The ADLC maturity model: stages of agentic development security

The proposed ADLC security maturity model evaluates how effectively organizations govern coding agents’ runtime actions and code-related outputs rather than how widely they have adopted AI tools. It defines five stages, from unmanaged experimentation with no inventory or controls, through visibility and centralized logging, deterministic guardrails, security feedback integrated into agents’ workflows, and finally continuously verified, evidence-driven governance. Each stage addresses both agent actions, including commands, file access, MCP calls, permissions, and package installs, and agent outputs such as code, dependencies, and secrets, reflecting risks including prompt injection, excessive permissions, unreviewed tools, and credential exposure. Stage 3 is presented as the crucial threshold because policies must be technically enforced through mechanisms such as scoped permissions, sandboxes, MCP allowlists, action-blocking hooks, and malicious-package prevention rather than merely suggested through prompts or configuration files. Reaching Stage 4 requires giving agents actionable security context while controlling alert fatigue through reachability and exploitability-based prioritization, while Stage 5 adds automated evidence, adversarial testing, bypass checks, policy-drift reviews, and metrics for audits and regulatory expectations. Organizations should score action and output controls separately using evidence, treat their lowest-scoring area as their actual maturity level, and avoid mistaking tool deployment, prompt-based rules, or noisy blocking checks for meaningful security progress.

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