Teams that treat security as part of daily engineering work, not a final gate, ship faster and spend less time on rework. That’s the core idea behind DevSecOps. For teams building complex, regulated products, DevSecOps produces audit-ready evidence as a natural byproduct of normal delivery. Getting there takes the right testing methods, pipeline design, and traceability practices.

This guide covers what DevSecOps is, how it differs from DevOps, the testing methods that support it, and best practices for implementation in regulated environments.

What Is DevSecOps?

DevSecOps, short for Development, Security, and Operations, is a software development methodology that treats security as a shared responsibility across engineering, security, and operations teams throughout the software development lifecycle (SDLC).

Instead of reviewing security at the end, teams codify security policies, automate security testing, and generate compliance evidence at every stage from first commit to production. Security configurations are version-controlled and peer-reviewed through the same workflows developers already use for application code.

DevSecOps vs. DevOps

DevOps (Development and Operations) removes silos between development and operations to speed up delivery. DevSecOps extends that philosophy to security, moving it from a single checkpoint before release to automated scans at every pipeline stage. For teams building products under aviation standards like DO-178C or ISO 26262, deferring security to a final gate creates the same late-discovery problem their safety processes are designed to prevent.

Why Is DevSecOps Important?

DevSecOps builds security into the process from day one. Starting early reduces late-stage rework, keeps compliance evidence current, and catches vulnerabilities before they get expensive to fix.

Late-Stage Security Flaws Multiply Rework Costs

Software defects found in production cost far more to fix than the same flaws caught during development, and security vulnerabilities are no exception. Remediation costs spike after release because fixes trigger wider regression testing, field updates, and documentation updates.

A security flaw in an embedded medical device or avionics system doesn’t stay contained to the software team. It cascades through risk assessments, verification activities, design history files, and regulatory submissions.

AI-Generated Code Expands the Attack Surface

AI-assisted coding tools are accelerating delivery, but they’re also introducing new risks. Research shows that AI-generated code introduces security flaws in roughly half of cases. The bigger problem is speed: AI removes the bottlenecks (code review, debugging, team oversight) that used to catch vulnerabilities before production. Automated security scanning at every pipeline stage is one of the few ways to keep pace.

Disconnected Security Work Creates Traceability Gaps

Traceability is the ability to follow a requirement from its origin through design, implementation, and testing so you can prove each step was completed. In regulated programs, a security requirement that lives only in a compliance document creates a traceability gap. That gap works the same way as any other missing link in the V-model (the development lifecycle where requirements flow down through design and implementation, then back up through verification and validation).

When security controls can’t be traced to the requirements they satisfy, systems engineers end up rebuilding that evidence manually before audits.

Regulators Now Require Security-by-Design

Regulatory bodies increasingly mandate security-by-design. The FDA’s cybersecurity guidance for medical devices, issued June 2025, requires machine-readable Software Bills of Materials (SBOMs), secure update mechanisms, and traceability between cybersecurity controls and identified risks.

Aerospace programs under DO-178C, automotive teams under ISO 26262 and ISO/SAE 21434, and medical device teams under FDA guidance all face the same expectation. Compliance requires evidence of continuous security integration, not documentation assembled after the fact.

How Does DevSecOps Work?

DevSecOps typically combines shift-left and shift-right security practices, wired into CI/CD (continuous integration and continuous delivery) workflows so that findings turn into tracked engineering work.

Security Testing Runs at Both Ends of the Pipeline

Shift-left security moves testing to the earliest possible stage of development so teams catch defects while they’re still cheap to fix. Pre-commit hooks scan for exposed secrets, static analysis runs on every code commit, and dependency checks flag vulnerable libraries at build time.

Shift-right security extends monitoring into production through canary deployments (releasing changes to a small subset of users first), runtime vulnerability detection, and chaos engineering (deliberately injecting failures to test resilience). When production monitoring detects a new vulnerability pattern, that pattern feeds back into pre-deployment scans so future builds are automatically checked. This combination of shift-left and shift-right with continuous feedback is sometimes also referred to as “shift everywhere.”

Automation Keeps Pace With CI/CD Delivery

Manual security reviews can’t keep pace with CI/CD pipelines that build and deploy multiple times per day. Automated scans at each pipeline stage replace manual checkpoints, and policy gates can block a release when it violates organization-defined governance rules.

Policy-as-code frameworks like Open Policy Agent (OPA) codify those rules as version-controlled files that enforce standards automatically. The result is compliance automation built into the pipeline instead of a separate audit activity. AI-assisted security tools are adding another layer here, with machine learning models that prioritize scan findings by exploitability and suggest code fixes. When those findings connect back to requirements, teams can trace a vulnerability from detection through remediation and verification.

Findings Become Tracked Engineering Work

Security scan results only matter if they reach the right people and get resolved. The common approach is to route findings from scanning tools into existing issue trackers (Jira, Azure DevOps, GitLab Issues) so vulnerabilities are prioritized alongside other engineering work. A critical CVE in a production dependency might page the on-call engineer and block the next deploy, while a low-severity finding like a missing HTTP header enters the normal sprint backlog.

Testing Methods and Tools for DevSecOps

The scans referenced above fall into four categories of security testing, each designed to catch a different class of vulnerability. A mature DevSecOps pipeline runs all four, with one or more tools per category:

• Static Application Security Testing (SAST): Analyzes source code without executing the application to catch buffer overflows, SQL injection flaws, and insecure coding patterns. Common tools include SonarQube, Semgrep, and Checkmarx.
• Dynamic Application Security Testing (DAST): Tests running applications from an external perspective to surface runtime issues and deployment-specific vulnerabilities that SAST can’t detect. Tools like OWASP ZAP and Burp Suite Enterprise validate whether a flaw is actually exploitable.
• Software Composition Analysis (SCA): Checks third-party libraries and open-source dependencies against vulnerability databases like the National Vulnerability Database, and generates SBOMs for compliance. Common options are OWASP Dependency-Check, Snyk, and Trivy.
• Infrastructure as Code (IaC) scanning: Catches cloud and Kubernetes misconfigurations in Terraform, CloudFormation, and manifests by applying policy-as-code rules against frameworks like Center for Internet Security (CIS) Benchmarks. Checkov, Terrascan, and KICS are widely used for this.

Running all four requires coordination between security and engineering teams, which is where most organizations hit friction.