ADLC metrics: How to measure agentic development security

Agentic development lifecycle (ADLC) metrics evaluate both the security of AI-generated software outputs, including newly introduced vulnerabilities, risky dependencies, and live secrets, and the safety of agent behavior such as shell commands, file access, tool calls, and package installations. Because traditional AppSec measures often rise with agent-driven development volume without reliably reflecting risk, the guidance recommends normalizing confirmed findings per merged change or per 1,000 changed lines, distinguishing newly introduced issues from legacy debt, and comparing agent-authored code against similar human-authored changes. It emphasizes monitoring actions before execution, since post-commit scanning cannot detect potentially harmful commands, sensitive file reads, or installation scripts that run during an agent session, while separately reporting deterministic policy blocks and voluntary model refusals. Suggested measurements span AI supply-chain visibility through discovered-versus-approved agents, models, MCP servers, and skills; action security through pre-execution block rates, policy coverage, injection testing, and regressions; code security through evidence-backed, reachable or exploitable findings; and package security through blocked unapproved installs and reachable vulnerable dependencies. Organizations are advised to pair security measures with developer-flow indicators, establish a small baseline set of metrics over a month, and set targets based on risk tiers so that controls scale with AI-assisted development without unnecessarily slowing delivery.

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