ADLC security best practices: how to secure agentic development from prompt to production

Agentic Development Lifecycle (ADLC) security extends traditional secure development practices to govern what AI coding agents can access, execute, install, and deliver throughout development, rather than only inspecting their output at pull-request time. It emphasizes maintaining inventories and approval controls for agents, models, MCP servers, skills, and behavior-altering configurations; treating these components as dependencies whose versions, permissions, and changes require review. Because agents can read sensitive files, run commands, retrieve untrusted content, and install packages before human review, the approach recommends least-privilege sandboxing, short-lived task-specific credentials, restricted network access, and deterministic pre-execution hooks that block or audit prohibited commands, secret reads, and unapproved tool calls regardless of model instructions. Package checks should occur at installation time to prevent malicious, squatted, or hallucinated dependencies from executing scripts, while generated code and pull requests require security review based on reachability and exploitability. The framework also calls for provenance, signing, protected release workflows, logging, and independent human approval to prevent pipeline tampering or agents approving their own changes, reflecting evidence that AI-generated code can introduce security flaws at substantial rates.

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