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To protect private code, configure four separate boundaries: what an assistant sends to a model, whether conversations may be used for training, how long data may be retained, and what repositories or tools an agent can access. Turning off training does not keep code on your computer or guarantee that it is never stored. The right settings depend on your product, plan, model route, and whether you use an editor, cloud agent, or API.
Start by separating the four data boundaries
An AI coding assistant may use information from prompts, selected code, open files, repository context, or an agent’s work environment. Before changing settings, establish which information might leave your machine, who receives it, what happens to it afterward, and what actions the assistant can take. These are related questions, but no single privacy label answers all of them.
- Transmission and context: What is sent when you use completion, chat, a command-line interface, or an agent? Which provider receives the request, and what code or surrounding context is included?
- Training: Can prompts, outputs, code snippets, or task data be used to improve models? Is that the default for your plan, and is there an opt-out?
- Retention: Are requests or copies of your environment retained, for how long, and under what exceptions? A training opt-out is not a deletion setting.
- Access and actions: Which repositories, files, integrations, and tools can the assistant read or change? Can it run commands, make network requests, or open a cloud workspace?
These distinctions matter because a tool can send code to a model provider while excluding it from training, or keep training disabled while retaining some data for other purposes. Configure and verify each boundary separately.
Think of the review as four questions, not one “private mode” switch. A setting may address training but not transmission; a service may state that content is not used to improve models by default but still have separate abuse-monitoring retention; and an agent permission setting may restrict actions without changing provider data terms. Record the answer to each question for every surface you use.
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Make an inventory before changing settings
Write down the product and plan, the account identity, and the execution surface you actually use. An individually managed editor subscription, a company workspace, a command-line agent, a cloud agent, and an API key may be governed by different controls. Note the model and provider route as well: a product may offer models with different retention arrangements.
- Is the account personal or managed by an organization?
- Which plan applies: individual, business, enterprise, education, or API?
- Are you using editor completion or chat, a local command-line tool, or a cloud-hosted agent?
- Do you use a provider API key or an extension or integration in addition to the product’s built-in account?
- Which repositories and tools are available to the assistant?
For organization-managed accounts, ask an administrator which policies are enforced and whether users can change them. Personal preferences do not necessarily override organization policy, and changing account, plan, model, or execution mode can change which rules apply.
Be specific about “where” a request runs. An editor completion request, a chat conversation, a CLI task, a cloud-agent run, and a direct API call can have different context and policy paths even when they appear in one developer workflow. A useful inventory notes the product, account, plan, model, key owner, repositories in scope, and whether work is processed locally or by a cloud service.
Configure GitHub Copilot
Choose the training setting for an individual account
GitHub’s March 25, 2026 policy announcement says that, beginning April 24, 2026, interaction data from Copilot Free, Pro, Pro+, and Max can be used to train and improve models unless the user opts out. The described change does not apply to Business and Enterprise customers. GitHub’s individual-subscriber documentation places the preference in Copilot settings: open the training-data setting and choose Disabled. Check the current account setting rather than assuming the preference is already off.
GitHub says the setting is not displayed for Business and Enterprise plans; it also says customer data on those plans is protected by its Data Protection Agreement and is not used for training under the described policy. For a managed account, have an administrator confirm the applicable organization policy and plan rather than relying on an individual settings page.
The announcement names interaction data broadly, including inputs, outputs, code snippets, and associated context. That is why checking the setting matters even if you mainly use code completion rather than chat. If your account is Free, Pro, Pro+, or Max, treat the opt-out as a deliberate account check, not as a consequence of having an older subscription or assuming the setting is disabled.
Keep public-code matching separate from training
Copilot’s public-code matching control concerns suggestions that match public GitHub code. It is an output-matching choice, not a training opt-out and not a control over whether your prompt or code context is transmitted. Set it according to your organization’s licensing and review requirements, but do not treat it as a privacy setting.
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GitHub organization policies control Copilot features, including whether coding agents and third-party agents are enabled. GitHub says enabled coding agents can access repositories enabled for Copilot cloud agent. Administrators should enable only the repositories that need the feature, and separately review which third-party agents are allowed. Users should confirm that the repository they are working in is approved for the agent they plan to use.
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Repository enablement is a practical access boundary: do not turn on an agent for every repository merely because the feature is available. Administrators should review that scope alongside organization-level feature policies, then check third-party-agent availability separately. A user’s personal training preference does not answer whether an organization has enabled an agent or which repository it can reach.
Configure Cursor
Enable Privacy Mode, then check the route
In Cursor, open Settings > General and turn on Privacy Mode. Cursor says this mode prevents code from being used for training. It is enabled by default for Teams and Enterprise, and team administrators can enforce it organization-wide. If you use an individual account, verify the setting yourself; if you use a team account, ask whether the admin enforces it.
Privacy Mode does not mean that no code leaves your machine. Cursor says that when AI features are used, prompts and code context are sent to model providers, including OpenAI, Anthropic, and Google. Treat provider transmission and training use as separate questions when deciding whether a repository can be used with Cursor.
For a team administrator, enforcement is more reliable than asking each developer to remember a preference, but it still answers only the training-use question described by Cursor. Developers should continue to verify the provider route and agent workflow, especially when using features that send repository context rather than a short prompt.
Review model exceptions and API keys
Cursor documents exceptions to its zero-data-retention arrangements: some models require retention by the provider and sit outside those arrangements. Cursor says team, enterprise, or privacy-mode users need admin approval before using these models. Check the exact model and route before selecting it; a product-wide privacy label does not establish identical handling for every model.
If you supply your own API key, Cursor says the provider’s privacy policy governs that use. Review the provider’s terms and account controls for the key you are using, and do not assume Cursor’s own model-retention arrangements apply to the provider request.
For a model change, repeat the review rather than carrying the prior decision forward. Confirm whether the model is covered by Cursor’s stated retention arrangements, whether an exception requires administrator approval, and whether the request is billed and governed through your own key. The same editor can therefore involve different provider terms depending on the selected route.
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Cursor distinguishes ordinary language-model requests from cloud-agent runs. A cloud agent needs access to the repository over the duration of its task. Cursor describes isolated virtual machines and encrypted repository copies stored temporarily while a run is active, then deleted after completion. Use cloud agents only on repositories approved for that workflow, and take their temporary repository access into account when assessing whether the code is suitable for remote processing.
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This is a separate decision from enabling Privacy Mode for ordinary AI requests. If a project cannot be copied to a cloud environment, do not infer that the training setting makes a cloud-agent run acceptable. Decide whether the repository may be processed remotely, and restrict use to approved projects.
Configure ChatGPT, Codex, and the OpenAI API
For individual ChatGPT and Codex use
For individual ChatGPT Free, Plus, and Pro use, OpenAI’s data-use instructions say to open Settings > Data controls and turn off Improve the model for everyone. The Privacy Portal is an alternative. OpenAI says either choice excludes new conversations and Codex tasks from training.
Codex has a separate Include environments control for training on full environments. Changing the ChatGPT or Privacy Portal setting does not change that Codex control. If you use Codex, inspect both preferences rather than inferring the environment setting from the general ChatGPT choice.
When you use both ChatGPT and Codex, make the settings review part of setup for each surface. The general model-improvement choice and the environment-inclusion choice are not interchangeable. Verify the Codex control directly, particularly if your task involves a full environment rather than only a conversation.
For business, education, enterprise, and API use
OpenAI says ChatGPT Business, Enterprise, Edu, and API content is not used to improve models by default. For API requests, that default addresses training, not every form of retention. OpenAI describes separate retention controls for abuse-monitoring logs; eligible organizations may apply for Modified Abuse Monitoring or Zero Data Retention. If approved, an administrator configures the applicable controls in organization or project data settings.
Eligibility and feature limitations apply, so do not promise that every API organization or request can use zero retention. Confirm approval, the project-level configuration, and the endpoint or feature limitations relevant to your workload. A training exclusion by itself is not evidence that no data is stored.
For an organization using the API, the administrator should verify the approved retention mode in the right organization or project settings rather than relying on a developer’s account-level preference. Then confirm that the specific feature or endpoint is covered by the controls. Modified Abuse Monitoring and Zero Data Retention are not automatic consequences of using a business account, and OpenAI documents eligibility and limitations.
Configure Claude Code without guessing at data policy
Claude Code’s official permissions documentation describes controls for what tools the agent may use, including allow and deny rules and permission modes. Use those controls to restrict capabilities and require approval where appropriate, especially for actions that could expose or change data.
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The available official data-usage material does not establish enough detail here to state a universal training or retention default for Claude Code. Do not infer one from its permission mode: permissions govern agent capabilities, not provider data-use terms. Check the current data-usage documentation and the terms for the account or deployment you use before putting private source code into the service.
In practice, use permission rules to decide what Claude Code may do, and verify data terms separately for the account or deployment. A deny rule or approval mode can limit a tool action, but it does not establish how prompts are retained or used. Avoid making a data-policy claim based solely on the agent’s permission configuration.
Reduce the amount of sensitive context available
Even when a provider’s training setting is disabled, avoid giving an assistant information it does not need. Do not paste credentials, access tokens, private keys, customer records, or unrelated sensitive files into a prompt. Keep agent repository access limited to the project and task at hand, and review integrations separately instead of assuming they inherit the editor’s settings.
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Do not assume that a particular file-exclusion control is identical across products. Verify the current controls for the exact editor, CLI, extension, agent, and model route you use. If you need to confirm a setting or workflow, test with non-sensitive sample code and inspect what context the tool says it collects before using it on a protected repository.
A good test is intentionally low risk: use a small, non-sensitive sample project to learn which actions trigger model requests and how the interface identifies the selected model or agent. Do not test exclusions by placing a real secret in a repository and hoping the assistant will ignore it. The point is to understand the workflow without exposing information that cannot be rotated or replaced.
Use this configuration sequence for a new tool or account
- Identify the account and plan. Record whether you are signed in personally or through a managed workspace, and identify the plan that governs the session.
- Identify the execution surface. Separate editor completion or chat from CLI use, cloud-agent runs, and direct API requests.
- Set the training preference. Disable training where the relevant plan exposes an opt-out; for Codex, also check the separate environment-inclusion control.
- Review retention independently. Check provider and product retention terms. For the OpenAI API, investigate eligibility and configuration for Modified Abuse Monitoring or Zero Data Retention if those controls are required.
- Check model routing. Confirm the selected model, whether the product uses its own provider arrangement or your API key, and whether a retention exception applies.
- Constrain repositories and capabilities. Enable only necessary repositories for cloud agents, review third-party agents and integrations, and use permission rules or approval modes to limit consequential actions.
- Keep unnecessary secrets out of context. Avoid sensitive prompt content and verify any file-exclusion behavior for the specific tool rather than assuming it is universal.
- Recheck after changes. Repeat the review when you change plan, account, model, API key, extension, or cloud feature, because those changes may alter the governing policy or data path.
For a team, turn the sequence into a short onboarding check: the developer identifies the product and surface; the administrator confirms plan-level and organization-level controls; and both agree which repositories are approved for agents. Keep the record concise, but note the date checked and the model route so it can be revisited when products or policies change.
Quick comparison: which control answers which question?
| Tool or use case | Training control or default established here | Transmission, retention, or access point to verify |
|---|---|---|
| Copilot Free, Pro, Pro+, Max | From April 24, 2026, interactions may be used for training unless the user opts out; personal setting is in Copilot settings, with the training choice set to Disabled. | Organization policy and agent repository scope are separate; check which repositories are enabled for Copilot cloud agent. |
| Copilot Business and Enterprise | Excluded from the April 2026 policy change; GitHub says customer data is protected by its Data Protection Agreement and is not used for training under the described policy. The individual training setting is not displayed. | Have the organization administrator confirm feature, privacy, and agent policies. |
| Cursor | Privacy Mode prevents code from being used for training; it is enabled by default for Teams and Enterprise and can be enforced by admins. | AI prompts and code context are sent to providers. Some models fall outside Cursor’s zero-data-retention arrangements; BYOK follows the provider’s policy. Cloud agents use temporary repository copies during runs. |
| Individual ChatGPT and Codex | Turn off Improve the model for everyone, or use the Privacy Portal, for new conversations and Codex tasks. Codex’s Include environments control is separate. | Do not infer zero retention from training exclusion; review the applicable service and account terms. |
| ChatGPT Business, Enterprise, Edu, and OpenAI API | Content is not used to improve models by default. | API abuse-monitoring retention has separate controls. Modified Abuse Monitoring and Zero Data Retention require eligibility and approval, and may have feature limitations. |
| Claude Code | A universal training default is not established here; verify current data-use terms for the account or deployment. | Permission modes and allow/deny rules constrain agent capabilities, not data-use policy. |
Use the table as a first pass, not as a substitute for checking the active account. It compares the distinctions established for these products, but each organization still needs to confirm its current settings, exact model route, available retention controls, and approved repository scope.
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Recheck privacy whenever the setup changes
Privacy configuration is not a one-time checkbox. Revisit it after changing a subscription, signing in with a personal rather than managed identity, switching models, adding an extension, entering an API key, or enabling a cloud feature. A setting that governs editor chat may not govern a separate agent or API request. Keep a short record of the active plan, training choice, model route, retention controls, and repository scope so an administrator or developer can verify the setup without relying on memory.
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Rechecking is especially important when a change introduces a different provider or moves work from an editor request into an agent run. A new model can bring a retention exception; a personal API key can invoke the provider’s own policy; and a cloud workflow can require repository access. Treat each change as a new data-path decision rather than assuming the old setting covers it.
Frequently Asked Questions
How do I stop AI coding tools from using my code to train models?
Check the training control for the exact product, plan, and account. For Copilot Free, Pro, Pro+, and Max, GitHub says interaction data may be used for training from April 24, 2026 unless the user opts out; the personal setting is in Copilot settings, where the training option can be set to Disabled. Cursor users can enable Privacy Mode. Individual ChatGPT and Codex users can turn off Improve the model for everyone, and Codex has a separate Include environments control. These settings address training, not necessarily transmission or retention.
Does turning off training keep my source code local?
No. Training, transmission, and retention are separate questions. Cursor says it sends prompts and code context to model providers when AI features are used, even though Privacy Mode prevents code from being used for training. Check what context is sent and which provider receives it, as well as the applicable retention terms.
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How do I keep private source code out of an AI coding assistant?
Use only a product and workflow approved for the repository, limit an agent to the narrowest practical repository scope, and avoid putting secrets or unrelated sensitive information into prompts. Review the model route, provider terms, organization policies, and any cloud-agent workflow. A training opt-out alone does not establish that no code is transmitted or retained.
Is Cursor Privacy Mode the same as zero data retention?
No. Cursor describes Privacy Mode as preventing code from being used for training. It also says AI prompts and code context are sent to providers and documents model exceptions outside its zero-data-retention arrangements. Check the specific model and route; BYOK use follows the provider’s policy.
Does the ChatGPT training setting also cover Codex environments?
Not completely. OpenAI says turning off Improve the model for everyone or using the Privacy Portal excludes new conversations and Codex tasks from training, but Codex has a separate Include environments setting for training on full environments. Changing the general setting does not change that separate control.
Does OpenAI API data stay stored nowhere if it is not used for training?
No. OpenAI says API data is not used for training by default, while abuse-monitoring retention is governed separately. Eligible organizations may apply for Modified Abuse Monitoring or Zero Data Retention, subject to approval and feature limitations, then configure approved controls in organization or project settings.
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Confirm which organization policy enables the agent, which repositories it can access, and whether third-party agents or integrations are allowed. GitHub says enabled coding agents can access repositories enabled for Copilot cloud agent. Cursor cloud agents use temporary encrypted repository copies during a run. Limit access to repositories approved for that workflow.
Do Claude Code permission settings tell me how prompts are used or retained?
No. Claude Code’s documented allow/deny rules and permission modes control tool capabilities and approvals. They do not, by themselves, establish training or retention terms. Check the current data-use terms for the account or deployment separately.
The Bottom Line
Protecting code in an AI coding tool means checking more than a training toggle. Verify what context is transmitted, how it is retained, which model or provider handles it, and what repositories and tools an agent can access. Recheck those boundaries whenever you change plans, accounts, models, keys, or cloud features.
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