary projects, record the approved product surface, settings, data classification, and person responsible for reviewing policy changes.
How to reason about common situations
Public or open-source project
Training and retention may still matter for contributor privacy, undisclosed security fixes, and license obligations. Public visibility of a repository does not authorize sending credentials, private issue discussions, or personal data to a third party.
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Use a business or API configuration only after verifying its written data controls. Confirm whether prompts and outputs are logged, who can retrieve them, where processing occurs, and how an administrator can delete or export records.
Incident response or vulnerability investigation
Do not paste a live exploit, customer dump, private key, or unpatched vulnerability report into an assistant before your disclosure and security policies permit it. Redact identifiers and reproduce the behavior with synthetic data where possible.
Using a third-party extension
The editor brand is not the whole data path. An extension can send context to its own server, a model provider, a search service, or several of them. Identify every processor named in the extension’s privacy and configuration documentation.
What happens after you submit a prompt
- The client gathers the text and whatever context the feature is configured to attach.
- The request travels to the service or provider selected by the account, organization, or API key.
- The model generates a response, such as an explanation, code completion, patch, or tool call.
- The service may retain the request, response, conversation, or operational event under its documented policy.
- The client may display, cache, index, or send acceptance feedback about the suggestion.
- If training is permitted for that surface, eligible data may later enter an improvement pipeline; if not, it can still be processed for security, abuse monitoring, or operations.
This sequence explains why “the model did not memorize my repository” is not the same claim as “no system retained or processed the request.” Each stage has a different control and risk.
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What to do if you exposed a secret
- Revoke or rotate the credential immediately; deleting the chat alone does not invalidate a leaked key.
- Check provider, workspace, extension, and proxy logs to determine where the prompt and response traveled.
- Preserve the incident timeline and notify the security or privacy owner under your organization’s procedure.
- Review repository history, terminal transcripts, caches, and generated patches for copied secrets.
- Change the assistant’s context rules and add repository-level exclusions before resuming use.
Questions that a vendor policy should answer
- Which exact files, metadata, history, and environment values are transmitted?
- Are prompts, outputs, snippets, or full environments eligible for model training, and how is opt-out applied?
- What is the default retention period for content and abuse-monitoring logs?
- Can an administrator enable logging, export records, impose a region, or request deletion?
- Do API, business, consumer, and third-party-hosted versions use different terms?
- What telemetry is collected when a suggestion is accepted or rejected?
Policies change frequently. Recheck the product’s current documentation and your account settings when a model, plan, extension, or organization configuration changes.
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