GitHub Copilot can be used with private code, but “private” does not mean that prompts stay only on your device or that every feature handles data the same way. Copilot may receive context beyond what you type, training rules depend on your plan and settings, and some model choices send prompts and responses to another provider. Treat the tool as part of your software supply chain: check the applicable policies, limit sensitive input, and review generated changes before merging.
What data can GitHub Copilot receive?
A Copilot prompt may include more than the question entered in chat. GitHub says Copilot Chat combines the prompt with contextual information, which can include open files, repository data, and chat history. In an IDE, context may include the repository name and files open in the editor; some experiences can also use repository data stored on GitHub. What is included depends on the feature and where you use it. GitHub describes this context handling in its prompt-engineering documentation.
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That does not establish that Copilot sends every file in a repository whenever you ask a question. It does mean you should not assume that only the typed text is considered. Avoid placing credentials, production secrets, customer data, or regulated information in prompts or in repositories available to Copilot unless your organization’s policy and the relevant service terms permit that handling.
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GitHub’s stated policy differs by plan. Its individual-subscriber documentation says that, starting April 24, 2026, interactions from Copilot Free, Pro, Pro+, and Max may be used to train and improve models unless the user opts out. The interactions covered can include inputs, outputs, code snippets, and associated context. This is GitHub’s policy statement, not an independent audit finding.
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| Plan | GitHub’s stated training policy | Who manages the relevant control |
|---|---|---|
| Copilot Free, Pro, Pro+, and Max | Starting April 24, 2026, interactions may be used for model training and improvement unless the user opts out. | The individual subscriber manages the personal setting in Copilot settings. |
| Copilot Business and Enterprise | GitHub says it does not use customer data to train models without customer authorization; these plans are covered by GitHub’s Data Protection Agreement. | Organization or enterprise administrators manage policies for managed seats. |
Do not assume that an individual setting and an organization-managed policy are interchangeable. If you use Copilot through work, confirm which account and plan are active and which organization policies apply before relying on personal settings.
Is Copilot safe to use with private code?
“Safe” depends on the code’s sensitivity, the enabled features, the account plan, the selected model, and your organization’s rules. A private repository can still supply context to Copilot features; private repository status alone does not answer whether a particular prompt, file, or model is appropriate for your data.
Check the actual use before sharing sensitive material
- Identify the Copilot plan and whether the seat is individually managed or organization-managed.
- Check the selected model and client surface, such as an IDE, GitHub.com, or an agent experience.
- Review the applicable training, content-exclusion, and model-provider policies.
- Keep secrets and restricted data out of prompts and accessible repositories unless the relevant policy and terms explicitly allow their use.
These checks reduce avoidable exposure; they do not establish a universal rule that all Copilot use is suitable or unsuitable for private code.
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How can administrators exclude sensitive files?
GitHub documents content exclusions for Copilot Business and Enterprise. Where supported, an administrator can configure files so they do not inform inline suggestions in other files or Copilot responses, and excluded files are not reviewed by Copilot code review. Exclusion coverage has important limits, so test the policy in the specific client and mode your developers use.
- An IDE may still provide semantic information from an excluded file indirectly.
- Symlinks and repositories on remote filesystems are not covered by the documented exclusion behavior.
- Edit and Agent modes in VS Code and other editors are currently unsupported for exclusions.
- Some website and mobile support is marked as preview in GitHub’s documentation.
Because support varies by surface and can change, administrators should verify the current GitHub guidance and test representative files, repository layouts, and workflows rather than treating exclusion as a guarantee that no information about a file can influence Copilot.
Can Copilot generate insecure or copied code?
Yes. Generated suggestions can be inaccurate or introduce vulnerabilities, and a plausible-looking answer is not evidence that it is correct, secure, or suitable for your project. GitHub advises users to review and test Copilot Chat code for errors and security concerns. Apply the same engineering controls you would use for code from an untrusted contributor.
Review generated changes before merging
- Inspect the full diff, including error handling, authorization checks, input validation, and changes to dependencies.
- Run the project’s tests and relevant security analysis; investigate unexpected behavior rather than accepting a passing build as proof of security.
- Require human review before merging or deploying security-sensitive changes.
GitHub also provides a setting to allow or block suggestions that match public code. When blocking is selected, GitHub says most Copilot products check suggestions against surrounding code of about 150 characters. If matching suggestions are allowed, users may inspect available repository and license details; GitHub documents references for certain accepted inline suggestions and chat responses. These references can help investigate a match, but they do not certify security, licensing suitability, or fitness for your project. Decide whether matches are permitted under your personal or organization policy, and review references and licenses before accepting or distributing a match.
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Model choice can change where data is handled. GitHub documents multiple model providers and warns that, with bring your own key (BYOK), prompts and responses are sent to the selected provider and may be subject to that provider’s retention and privacy policies. Assess that provider’s terms and protect the API key as you would other credentials. In Agent mode, some actions, including code application or tool calls, may still use Copilot-integrated models rather than the BYOK provider. Check current documentation for the selected model because hosting and handling can vary.
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How long does Copilot keep chats and memory?
Retention is feature-specific; the documented figures below are not a universal schedule for every Copilot interaction, model, or surface.
- For the documented experience of asking Copilot questions on GitHub, GitHub says it stores up to 100 recent conversations and retains messages for 28 days before permanent deletion.
- Copilot Memory is separate from chat history. 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 on individual plans, while administrators must enable it for organization-managed use.
These statements describe particular features. They do not establish how long every category of interaction, telemetry, or data handled by a model provider is retained.
A practical policy for teams
For a team adopting Copilot, a useful baseline is to make data handling explicit before enabling it for sensitive repositories.
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Quick Recap
- Document approved plans, models, client surfaces, and uses for each data classification.
- Set the organization’s training and public-code matching policies deliberately, rather than relying on individual preferences.
- Use content exclusions for files that should not inform Copilot where the feature is supported, and test known limitations on the clients and modes in use.
- Define review, testing, and escalation requirements for AI-generated changes, especially for authentication, authorization, cryptography, and other security-sensitive code.
- Review model-provider terms and Memory controls when those features are enabled, and revisit the policy when GitHub changes feature support or handling terms.
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