AI Coding Assistant: Protect Private Code During Setup

Setting up an AI coding assistant safely means deciding what code it can access, checking where prompts and files are processed, and limiting what the tool can retain or share. Before using one on a private repository, review its privacy controls and test the configuration with non-sensitive code.

What You’ll Need

• An approved AI coding assistant and access to its account or administration settings.
• The repository or workspace you plan to use, plus permission to change its settings.
• Your organization’s rules for source code, customer data, secrets, and external services. If you work on a team, confirm the rules with the person responsible for security or IT.
• A small, non-sensitive test project or sample file.

Step-by-Step

1. Classify the code before connecting anything. Determine whether the repository contains proprietary code, personal data, credentials, unreleased product details, or material subject to contractual or regulatory restrictions. Treat generated prompts, code snippets, filenames, chat history, and diagnostic logs as potential disclosures too. If the policy does not clearly allow an external assistant to process the material, do not connect the repository until you receive approval.
2. Check where processing happens. Read the provider’s current privacy and security documentation and your organization’s agreement. Find out whether prompts or code are sent to a remote service, whether they may be used to improve models, how long data is retained, who can access it, and whether enterprise controls differ from individual accounts. Do not assume that a paid plan, local-looking editor, or “private” label means code never leaves your device. If you cannot verify the data flow, use only approved non-sensitive examples.
3. Choose the approved account and service settings. Sign in with the account your organization authorizes; avoid personal accounts for work repositories unless policy explicitly permits them. In the assistant’s privacy, data, or administration settings, disable model-training or product-improvement use of your inputs where that option exists. Set retention to the shortest suitable option, and turn off optional collection of prompts, code, or telemetry if permitted. Save the settings and record what you changed. Controls and names vary by service, so verify them in its current documentation.
4. Install the tool from an approved source. Use your organization’s software catalog or the official distribution channel, and check that the publisher and requested permissions are expected. Review permissions for repository access, terminal commands, file changes, and network access. Grant only what the assistant needs. Avoid installing unrelated extensions or enabling broad workspace access just to make setup easier.
5. Limit the workspace and files it can see. Open only the repository needed for the task. Use the assistant’s documented ignore or exclusion settings to prevent access to secrets, configuration files, customer data, generated artifacts, and unrelated folders. Common examples to review include environment files, private keys, credential stores, and local configuration; filenames differ, so inspect your project rather than relying on a universal list. Check that exclusions cover both chat context and indexing or code-search features.
6. Keep secrets out of prompts and source control. Never paste passwords, access tokens, private keys, recovery codes, or live customer records into a prompt. Store credentials in an approved secret manager, and use placeholders in examples. Ensure secret files are excluded from the assistant and protected by the repository’s ignore rules. Those rules do not erase a secret already committed or shared; if a credential may have been exposed, follow your organization’s incident process and rotate or revoke it.
7. Restrict actions and review changes. Start with suggestions or read-only behavior where available. Require confirmation before the assistant runs shell commands, accesses the network, installs dependencies, or edits files. Review every generated diff and command before accepting it. Run the project’s normal tests and security checks, and do not treat generated code as trusted merely because it compiles.
8. Test the setup with harmless material. Ask the assistant to explain a small sample function, then inspect the files it can access, the proposed edits, and any activity or data-use indicators the product provides. Confirm that excluded files are not included in context and that it requests approval for sensitive actions. If the test reveals unexpected access or transmission, disconnect the workspace and ask your administrator or provider for clarification before using private code.
9. Recheck settings over time. Revisit permissions and data controls after updates, account changes, or changes to provider terms. Remove access when a project ends or a teammate no longer needs it. For team use, document the approved configuration and give developers a simple process for reporting suspected exposure.

Common Mistakes

• Assuming local editing means local processing: an editor extension may send context to a remote service. Confirm the actual data flow.
• Relying on a single exclusion file: exclusions may apply to indexing but not chat attachments, terminal output, or manually pasted text. Check each feature separately.
• Using personal accounts for company code: this can bypass organizational settings and make access or retention harder to manage.
• Sharing “sanitized” logs without checking them: logs can contain tokens, internal paths, snippets, or customer information. Inspect and redact before sharing.
• Accepting commands or edits without review: generated changes can introduce vulnerabilities, expose data, or damage files. Use version control and inspect the diff.

Tips

• Start with the least access possible, then grant more only when there is a clear need and approval.
• Use a separate test repository with fabricated data to learn how context, indexing, and permissions work.
• Keep sensitive examples out of prompts; describe the issue with generic names and representative values instead.
• Use branch protections, code review, automated tests, and secret scanning as additional safeguards. They complement privacy settings but do not prevent disclosure to a service.
• Ask your administrator for the approved settings in writing, especially when controls differ between account types or products.

Troubleshooting

The assistant can see a file it should not: stop using it on the repository, remove the workspace connection, and check exclusions for indexing, chat context, and attachments. Restart or rebuild the index if the product requires it. Ask the administrator how to clear cached context or stored data; do not assume deleting a local file removes copies already sent.

A privacy control is missing or unclear: do not infer that the feature is disabled. Check the current documentation and account plan, then ask the provider or organization administrator. Use a permitted local or non-sensitive workflow until the data handling is confirmed.

The assistant requests broad permissions: deny the request and see whether a narrower permission or read-only mode is available. If the tool cannot operate without access that policy forbids, do not use it with that repository.

You may have pasted a secret: stop sharing it, notify the appropriate security contact, and follow incident procedures. Revoke or rotate the credential if advised, review relevant access logs where available, and ask the provider about deletion or retention options. Treat a request to delete data as separate from credential rotation; both may be needed.

Frequently Asked Questions

Can I use an AI coding assistant with private code?
Only if your organization’s policy and the service’s data handling terms allow it. Confirm processing, retention, and training settings first.

Does disabling model training keep my code private?
Not necessarily. It may address training use, but processing, temporary retention, support access, and telemetry are separate questions.

Is a local coding assistant automatically safe?
No. Verify that inference is truly local and check extensions, update mechanisms, logs, network connections, and where prompts or indexes are stored.

Should I add secret files to an ignore list?
Yes, where supported, but also keep them out of prompts and source control. Confirm what the ignore setting covers.

Can I paste a code snippet instead of connecting the whole repository?
That can reduce exposure, but snippets may still reveal proprietary logic or include secrets. Share only what policy permits and remove sensitive details.

What should I do if private code was sent by mistake?
Notify your organization’s security contact promptly, preserve relevant details, and follow its incident process. Ask the provider about available deletion controls, but do not delay credential rotation if a secret was exposed.

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