You can use an AI coding assistant on a non-public project more safely by approving the service for that code, limiting what it can access, excluding sensitive files in the assistant’s own settings, and reviewing its actions and output. No single setting guarantees privacy: what the assistant receives depends on the product, plan, configuration, and how you use it.
Can an AI coding assistant see your whole codebase?
Not necessarily, but the prompt you type may not be the only information the tool uses. Depending on the product and its configuration, context can include open files, project structure, terminal output, or other material the assistant can access. That context can expose credentials, personal data, proprietary logic, or internal architecture. OWASP describes these risks in its Secure Coding with AI Cheat Sheet; GitHub also describes context behavior and responsible use for Copilot Chat in GitHub.
Whether a particular assistant can read a particular file depends on its integration, permissions, context rules, and settings. Check the documentation and configuration for the exact tool and deployment you use rather than assuming it sees either only your prompt or every file on your computer.
Does Copilot train on your code?
There is no single answer that applies to every Copilot product, plan, and configuration. Before using it with proprietary code, check current GitHub documentation, organization settings, and the applicable terms for your account. Confirm what data may be collected, retained, or used for model improvement, and whether administrators can control those settings. GitHub’s security and governance documentation is the relevant starting point; your organization’s agreement and configuration also matter.
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Apply the same check to any other provider. Product behavior and terms can change, so record which service, plan, and settings your organization approved, along with the date they were checked.
Before a session: approve the tool and limit access
- Classify the repository. Determine whether its code and data may be used with the selected assistant and deployment under organizational policy, contractual obligations, and applicable regulatory or regional requirements. If it is not approved, do not send it to that service.
- Check the actual configuration and terms. Review current provider documentation and organization settings for context collection, data handling, retention, model improvement, exclusions, session logs, and sandboxing. These controls vary by product and plan.
- Set exclusions in the assistant. Use the assistant’s supported exclusion or ignore mechanism for files and directories such as
.env,.env.*, private keys, and credential files. Then verify the exclusion with a harmless canary or other controlled check—never use a real credential as a test. OWASP cautions that.gitignorecontrols Git behavior; by itself, it does not stop an AI tool from reading local files. - Keep credentials out of the project tree. Prefer environment variables, a vault, or an encrypted secret store. Use scoped, short-lived credentials where supported. Do not paste tokens into prompts or into terminals the assistant can observe.
- Constrain agent permissions. For an agentic tool, consider a separate workspace or sandbox. Remove access to unrelated repositories and credentials, and restrict filesystem access, network access, and command execution to what the task needs.
During a session: provide only the context and authority needed
- Share only the relevant code and context. Do not paste secrets, customer records, or unrelated proprietary material into a prompt.
- Treat repository files, issues, pull requests, README files, changelogs, fetched webpages, terminal output, and tool responses as untrusted input. They may contain instructions intended to steer an agent. Such instructions do not authorize disclosure, broader access, or unrelated actions.
- Require confirmation before consequential operations such as publishing, pushing, deploying, changing access controls, installing an unreviewed dependency, or using credentials. Avoid broad automatic approval.
- Remember that excluding a file from Git does not, on its own, exclude it from an assistant’s local context.
These practices address different risks: a file exclusion can reduce accidental context sharing, while least privilege and approval gates limit what an agent can do. Neither is a substitute for the other.
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- 【Superior Privacy】- Our advanced multi-layered film filter blacks out your screen when viewing from the side, while maintaining a crystal clear screen straight-on. It also protects your eyes from harmful UV and blue light. [Note: It does not block visibility directly behind you, regardless of the distance.]
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After a session: inspect the changes and handle possible exposure
- Review the diff and commands run. Look closely at changes to tests, configuration, dependencies, CI, permissions, and security-sensitive code.
- Run the project’s usual checks. Use the normal tests and security controls, and review both findings and generated tests. Passing checks are not proof that every change is safe.
- Check available logs or session history. If the tool provides them, look for accidental disclosure. If a credential may have been exposed, revoke or rotate it promptly and follow your organization’s incident process.
- Keep an approval record. Note the approved tool, plan, configuration, and review date so the team can revisit the decision if provider behavior or terms change.
For Copilot cloud agent, GitHub describes safeguards including CodeQL, dependency advisory checks, secret scanning, and session logs in its risks and mitigations documentation. Those are vendor-described measures, not a certification that every change is safe; GitHub’s responsible-use guidance still calls for review and testing.
How to compare assistants and deployments
There is no universally best provider or plan established for every team. Compare the specific service, deployment, plan, and settings against the questions below, using current provider documents and your organization’s agreements.
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| Decision area | What to verify |
|---|---|
| Context | Which files, project metadata, terminal output, or other content can enter context, and how can that be limited? |
| Data handling | Under the exact plan and settings, may data be retained or used for model improvement? What do the applicable terms say? |
| Administration | Can administrators manage exclusions, account access, and approved use? |
| Execution and isolation | Does work run locally or in the cloud? What filesystem and network boundaries can you enforce? |
| Credentials | Which credentials can the assistant or agent access, and can access be scoped to the task? |
| Visibility and response | Are logs or session history available, and can your team investigate a suspected disclosure? |
| Code safeguards | Which code review, secret scanning, and dependency checks are available, and what remains for your team to review? |
| Organizational fit | Does the deployment meet your contract, regulatory, and regional requirements? |
For particularly sensitive work, an isolated or self-hosted option may be worth considering after a security review. Hosting something yourself does not, by itself, establish that the full setup is safe. The UK NCSC’s secure deployment guidance offers broader AI deployment principles; apply them in the context of your own system and threat model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you use an AI coding assistant with proprietary code?
Only when the specific assistant, plan, configuration, and deployment are permitted for that code under your organization’s policy and applicable terms. If approval is unclear, do not submit proprietary code until the appropriate owner has confirmed it. GitHub’s product guidance says, “Of course, you should always use GitHub Copilot together with good testing and code review practices and security tools, as well as your own judgment.” (GitHub Copilot.)
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- Superior Privacy and Anti Glare - Our advanced multi-layered film filter blacks out your screen when viewing from the side, while maintaining a crystal clear screen straight-on. It also protects your eyes from harmful glare, UV, and blue light. [Note: It does not block visibility directly behind you, regardless of the distance.]
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- Package Contents - Each package includes one privacy screen shield filter, two sets of clear adhesive strips, two sets of slide mount tabs, and a microfiber cleaning cloth. Buy with confidence – located in the US, Sight Pro specializes in providing best-in-class privacy solutions to individuals, small businesses, corporations, government, and educational institutions. Our privacy screens are Section 889 and TAA compliant.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
