DevSecOps

When teams moved to DevOps and began releasing many times a day, traditional security reviews could not keep up. A security team that tested applications once before a quarterly release became either a bottleneck that slowed delivery or a formality that was skipped. DevSecOps resolves that tension by moving security into the delivery process itself. Security checks run automatically on every change, developers get findings while they still remember the code, and security specialists spend their time on guidance, tooling and the hardest problems instead of reviewing every release by hand. The name reflects the change: security becomes part of DevOps rather than a separate stage at the end.

The approach has grown alongside a rise in software supply chain attacks, in which attackers compromise open source packages, build systems or third-party components rather than the application itself. Frameworks such as NIST's Secure Software Development Framework and the SLSA standard for build integrity, and the growing use of software bills of materials, all assume security is part of the pipeline. For engineering leaders, application security teams, platform teams and compliance staff, DevSecOps is how security works at the speed of modern delivery.

Definition of DevSecOps

DevSecOps is an extension of DevOps in which security practices, tools and responsibilities are integrated throughout the software lifecycle, from design and coding through build, testing, deployment and operation, with security checks automated in the delivery pipeline wherever possible.

It builds on DevOps, adds security stages to the CI/CD pipeline and is part of a secure software development life cycle (SDLC).

Security Practices in the Pipeline

Static application security testing (SAST) scans source code for vulnerabilities such as injection flaws before the code is built.

Software composition analysis (SCA) checks open source dependencies for known vulnerabilities and license issues, which matters because most applications are built largely from third-party code.

Secrets scanning catches passwords, keys and tokens accidentally committed to repositories.

Dynamic testing (DAST) probes running applications, while infrastructure as code scanning checks cloud configurations for risky settings before they are deployed. Container image scanning checks the operating system packages and libraries inside each image, which are often overlooked because developers did not write them.

Shifting Left

Shifting left means finding security problems earlier in the lifecycle, when they are cheaper and faster to fix.

Threat modeling during design identifies risks before code is written. Checks in developers' editors and pull requests catch issues before they are merged.

Shifting left does not remove later checks. Runtime monitoring and incident response remain essential, because some risks appear only in production. The aim is defense in depth, with checks at several stages so that a problem missed by one is caught by another.

Importance of DevSecOps in Delivery and Engineering Operations

DevSecOps lets security keep pace with delivery. Automated checks run on every change without waiting for a manual review slot.

It reduces risk. Vulnerabilities are found and fixed in days rather than months, shrinking the window in which they can be exploited.

It also builds shared ownership. Developers learn secure practices through fast feedback, and security teams gain influence across the organization rather than only at release gates. For AI features, the same approach extends to checks for risks covered under AI security, such as prompt injection and unsafe model outputs.

DevSecOps and Financial Management

Fixing a vulnerability during development costs far less than fixing it after a breach, which can involve incident response, legal costs, fines and lost customers.

Automated checks reduce the cost of manual security reviews and penetration tests for routine changes, reserving expert time for high-risk areas.

Security tools carry license costs and require tuning, so their value should be measured by findings fixed, not just findings reported.

Evidence produced automatically by the pipeline lowers the cost of audits and customer security questionnaires.

Common Challenges

Security tools often produce many false positives, and developers learn to ignore them. Tuning rules and prioritizing by real risk keeps findings credible.

Adding too many blocking checks at once can slow delivery and create resistance. Starting with warnings and blocking only critical issues helps.

Security specialists are scarce, so programs that rely on them reviewing every change cannot scale. Ownership of findings can also be unclear. Every finding needs a team responsible for fixing it, or it will sit in a report indefinitely.