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GitHub Copilot can be used with private code, but it is not a blanket guarantee that code and prompts stay within your machine or are never used to improve models. What happens depends on your plan, settings, chosen model, and the Copilot feature you use. Before using it on sensitive work, check what context may be sent, whether your plan permits training use, and whether your organization has configured exclusions.
Is GitHub Copilot safe to use with private code?
It can be appropriate for private-code work when the account, organization policy, selected model, and data handling meet your requirements. But “private” does not mean Copilot operates only on your computer: prompts may be combined with relevant context and sent to a model. Nor does a privacy setting make generated code trustworthy. Treat Copilot as a service that may process code context, and treat its suggestions as untrusted code that needs review.
GitHub’s documentation describes different protections and controls for individual and organization-managed plans. Those distinctions matter: a personal setting is not a substitute for an organization’s policy, and a content exclusion does not cover every feature or prevent all indirect inferences.
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What data can GitHub Copilot send?
The text you type is not necessarily the entire prompt. GitHub says Copilot Chat may combine a prompt with contextual information such as open files, repository data, and chat history before sending it to a model. In an IDE, context can include the repository name and files open in the editor; some experiences can also use repository data stored on GitHub. The context varies by feature and product surface, so do not assume that every Copilot interaction sees the same material—or that it sees every file in the repository.
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As a practical precaution, do not put credentials, production secrets, customer data, or regulated information into prompts or repositories available to Copilot unless your organization’s policy and the applicable service terms explicitly allow that handling. This is a risk-management measure, not a claim that every file is transmitted each time you use Copilot.
Does GitHub Copilot use your code to train AI?
GitHub’s stated policy changed on April 24, 2026. Its documentation says it may use interactions from Copilot Free, Pro, Pro+, and Max—including inputs, outputs, code snippets, and associated context—to train and improve models unless the individual user opts out in Copilot settings. This is GitHub’s published policy, not an independent audit finding. If you use an individual plan, review the current setting in your account rather than relying on an old assumption about training.
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GitHub says it does not use Copilot Business or Enterprise customer data to train models without customer authorization under its Data Protection Agreement (DPA). That statement is specifically about training; it should not be read as meaning no data is processed to provide the service, or that every feature and model has identical handling.
| Copilot plan | GitHub’s stated training position | Who manages the relevant control |
|---|---|---|
| Free, Pro, Pro+, and Max | Starting April 24, 2026, interactions may be used to train and improve models unless the user opts out; inputs, outputs, code snippets, and associated context are included in the stated policy. | The individual user manages the personal setting in Copilot settings. |
| Business and Enterprise | GitHub says customer data is not used to train models without customer authorization under the DPA. | Organization or enterprise administrators manage policies for managed seats; applicable customer authorization and terms still matter. |
These are GitHub’s documented positions for the named plans. Check the current documentation and your account or organization policy if your plan, seat management, or model has changed.
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How can an organization limit Copilot’s access to sensitive files?
GitHub Business and Enterprise administrators can configure content exclusions for supported Copilot uses. GitHub says excluded files will not inform inline suggestions in other files or Copilot responses, and excluded files will not be reviewed in Copilot code review. Exclusion is useful for reducing exposure, but it is not a universal “Copilot cannot know anything about this file” boundary.
- An IDE may still provide semantic information derived from an excluded file indirectly.
- GitHub documents that exclusions do not cover symlinks or repositories on remote filesystems.
- Edit and Agent modes in VS Code and other editors are currently unsupported by the documented exclusion behavior.
- Some website and mobile support is marked as preview, so coverage can change.
Before relying on an exclusion, check GitHub’s current content-exclusion documentation for the exact client, mode, repository type, and path. Then test the policy in the actual environment where developers work and document any unsupported cases. Do not assume a path exclusion applied in one surface will carry over to another.
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Does Copilot store prompts and chat history?
There is no single retention period established here for every Copilot feature, model, and provider. The numbers GitHub documents are feature-specific:
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- Chat in GitHub: GitHub’s 2026 documentation for asking Copilot questions in GitHub says it stores up to 100 recent conversations and retains messages for 28 days before permanent deletion.
- Copilot Memory: GitHub says unused Memory facts and preferences are automatically deleted after 28 days. The timer may reset when an entry is validated and used. Memory is enabled by default for individual plans; an administrator must enable it for organization-managed users.
Conversation history and Memory are separate features, not interchangeable retention rules. Telemetry, model-provider processing, and other Copilot surfaces may have their own handling. Do not infer a universal retention schedule from either of the documented 28-day periods.
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Can the model you choose change data handling?
Yes. GitHub documents model-specific hosting, and a model choice can affect which provider handles a request. With bring your own key (BYOK), prompts and responses are sent to the provider you selected and may be subject to that provider’s privacy and retention policies. Assess those terms before sending sensitive material, and protect the API key as a credential.
In Agent mode, some actions—such as applying code or making tool calls—may still use Copilot-integrated models rather than the BYOK provider. Check the current documentation for the selected model and feature instead of assuming that BYOK routes every part of an agent workflow through one provider.
Can GitHub Copilot generate insecure code or copy public code?
Review suggestions as untrusted code
Yes, a suggestion can be inaccurate or introduce a vulnerability. GitHub’s responsible-use guidance says: “You should always review and test the code generated by Copilot Chat to ensure that it meets your requirements and is free of errors or security concerns.” That is advice, not a guarantee that review or testing will catch every defect.
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- Review any new or changed dependencies, including their source and suitability for the project.
- Run the project’s tests and security analysis, and require human review before merging or deploying security-sensitive changes.
Understand public-code matching controls
GitHub offers a setting to allow or block suggestions that match public code. When blocking is selected, GitHub says most Copilot products check a suggestion against surrounding code of about 150 characters. If matching suggestions are allowed, users may inspect matching repository and license details. GitHub also documents references for certain accepted inline suggestions and chat responses.
Those controls can help investigate a match; they do not certify that code is secure, correctly licensed for your particular use, or suitable for your project. Decide whether matches are permitted under your personal or organization policy. If they are allowed, inspect available references and license information before accepting or distributing a match.
Quick Recap
A practical checklist before using Copilot on sensitive work
- Identify the deployment: Confirm whether you are using an individual plan or a Business or Enterprise managed seat, and check the applicable account or organization policy.
- Check training preferences: If you have an individual Free, Pro, Pro+, or Max plan, review the opt-out setting in Copilot settings under GitHub’s policy effective April 24, 2026. For managed plans, confirm the organization’s terms and authorization.
- Know the active context: Check the feature and client in use, which files are open, and whether repository data or chat history may be included. Keep secrets and restricted data out unless permitted.
- Configure exclusions where available: For Business or Enterprise, exclude sensitive paths where supported, then test the actual IDE, mode, and repository setup against the documented limitations.
- Verify the model route: Check the selected model and whether BYOK is enabled. If a third-party provider receives prompts and responses, assess its terms and secure the key.
- Set code-review expectations: Decide whether public-code matches are allowed, and require suitable tests, security checks, and human review for generated changes.
- Handle Memory deliberately: If Copilot Memory is enabled, account for stored repository facts and preferences separately from chat history, and review the feature’s controls.
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