Short answer
If you’re seeing Copilot suggest snippets that resemble private code, treat it as an immediate security incident and then harden configuration, auditing, and policies to stop further leakage. The two safe enterprise options are: (A) restrict/disable Copilot usage in sensitive contexts, or (B) move to Copilot Enterprise / private completions with “do not use org data to train” data controls or a self-hosted private model. Which is right depends on budget, risk tolerance, and engineering skill.

Recommendation (practical):
1) Immediately disable Copilot access for users who worked in the affected repos or disable the Copilot extension centrally for sensitive workstations. 2) Use GitHub org admin settings to opt out of sending org code to model training (Copilot Enterprise / Business supports data controls) or remove Copilot from those repos. 3) Run an IR: identify user/time via audit logs, search public code for the leaked snippet, rotate secrets, and communicate to legal if necessary.

Decision criteria (how to choose a permanent path)
- Budget: If you can pay for Copilot Enterprise or a private LLM + infra, you can keep productivity and control data. If not, you may need to ban external AI assistants for high-risk code.
- Team size & workflow: Large orgs benefit from central policy and audit logging; small teams may prefer self-hosted models or strict bans.
- Skill level: Self-hosted private models require ML/infra expertise; Copilot Enterprise requires admin configuration but less ops work.
- Output quality vs. risk: Public-trained assistants are higher quality but carry more leakage risk; private models reduce leakage but need tuning.

Practical checklist
Immediate (0–24h)
- Revoke or disable Copilot extensions for involved users or sensitive machines. If using managed endpoints, push policies to block the extension.
- Pull GitHub audit logs (org-level audit log) to find which accounts used Copilot and when.
- Rotate any credentials or secrets that could have been exposed.

Short-term (days)
- Search public code (GitHub, search engines) for the leaked snippet to assess spread.
- Enable or verify Copilot data controls: opt out of using org code for model training (contact GitHub support/sales if you don’t see the option).
- Configure repository-level restrictions: remove Copilot from private repos with sensitive IP, or whitelist approved repos only.
- Add pre-commit or CI checks that detect internal-snippet re-use and secrets (fuzzy match, rolling hashes, or substring scan).

Medium/long-term (weeks+)
- Implement enterprise policy: allowed tools list, mandatory review of multi-line completions, and a code-acceptance checklist before merging suggestions.
- Centralize monitoring: ingest Copilot-related audit events into your SIEM for alerting.
- Consider a private/codebase-only completion solution (Copilot Enterprise private mode or self-hosted LLM) if leak risk is unacceptable.
- Train developers: best practices for using code completions, and disciplinary/education policy for violations.

Detection & remediation steps
- Use audit logs to map user/time and IDE/host. Cross-check with source-control commits to see if suggestion was accepted verbatim. If external publication is found, follow incident response: rotate keys, issue takedown requests, involve legal.

Best-for / Avoid-if
- Best-for: Enterprises that require productivity plus data controls = Copilot Enterprise with training disabled or a vetted private completion service. Good for medium+ budgets and teams that want low ops overhead.
- Avoid if: you can’t control endpoints or the risk tolerance is zero — then block public assistants and use internal-only models.

Notes
- The exact admin toggles and audit events depend on your GitHub tier; consult your GitHub Enterprise admin docs or contact GitHub support to enable the specific “do not use org code for training” option and to expose Copilot audit events.
- If you want a vendor comparison or migration plan (Copilot Enterprise vs. a self-hosted private model), I can outline options tailored to your budget and infra maturity.

Tools mentioned: GitHub Copilot (for admin settings and enterprise data controls).

Compare GitHub Copilot and Cursor