DevSecOps emerged from a simple observation: security works better when it's not an afterthought. Instead of waiting until code is ready to deploy and then doing a security review, DevSecOps integrates security testing throughout the development process.
The goal is to catch issues earlier when they're cheaper to fix, and to make security a normal part of development rather than a separate gate that slows everything down. NIST's DevSecOps guidance provides a framework for implementing these practices.
What DevSecOps Looks Like
In practice, DevSecOps means automated security checks run as part of the normal development workflow. Push code, and security scans run automatically. Open a pull request, and it is automatically flagged if there are vulnerabilities in new dependencies. Try to merge code with known issues, and the pipeline automatically blocks it.
The emphasis on automation isbecause manual security reviews don't scale. You can't have a security engineer review every commit, but you can have tools check every commit against a set of rules.
The Tooling
Common DevSecOps tools include static analysis (SAST) that scans source code, software composition analysis (SCA) that checks dependencies, secrets detection that catches accidentally committed credentials, and infrastructure scanning that reviews configuration files.
These tools integrate with CI/CD systems (GitHub Actions, GitLab CI, Jenkins) and run automatically on triggers like commits or pull requests. Results are surfaced to developers where they're already working.
Where AI Coding Fits In
DevSecOps practices developed before AI coding agents became common. The tooling assumes a certain pace of development: humans writing code that gets reviewed and merged on a regular cadence.
AI agents disrupt this by generating code faster than traditional DevSecOps pipelines were designed to handle. A developer using an AI agent might generate and commit multiple features in a session, overwhelming review capacity.
This is where ACSM complements DevSecOps. While traditional DevSecOps catches issues in CI/CD, Corridor catches them during generation. Together they provide coverage at both the speed of AI generation and the thoroughness of pipeline scanning.