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To keep code private, control the entire data path—not just where the model runs. A local model can reduce exposure to a model provider, but an editor extension, agent tool, telemetry service, or session-sync feature may still send code off your device. With a cloud assistant, check the exact plan, model host, features, training terms, retention rules, and repository context being transmitted. Training and storage are separate questions: a no-training commitment does not necessarily mean prompts and outputs are never logged or retained.
What “private” means for an AI coding workflow
Privacy is a property of the configured workflow, not a label attached to a model. Trace what happens when you ask for help: which code or metadata the client gathers, where each request goes, what tools or providers handle it, and what is retained afterward.
- Transmission: Does the prompt, selected code, repository context, or session history leave your device? Which provider or host receives it, and where?
- Training: Can inputs or outputs be used to train or improve models? Is that the default, an opt-in, an opt-out, or subject to exceptions?
- Retention: Are prompts, responses, abuse-monitoring logs, feedback, and application state treated separately? For how long, and under which exceptions?
- Access and controls: Who can access the data, which repository and agent permissions are enabled, and what administrative or contractual controls apply?
- Workflow fit: Does the model and integration meet the task’s needs without sending more code or granting more access than necessary?
These questions are distinct. A provider can promise not to train on data while still retaining some prompts or responses for operational or safety purposes. Likewise, a local model runtime does not establish that every component around it is local.
Local models: verify the whole route, not just inference
Running inference on your own device can avoid sending prompts to a remote model host, but that benefit depends on the surrounding setup. The editor, extension, agent, telemetry, update service, or synchronization feature may have its own network behavior. The reviewed provider materials do not certify any particular local setup as isolated.
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Before using a local model with sensitive code, identify the runtime and every connected component, then check their network behavior and settings. Confirm whether the assistant retrieves broader repository context, calls external tools, or syncs conversation history. If you cannot establish where a feature sends data, treat that route as unverified and do not use it for confidential code until your organization approves it.
Cloud assistants: compare the exact product and data terms
Cloud services differ by plan, model, hosting provider, feature, and account type. The statements below are scoped to the named official documentation; they are not general guarantees for every product from those companies. Check the current terms and settings for the model and account you actually use, since model-hosting and retention details can change.
Rank #2
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| Service and scope | Training use described in the cited policy | Retention or routing detail |
|---|---|---|
| OpenAI API | OpenAI says API data is not used to train or improve models unless the customer opts in; the documentation dates this policy to March 1, 2023. | Default abuse-monitoring logs may include prompts, responses, and derived metadata and may be retained for up to 30 days under OpenAI’s current API data-controls documentation, accessed in 2026. This is not a universal maximum for every product or application state. Eligible customers may request Modified Abuse Monitoring or Zero Data Retention; eligibility, approval, feature limitations, and legal or safety exceptions apply. |
| OpenAI business products and API platform | OpenAI says inputs and outputs for ChatGPT Enterprise, Business, Edu, Healthcare, Teachers, and the API platform are not used for training by default. | OpenAI says business data is encrypted at rest and in transit and describes retention controls for qualifying organizations. Exact retention and control availability depend on the applicable product and organization; not stated as one period for every listed service. |
| GitHub Copilot | GitHub says Copilot Business and Enterprise data is not used by GitHub to train AI models. For individual subscribers, interaction data—including prompts, suggestions, and generated snippets—may be used for training under GitHub’s privacy statement and settings; an opt-out setting is described. | Hosting and retention vary by model and account type; GitHub documents model-specific conditions and exceptions, including retention for certain models and a time-bounded Zero Data Retention exception. Check the current entry for the selected model. |
| GitHub Copilot with BYOK | BYOK selects a provider for prompts and responses, so that provider’s applicable policies matter. | GitHub says prompts and responses are transmitted to the selected provider and may be subject to that provider’s retention and privacy policies. In Agent mode, some code-application or tool actions may still use Copilot-integrated models, so BYOK does not necessarily route every action through one provider. |
| Anthropic consumer products, including Claude Code on consumer accounts | Anthropic’s consumer privacy article, dated March 16, 2026, says chats and coding sessions may be used to improve models when the user opts in, when a conversation is flagged for safety review, or through another explicit opt-in. It also says Incognito chats are not used to improve Claude, even if Model Improvement is enabled. | When a user gives feedback, Anthropic says the related conversation may be stored for up to five years under that consumer policy. This is not a general retention period for all Claude use. Commercial and API users are outside the scope of this consumer description and should consult their separate terms. |
The two stated periods in this table measure different things: OpenAI’s up-to-30-day figure is for default API abuse-monitoring logs, while Anthropic’s up-to-five-year figure concerns a related consumer conversation stored with feedback. Neither figure describes all data for all products.
What to check before sending code to a cloud assistant
- Identify the exact account and model. Record whether you are using an individual or organization account, the product tier, selected model, model host, enabled features, and relevant hosting geography. A provider-level privacy statement may not cover every combination.
- Read the terms for each data category. Check training and improvement use separately from prompt and response retention, abuse monitoring, feedback, application state, human review, and deletion controls. Note opt-ins, opt-outs, eligibility conditions, and exceptions.
- Map what the client sends. Determine whether the request contains only selected text or also surrounding files, repository context, prior chat history, diagnostics, or agent outputs. For BYOK or agent workflows, identify which provider handles each action.
- Confirm organization approval and controls. Use an approved plan and obtain the administrative and contractual protections required by your organization. A public no-training statement is only one part of that decision.
- Minimize the request. Remove credentials and secrets, share the smallest relevant snippet, avoid broad repository context unless needed, restrict tools and repository permissions, and disable unnecessary history or synchronization.
- Recheck when the workflow changes. Revisit settings and terms after a model, feature, extension, or product change; then review generated code before using it.
A practical privacy checklist for teams
- Classify source code and define which tools are approved for each sensitivity level.
- Document the route for local inference, cloud calls, extensions, agent actions, BYOK, telemetry, and session synchronization.
- Record the selected plan, model, host, geography, training status, retention terms, exceptions, and available controls.
- Set least-privilege repository and tool access; avoid secrets and unrelated source in prompts.
- Give developers a clear escalation path for workflows whose data handling cannot be verified.
- Reassess the configuration and current terms after provider or product changes.
These steps reduce unnecessary exposure, but they are operational safeguards rather than a claim that any particular local or cloud tool is risk-free.
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