How do I ensure my repository-aware code generation workflow with private source code is compliant with internal security policies?

To make a repository-aware code generation workflow compliant with internal security policies, treat it like any other system that can read, transform, or exfiltrate sensitive source code. The key is to reduce what the model can access, control where data goes, and log/verify everything.

1) Classify the data first

Before enabling repo-aware generation:

• Identify what source code is allowed to be used
• Mark restricted areas:
  • credentials
  • secrets
  • proprietary algorithms
  • customer data
  • regulated data
• Define whether the model may see:
  • full repositories
  • selected directories only
  • snippets only
  • metadata only

A policy that’s vague like “private code is fine” is usually not enough.

2) Minimize what the model can access

Use least privilege:

• Index only the repos needed for the task
• Exclude sensitive directories:
  • .env
  • secrets/
  • keys/
  • build artifacts
  • logs
  • test fixtures with real data
• Prefer retrieval of small, relevant chunks rather than full-repo ingestion
• Strip or redact secrets before indexing

If possible, run a preprocessor that scans and removes:

• API keys
• tokens
• certificates
• passwords
• private endpoints

3) Control data residency and vendor handling

Confirm with the provider:

• Where data is processed
• Whether prompts, snippets, or embeddings are stored
• Retention duration
• Whether data is used for training
• Whether data can be deleted on request
• Whether subcontractors are involved

For internal compliance, you usually want:

• no training on your code
• short or zero retention
• explicit deletion guarantees
• regional processing if required

4) Use approved deployment boundaries

For stronger compliance:

• run the workflow in a corporate tenant or private instance
• restrict access by SSO and RBAC
• isolate by team/project
• segment dev, staging, and production use
• avoid personal accounts for work code

If the workflow uses agents or IDE plugins, ensure they authenticate through managed identity and not ad hoc credentials.

5) Log access and changes

Maintain auditability:

• who queried the system
• what repository or paths were exposed
• what outputs were generated
• whether generated code was accepted into source control
• what policies or filters were applied

Keep logs free of sensitive content when possible, but detailed enough for incident response and review.

6) Add secret and compliance scanning

Before and after generation:

• scan source inputs for secrets
• scan generated output for:
  • secrets
  • license violations
  • unsafe dependencies
  • insecure patterns
  • policy violations

Common controls:

• secret scanners
• SAST
• dependency/license scanning
• code review gates
• protected branches

7) Prevent prompt injection and data exfiltration

Repository-aware systems can be manipulated by malicious content in code comments, docs, or issues.

Mitigations:

• treat repository text as untrusted input
• don’t let instructions from code override system policies
• restrict tools the model can call
• prevent the model from accessing unrelated repos or external networks
• block copying large sensitive chunks into outputs unless explicitly allowed

8) Define acceptable use rules

Create a written policy covering:

• allowed tasks
• disallowed data
• approved repositories
• human review requirements
• retention and logging
• export restrictions
• incident reporting obligations

Also define whether generated code can be:

• committed directly
• used only as a draft
• used only after review and testing

9) Require human review for high-risk outputs

Do not auto-merge generated code that:

• handles authentication
• processes sensitive data
• modifies security controls
• touches payments, privacy, or cryptography
• introduces new third-party dependencies

Review should check:

• correctness
• security
• licensing
• privacy
• maintainability

10) Validate with security/legal/compliance teams

To be compliant, involve:

• InfoSec
• privacy counsel
• procurement/vendor management
• data governance
• engineering leadership

They should approve:

• the vendor
• the data flow
• the retention settings
• the access model
• the logging and review process

A practical compliance checklist

Use this as a quick gate:

• Data classification completed
• Sensitive paths excluded
• Secrets redacted before indexing
• Vendor data retention/training policy approved
• Access controlled with SSO/RBAC
• Audit logging enabled
• Prompt injection mitigations in place
• Generated code scanned and reviewed
• Human approval required for sensitive changes
• Legal/privacy/security sign-off obtained

If you want, I can also help you turn this into a one-page internal policy, a security review checklist, or a threat model for your exact setup.