Short diagnosis
Copilot gives short, context-driven completions and will mirror patterns in the file/repo and public training data; ChatGPT returns longer, explainable completions because you can prompt it with explicit security instructions. The mismatch you’re seeing is normal: Copilot picks whatever fits the local context fastest, while ChatGPT follows the explicit prompt it’s given. The fix is to combine (1) guardrails and automation around Copilot, (2) tuned prompts and templates for ChatGPT, and (3) CI/IDE enforcement so insecure suggestions never reach main branches.

Recommendation (one-sentence)
Treat Copilot as a productivity layer, not a security oracle: enforce secure defaults via IDE snippets/comments + linters/semgrep/CodeQL in CI, and use ChatGPT (with a security system prompt) for design/explanations and generating vetted snippets.

Decision criteria (pick based on these)
- Budget: full automated enforcement (SAST, commercial policy engines) costs more; small teams get most value from linters + pre-commit hooks + snippet libraries.
- Skill level: junior-heavy teams need stricter pre-commit checks and templates; senior teams can rely more on peer review plus SAST.
- Workflow stage: exploratory prototyping can allow looser Copilot suggestions; gate them at PR/CI before merge.
- Output quality vs speed: if you need fast drafts, keep Copilot but require security CI; if production-ready code is needed, require ChatGPT-created vetted snippets or approved templates.

Practical checklist (implement in this order)
1. Create a short company Secure Coding Checklist (OWASP+language specifics) and publish in repo README.
2. Add linters and rules (ESLint, Bandit, gosec, etc.) configured to your standards with autofix where safe.
3. Add semgrep/CodeQL rules for high-risk patterns and fail CI on critical findings.
4. Add pre-commit hooks to run fast checks locally (lint + unit smoke + semgrep quick rules).
5. Maintain an “approved-snippets” private repo with secure helpers (DB wrappers, auth helpers). Encourage devs to import these, and use file-level context so Copilot suggests those patterns.
6. Use in-file prompt comments for Copilot: explicitly describe security constraints above the function (e.g., // Use parameterized queries; validate input; no eval). Copilot honors file comments and signatures.
7. Create a ChatGPT system prompt template for engineers: e.g., “You are a secure-coding assistant. Produce code that follows X rules; return a 1-line security rationale and tests.” Use ChatGPT to produce vetted patterns and copy them into approved-snippets.
8. Enforce PR review checklist items (security tests run, dependencies scanned, snippet provenance recorded).
9. Run periodic audits of Copilot-sourced PRs and add semgrep rules for recurring issues.
10. Train devs on the workflow so they know when to rely on Copilot vs request ChatGPT-vetted code.

Best-for / Avoid-if
- Best for: teams wanting fast dev flow but enforceable safety — keep Copilot enabled + CI gates and an approved snippet library.
- Avoid if: you have zero CI or no linters — then Copilot will surface insecure code unchecked.

Example prompts (practical)
- For in-file comment (Copilot):
// Implement insertUser(); use parameterized queries only, reject SQL concatenation, sanitize email with X rule.
- For ChatGPT system prompt:
“You are a security-focused coding assistant. Always follow OWASP Top 10, avoid insecure deserialization, XSS, SQL injection; return a secure implementation, short rationale, and unit test.”

When this varies
If you’re low on budget, start with linters + pre-commit + approved-snippets. If you have budget and scale, invest in SAST + automated policy enforcement and integrate that into the Copilot/IDE feedback loop.

If you want, I can draft a concrete in-file comment template and a ChatGPT system prompt tuned to your language/framework.

Compare GitHub Copilot and Cursor