The safest approach is to reduce what an AI pull-request (PR) tool can read before you rely on a provider’s privacy promises. Define which tools and repositories are approved, exclude secrets and sensitive paths in the assistant itself, keep credentials outside repository trees, grant the agent only the files and tools it needs, inspect real outbound requests, and verify retention and training terms for the exact plan, model, endpoint, and hosting route. Then review and test every generated change as if a human contributor had submitted it.
Why an AI PR tool can see more than the file you opened
AI coding assistants and PR agents commonly build a prompt from more than the changed lines. Depending on the client and task, the context may include open files, nearby modules, repository structure, diffs, issues, pull-request comments, terminal output, documentation, and data returned by connected tools. OWASP warns that assuming an assistant sends only the current file is unsafe: many assistants send broader project context. That context can contain credentials, personal data, proprietary logic, or details of an internal architecture. See the OWASP Secure Coding with AI Cheat Sheet.
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A PR agent may also run commands, search the repository, read issue discussions, or call an external tool. A narrowly scoped code-review request can therefore become a much larger data flow. Treat every agent, IDE extension, hosted PR integration, and model endpoint as a separate route that needs its own approval.
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Before enabling an AI PR feature, classify the repository and decide which deployment surfaces are allowed for that class. The decision should be made by security and engineering together, not by an individual developer selecting an IDE setting.
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Define repository sensitivity tiers
- Public or low-sensitivity code: ordinary open-source material with no credentials, personal data, regulated records, or confidential business logic.
- Internal code: proprietary implementation details, private infrastructure information, or internal documentation that may be sent only to approved services under company terms.
- Confidential or regulated code: customer data, security controls, cryptographic material, health or financial records, unreleased products, or information subject to contractual or legal restrictions.
- Highly sensitive or classified code: material for which a cloud-hosted assistant is not approved. OWASP recommends self-hosted or air-gapped AI coding tools for these projects and advises against using cloud assistants without explicit approval.
Record the permitted route for each tier
Your policy should name the approved product surface, model or endpoint, hosting route, client versions, and account type. “The company uses an approved AI vendor” is too broad: an API endpoint, an IDE extension, a cloud PR agent, and a third-party agent may have different context and retention behavior.
For each tier, document whether the assistant may read repository files, issue data, terminal output, external knowledge sources, and MCP-connected services. Require a security review before enabling a new agent or changing the model route.
Exclude sensitive files in the AI tool itself
Use the exclusion mechanism provided by the specific assistant, such as a tool-specific ignore file or an organization policy. OWASP lists these common candidates for exclusion:
.envand.env.**.pemand*.keycredentials.jsonserviceAccountKey.json- Private certificates, signing keys, database exports, customer-data fixtures, incident records, and internal architecture documents
Use the syntax and location documented by your tool. Examples include .cursorignore, .copilotignore, or an equivalent context-exclusion setting, but support and behavior differ by client and agent mode. Test that the exclusion applies to PR review, chat, autonomous agent runs, and any terminal or tool connector—not just inline completion.
A .gitignore file is not an AI security boundary. It controls Git operations; it does not necessarily stop an assistant from reading an ignored file that exists on the local filesystem. Maintain both lists where appropriate and verify the assistant’s actual file-discovery behavior.
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Test exclusions with harmless canaries
Create a non-secret test file containing a unique marker, place it in an excluded path, and ask the agent a task that would reveal whether it can read that marker. Repeat the test for each client and agent mode. Remove the canary after testing, and do not use a real credential as a test value.
Keep secrets outside the repository and the assistant’s working tree
Exclusion rules reduce accidental context but are not a substitute for sound secret management. Store credentials in environment variables, a vault, or an encrypted secret-management service rather than in files an assistant can enumerate. Keep production keys, signing certificates, cloud tokens, and customer exports out of the project tree entirely.
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- Do not open a secret file in an IDE while an assistant is active.
- Do not paste credentials into a terminal whose output the agent can read.
- Use short-lived, least-privilege credentials for development and automation.
- Prevent secrets from appearing in build logs, test fixtures, crash reports, and issue comments.
- Scan commits, pull requests, and generated patches for secrets before merging.
If a credential may have entered an AI context, revoke or rotate it according to your incident procedure. Deleting the chat or closing the pull request does not prove that every copy has been removed from provider logs, application state, backups, or human-review workflows.
Minimize the context and permissions granted to an agent
Give the tool the smallest useful slice of information. A reviewer that needs a two-file diff should not receive the entire monorepo, unrelated issues, or a production shell. Context minimization lowers disclosure risk and makes the generated review more relevant.
Limit repository and conversation context
- Use a dedicated review checkout containing only the repositories and branches required for the task.
- Attach the relevant diff and tests instead of exporting a complete repository archive.
- Separate confidential issue discussions from ordinary coding threads.
- Remove customer identifiers, access tokens, and internal URLs from issue descriptions before allowing an agent to read them.
- Disable automatic inclusion of neighboring files or historical conversations when the task does not require them.
Limit tools and external data sources
An autonomous agent’s risk depends on what it can do as well as what it can read. Restrict shell commands, network access, package installation, issue systems, artifact stores, and deployment tools. Use read-only credentials for review tasks whenever possible, and require an explicit approval step for writes, merges, releases, or changes to access-control files.
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Connected-tool policies must be evaluated separately. GitHub’s enterprise documentation describes controls for allowing or blocking MCP servers and managing approved registries on supported Copilot surfaces. Review the GitHub agent-management documentation for the controls available in your enterprise plan and client.
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Inspect what actually leaves your environment
Vendor documentation explains intended behavior, but a controlled inspection tells you what your configured client sends. Perform the inspection in a test organization or repository with synthetic data, and redact credentials and personal information before retaining any traffic.
- Inventory every entry point. List IDE extensions, web PR reviews, command-line agents, CI jobs, bots, MCP servers, and API integrations that can access source code.
- Create a synthetic repository. Add uniquely named fake secrets, personal-data placeholders, excluded files, terminal output, and irrelevant modules.
- Run representative tasks. Test inline completion, chat, PR review, autonomous fixing, test execution, and connected-tool calls separately.
- Capture the request path. Use approved proxy, endpoint, or vendor diagnostic controls to record metadata and sanitized payloads. Do not capture real secrets.
- Compare the payload with policy. Check file names, code, diffs, issue text, terminal output, tool results, model route, and destination. Confirm excluded markers are absent.
- Repeat after changes. Re-test after a client upgrade, policy change, model change, new MCP connection, or migration between API and hosted-product routes.
Request inspection should be a recurring control, not a one-time demonstration. A change in agent mode can alter context collection without changing the visible editor experience.
Separate the vendor questions that are often conflated
Privacy claims such as “not used for training” and “zero retention” answer different questions. Obtain the terms for the exact plan, model, endpoint, hosting route, and account configuration.
| Question | What it establishes | Why it is separate |
|---|---|---|
| Is customer data used to train or improve models? | Whether submitted content can contribute to model training under the stated terms. | A no-training promise does not describe operational logs or application state. |
| How long are abuse-monitoring logs retained? | How long provider security systems may keep prompts, outputs, or metadata for abuse detection. | Monitoring retention can exist even when training use is disabled. |
| Is application state stored? | Whether files, conversations, vector indexes, or task state persist to support a feature. | Stateful features may retain data independently of request logging. |
| Can humans review content? | Whether authorized personnel or contractors may access data for safety, support, or legal reasons. | “No human review” may apply only to a particular route or setting. |
| Can data be deleted on demand? | Which stored copies can be removed, on what schedule, and with what exceptions. | Deletion terms may exclude backups, legal holds, or safety records. |
OpenAI API terms
OpenAI’s API data-controls documentation says API data is not used to train or improve models unless the customer opts in. The same documentation distinguishes abuse-monitoring logs from application state and describes retention controls that require approval, with eligibility and limitations that vary by endpoint and feature. Treat this as API guidance, not as a blanket statement about every OpenAI product or a third-party PR integration. Verify the current terms at OpenAI’s data controls page.
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Business security commitments
OpenAI describes business-data protections, encryption, administrative controls such as audit logs, and data-residency options in its business security materials. These are vendor-published commitments; confirm the product-specific contract, settings, and deployment before treating them as controls for a PR integration. See OpenAI’s security and privacy information.
GitHub feature boundaries
GitHub’s security-feature application card says data handled by Copilot Autofix is not employed for LLM training. That statement is specific to Autofix and must not be generalized to every Copilot feature. Enterprise agent policies also have boundaries: third-party agents can have policies managed separately from the Copilot cloud agent, and local agents running in VS Code are not managed through GitHub’s enterprise controls. Use the GitHub security and quality AI-features card and the agent-management documentation to map the exact surface you use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Govern every agent surface independently
Central enterprise controls can improve consistency, but one organization switch rarely governs every way an agent can run. Build an inventory that distinguishes:
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- Cloud-hosted PR and issue agents
- IDE chat and local agents
- CI-based review bots
- Third-party agents connected through MCP
- Direct API clients and internal wrappers
For each surface, record who can enable it, which repositories it can access, which models and endpoints it may call, whether sessions and prompts are auditable, and how access is revoked. Review agent availability, session visibility, audit records, and external-tool policies at least quarterly and whenever a provider changes its terms.
Use a permission matrix
| Capability | Default for sensitive repositories | Exception process |
|---|---|---|
| Read repository files | Only approved paths and branches | Repository owner and security approval |
| Read issues and pull-request comments | Disabled unless the task requires it | Remove confidential discussion first |
| Read terminal output | Disabled for sessions that may contain credentials | Use sanitized logs |
| Run shell or network commands | Read-only sandbox with allowlisted commands | Time-limited approval and recorded session |
| Write files or open a pull request | Require human confirmation | Independent review before merge |
| Access MCP or external services | Allowlisted servers only | Security review for each server and scope |
Review generated changes as untrusted code
Protecting input data does not make the output safe. GitHub’s responsible-use guidance says users remain responsible for reviewing and validating generated responses, which can be inaccurate or insecure. Read the GitHub responsible-use guidance for Copilot Chat.
- Require a human owner for every AI-generated commit and pull request.
- Run unit, integration, regression, and security tests appropriate to the change.
- Use independent static analysis, dependency checks, secret scanning, and infrastructure-policy checks.
- Inspect authorization, input validation, cryptography, logging, and error handling manually.
- Compare the patch with the requested scope; remove unrelated refactors or generated files.
- Check that tests do not embed real customer data or newly exposed credentials.
For security-sensitive applications, require a reviewer who did not operate the agent and who can challenge its assumptions. An AI reviewer cannot serve as the sole independent review.
Respond when sensitive information may have been exposed
- Stop the flow. Disable the affected agent, revoke its repository or tool access, and pause automated runs.
- Contain credentials. Rotate tokens, keys, certificates, and passwords that may have appeared in files, prompts, terminal output, logs, or generated patches.
- Preserve sanitized evidence. Record the client, version, model, endpoint, repository, task, time, and known recipients without copying the sensitive payload into a new system.
- Determine the scope. Identify whether the data reached a provider, connected tool, human reviewer, cache, artifact, or downstream log.
- Apply contractual and legal procedures. Notify the security, privacy, and compliance owners when personal, regulated, or customer data is involved.
- Fix the boundary. Add or correct exclusions, move secrets out of the tree, narrow permissions, and repeat the synthetic-request test before re-enabling the tool.
A practical rollout checklist
- Classify the repository and name an accountable owner.
- Approve the exact AI product surface, model, endpoint, client, and hosting route.
- List files, issue sources, terminals, tools, and external systems the agent may access.
- Configure tool-specific exclusions for secrets and sensitive paths.
- Verify that exclusions apply to every agent mode, not only inline completion.
- Move credentials to environment variables, a vault, or encrypted secret storage.
- Use least-privilege, read-only, and short-lived credentials wherever feasible.
- Disable unnecessary repository, issue, terminal, network, shell, and MCP access.
- Inspect sanitized requests in a synthetic repository.
- Document training, abuse-monitoring, application-state, human-review, and deletion terms.
- Confirm enterprise policies and audit coverage for cloud, local, CI, and third-party agents separately.
- Require human review, tests, secret scanning, and security analysis for generated changes.
- Define rotation and incident-response steps before production use.
- Repeat the review after upgrades, policy changes, model changes, or new integrations.
How to choose between two AI PR deployments
There is no universal safest vendor or product. Compare the actual deployments against the same questions:
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|---|---|
| Context boundary | Can it read files beyond the diff, repository history, issues, terminal output, artifacts, or connected services? |
| Exclusion and inspection | Can exclusions be enforced centrally, and can the organization inspect sanitized requests? |
| Data handling | What are the training, abuse-monitoring retention, application-state, deletion, and human-review terms for this exact route? |
| Governance scope | Which IDE clients, PR integrations, local agents, third-party agents, and MCP servers are covered by policy and audit? |
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Choose the deployment that gives you a defensible context boundary and enforceable controls for the repository’s sensitivity, not the one with the broadest automation.
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