A practical checklist for developers and security engineers building with generative AI.

Generative AI has made it possible to ship working services in minutes. But faster code generation doesn’t automatically mean safer software.

As AI-assisted development accelerates especially, traditional security review cycles may not activate the way they were designed to. The risk isn’t that AI always writes insecure code, it’s that unsafe behavior can reach shared or production environments without anyone intentionally accepting that risk.

This guide outlines 15 structural security guardrails to help teams constrain AI-generated code, enforce deterministic controls, and prevent silent risk from prompt to production.

Why This Guide Exists

AI-assisted development changes workflow dynamics:

• Code is generated faster than it is reviewed
• More contributors can ship production services
• Model-level safeguards are not security boundaries

Security controls must live outside the AI — in templates, pipelines, infrastructure, and environment boundaries.

This checklist helps you engineer those controls directly into your development workflow.

What You’ll Find Inside

Each of the 15 guardrails includes: a clear explanation of the control, why it matters in AI-assisted development, and practical steps to implement it

Topics include:

• Hardened project templates
• Centralized authentication and authorization
• CI/CD security gates
• Secrets management enforcement
• Network egress restrictions
• Least-privilege roles for services and agents
• Dependency governance
• Environment isolation and promotion controls

Built for AppSec and Engineering Teams

This guide is for application security engineers, DevSecOps teams, platform engineers, developers using AI coding tools