Secure by Default: How to Make AI Code Generation Safe in Production

Introduction

AI code generation has quickly become standard in many engineering departments. From autocomplete tools to fully AI-authored functions, developers are shipping code faster than ever. But while AI accelerates productivity, it often skips over security by default — introducing risks that grow with every unchecked commit.

Securing AI-generated code doesn’t require slowing down your developers. With the right controls in place, you can shift from detect-and-alert to detect-and-remediate — enabling a secure-by-default pipeline.

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1. Set Guardrails Within the IDE

Developers working with AI tools need support inside the environments where they write code. This includes:

• Pre-configured templates that use secure patterns
• Local linters and SAST tools
• Real-time policy feedback based on security best practices

Why it matters: Developers are more likely to follow security guidelines if enforcement happens automatically and in context.

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2. Use Pre-Commit Hooks and Scanning in CI

Security should be enforced before code ever reaches production. Pre-commit hooks and CI-based scanning ensure that vulnerable code is caught early in the development lifecycle.

• Scan for secrets, insecure functions, and dependency risks
• Fail builds when high-severity issues are detected
• Alert developers within pull requests

Tools to consider: Semgrep, Trufflehog, OWASP Dependency-Check, GitHub Advanced Security

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3. Auto-Remediate What You Can

Even if vulnerabilities are flagged early, remediation is often slow — especially when AppSec teams are small. Automatic remediation helps bridge this gap by fixing known issues without waiting on manual intervention.

Mobb provides auto-remediation for vulnerabilities directly in your repositories, reducing mean time to remediation (MTTR) and keeping security in pace with development.

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4. Validate Before Merge

AI-generated code should never be merged to production without validation. In vibe coding workflows, where developers often work alone or skip reviews, this step becomes even more important.

Set up enforcement rules for high-risk changes:

• Require two approvals or automated test pass
• Block merges when vulnerabilities are present
• Run security tests alongside unit and integration tests

5. Monitor for Drift in Production

Even with all controls in place, post-deployment monitoring is critical. Infrastructure or application misconfigurations can emerge from later code changes, scaling issues, or human error.

• Use RASP tools to monitor runtime behavior
• Continuously scan production assets for drift
• Feed incident data back into developer workflows

Conclusion

Securing AI-generated code is not about limiting developers — it’s about building workflows that make security the default. By integrating controls early and automating remediation wherever possible, teams can accelerate delivery while reducing risk.

To better understand how vibe coding introduces these security gaps, read this deep dive on common risks. And for help recognizing whether your team is already vibe coding, this checklist can help you evaluate.