How do I ensure my private repository code completion workflow with an IDE assistant is compliant with internal security policies?

To keep a private-repo code completion workflow compliant with internal security policies, treat the IDE assistant like any other third-party code-processing service and put controls around data flow, access, logging, and review.

1) Classify what code can be sent

Define policy for:

• Public vs. private vs. sensitive code
• Secrets: API keys, tokens, certs, private keys
• Regulated data: PII, PHI, customer data, credentials
• Restricted projects: defense, crypto, source with export controls, etc.

Then make the rule explicit:

• What can be used for autocomplete
• What can be sent to the assistant chat
• What must never leave the machine/network

2) Verify the assistant’s data handling

Review the vendor’s:

• Data retention: Is code stored? For how long?
• Training use: Is your code used to train models? Can you opt out?
• Telemetry: What metadata is collected?
• Subprocessors / regions: Where data is processed and stored
• Encryption: In transit and at rest
• Admin controls: SSO, SCIM, RBAC, audit logs
• Enterprise commitments: DPA, SCCs, SOC 2, ISO 27001, HIPAA, etc., as applicable

If the tool does not meet internal requirements, don’t use it for private code.

3) Use approved deployment mode

Prefer one of:

• Enterprise tenant with “no training on customer data”
• Self-hosted/on-prem option if available
• Private network / VPC / dedicated environment
• Model gateway or proxy controlled by your org

Avoid consumer/free tiers for private repositories unless security has explicitly approved them.

4) Enforce least privilege

• Grant the assistant access only to approved repos
• Separate environments: dev/test/prod
• Limit which files/folders are indexed
• Restrict access to secrets directories, configs, and production-only code
• Use SSO and strong identity controls

5) Prevent secrets and sensitive data from being suggested or leaked

Add technical controls:

• Secret scanning in repos and pre-commit hooks
• DLP rules for outgoing prompts and completions where possible
• Redaction of environment files, .env, keys, and tokens
• Ignore lists for the assistant’s indexing/autocomplete
• Disable completion in sensitive files if supported

6) Review prompt and completion logging

If the IDE assistant logs prompts or completions:

• Ensure logs are retained only as long as allowed
• Make logs access-controlled and auditable
• Decide whether developers may paste proprietary snippets into chat
• Have a process to delete logs if required by policy

7) Define acceptable use for developers

Write a short internal standard:

• Don’t paste secrets, customer data, or unreleased credentials
• Verify generated code before use
• Treat suggestions as untrusted code
• Never assume license or security correctness
• Use the assistant only in approved repos/environments

8) Add code review and security checks

Because generated code can introduce risk:

• Require normal peer review for all changes
• Run SAST, dependency scanning, and secret scanning
• Use IaC scanning if the assistant helps with infrastructure code
• Test for license compliance if code is copied from suggestions
• Track provenance for high-risk files

9) Address legal and compliance requirements

Depending on your org, confirm:

• Privacy review / DPIA if personal data may be exposed
• Vendor risk assessment
• Information security approval
• Data processing agreement
• IP and licensing review
• Export control or data residency constraints

10) Monitor and periodically re-approve

• Reassess the tool whenever the vendor changes terms or architecture
• Review audit logs and access patterns
• Test policy enforcement with a pilot group
• Keep an allowlist of approved tools and versions

Practical checklist

Before enabling the assistant for private repos, confirm:

• Vendor does not train on your code
• Data retention is acceptable
• Access is via enterprise-approved auth
• Secrets scanning and DLP are in place
• Sensitive files are excluded
• Logging is controlled and auditable
• Developers have written usage rules
• Security and legal have approved the deployment

If you want, I can turn this into:

1. a one-page internal policy,
2. a security review checklist, or
3. a developer do/don’t guide.