Best Copilot settings for enterprise codebases

Short answer
Use per-repo/per-team Copilot policies: conservative defaults for sensitive code, more permissive inline suggestions for day-to-day app code. Combine on-device/minimal-sharing telemetry with centralized request logging, per-repo enablement, and mandatory review/SAST of AI-generated code.

Recommendation (practical config)
- Suggestion frequency: default to “on-demand” (manual trigger) for repos tagged Sensitive/Regulated; allow “inline/continuous” suggestions for General/Library repos. If inline is allowed, limit suggestion size to single-line or single-function (no whole-file completions).
- Telemetry & data sharing: disable vendor training on private repos if your vendor supports it; centralize and retain request/usage logs for audit (90–180 days typical). Only collect minimal metadata (who, repo, timestamp, request id) — avoid storing full request/response payloads unless required for investigations.
- Policy controls: enforce per-repo enable/disable, file-type denylist (e.g., secrets, .env, crypto keys), block whole-file suggestions for critical modules, and require attribution metadata for AI‑suggested code (comment headers or git commit footers).
- Audit & approval: require human review and SAST/linters for any AI-suggested code before merge; tag PRs that include Copilot suggestions for faster review tracking.
- Access & auth: require org-managed accounts (no personal tokens), enforce MFA, and use scoped service tokens for any automation.

Decision criteria (how to choose stricter vs. looser)
- Code sensitivity: High sensitivity (IP, crypto, regulated PII) → disable or on-demand only. Low sensitivity (UI components, internal tooling) → inline allowed.
- Team size & velocity pressure: small teams needing speed may accept inline suggestions + mandatory review. Large orgs benefiting from consistency should favor central controls and logging.
- Compliance & legal risk: if subject to audits/NDAs/regulators, choose conservative telemetry and strict per-repo controls.
- Budget & skill level: tighter controls require engineering work (policy enforcement, logging, training). If budget/skills are limited, start with per-repo disable/on-demand and gradually relax.

Best-for / Avoid-if
- Best for: accelerating routine development, generating tests, code examples, and boilerplate while preserving audit trails.
- Avoid if: working on highly proprietary algorithms, core IP, or regulated data without strong review + logging.

Practical rollout checklist (ordered)
1. Inventory & classify repos by sensitivity (owner).
2. Create policies: Disabled / On-demand / Inline with size limits for each class (security lead + engineering manager).
3. Configure Copilot at org level (enable opt-out of training if available) and per-repo settings (devops).
4. Implement telemetry pipeline: collect minimal metadata, index by repo/user, set retention and access controls (SRE/security).
5. Add guardrails: denylists, disable whole-file completions in critical dirs, enforce org accounts and MFA (IAM).
6. Integrate into CI: tag PRs with AI-suggested code, run SAST and license checks, require human review before merge.
7. Train developers on expectations and how to annotate AI-generated code (engineering manager).
8. Periodic audit: review logs, false positives, and policy effectiveness quarterly (security + audits).

Notes
- The right balance depends on budget (engineering effort to enforce policies), skill level (ability to review AI code), and stage of workflow adoption. Start conservative for critical code and open up for lower-risk areas.
- If you use GitHub Copilot, map these controls into the Copilot for Business admin settings (per-repo enablement, data sharing options) and integrate with your org’s CI + logging.

If you want, I can convert this into a concrete policy matrix (Disabled / On‑demand / Inline) and sample enforcement scripts for CI and commit hooks.

Compare GitHub Copilot and Cursor