The Challenges of Building a DevSecOps Platform
Table of Contents
- Introduction
- What Is a DevSecOps Platform, Really?
- The First Obstacle: Standardization Without Stifling Teams
- The Trap of Custom Pipelines
- Security and Quality as Part of the Flow
- When Automation Becomes Complexity
- Lessons Learned
- Conclusion
Introduction
Building a DevSecOps platform might sound like a purely technical challenge: pipelines, orchestrator, security scanners, quality gates, and so on. But over time, I realized that the real challenge isn’t just technical — it’s organizational too!
When I started working on our internal DevSecOps platform, the goal was simple: unify pipelines and make delivery more predictable and secure. The problem? Every team had its own stack, dependencies, and way of thinking. And that doesn’t change with YAML.
The result was a collection of highly customized pipelines that solved individual problems but silently created a new one: maintaining what was both standardized and not standardized at the same time.
What Is a DevSecOps Platform, Really?
A DevSecOps platform is not just a toolbox of CI/CD scripts and scanners. It’s a layer of abstraction between product teams and the underlying engineering complexity.
Its purpose is to provide:
- Reproducible automation without sacrificing flexibility;
- Governance by design, not by enforcement;
- Continuous integration of quality and security, right from code creation.
In practice, it’s an internal product — and developers are your customers. The moment you start treating the platform as a product, everything changes.
The First Obstacle: Standardization Without Stifling Teams
One of the biggest mistakes I’ve seen (and made) is confusing standardization with rigidity.
It’s tempting to create a single pipeline template and force everyone to use it. That works — until the Mobile team needs macOS builds, and the Backend team builds Go binaries across multiple architectures.
The real challenge is to balance autonomy with governance:
- Let teams customize their pipelines when needed;
- But ensure every pipeline passes through the same checkpoints — quality, security, versioning, deployment, and observability.
The key for us was designing a system of dynamic templates, where pipelines are generated based on declared stack metadata rather than duplicated YAML.
The Trap of Custom Pipelines
Our initial scenario was simple: “Each team builds its own pipeline — we’ll support them.” It sounded agile, but it didn’t scale.
With dozens of unique pipelines, even a small update — a variable name or a scanner version — required dozens of merge requests across repositories. That pain led us to build declarative, dynamic pipelines — something like this:
“If the project uses Java, include the base template for java; if it’s Node.js, include the base template for javascript/typescript with it's own tweaks.”
This approach drastically reduced duplication and turned our platform into an orchestrator of behaviors, not a collection of hard-coded YAMLs.
Security and Quality as Part of the Flow
Another common mistake is treating security and quality as “extra steps.” They should be organic parts of the build process.
An ideal pipeline doesn’t “run SonarQube” — it produces quality metrics automatically, using predictable paths:
target/site/jacoco/jacoco.xml(Java/Kotlin)coverage/lcov.info(JavaScript/TypeScript)coverage.xml(Python)
Similarly, tools like Trivy or Dependency-Track shouldn’t live on the sidelines. They should be first-class citizens in the delivery flow — triggered automatically, with actionable feedback.
This creates what I like to call governance by design: developers don’t have to remember security — the process already enforces it.
When Automation Becomes Complexity
Automation solves many problems… until it becomes one itself.
As we added integrations — GitLab, ArgoCD, Helm, SonarQube, Trivy, Kafka — the platform started behaving like a living ecosystem, with dependencies, queues, events, and its own logs.
At that point, observability and traceability become essential. Without them, the platform stops being an ally and turns into a black box.
I learned that not everything should be automated. Automate what’s predictable and high-value. Document what’s variable and human.
Lessons Learned
After years evolving this platform, a few lessons stand out:
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Pipelines are code — treat them like it. Version them, review them, and follow semantic versioning.
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Don’t start with tools. Start with the developer journey — how they build, test, ship, and monitor.
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Autonomy and governance aren’t opposites. A platform should provide guardrails, not walls.
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What’s not automated must be observable. Logs, metrics, and visual feedback are part of the user experience.
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Treat the platform as a product. Have a backlog, roadmap, and adoption metrics. Your engineers are users — they deserve good UX too.
Conclusion
Building a DevSecOps platform is an exercise in balance. It’s not about gluing tools together — it’s about creating an ecosystem that harmonizes people, processes, and technology.
Success doesn’t come from running the most advanced pipeline. It comes when a developer can push code and know — instantly — that it’s secure, tested, and production-ready.
If there’s one thing I’ve learned, it’s this: a great platform doesn’t automate everything — it just makes the hard things feel effortless.
Written by AI, revised by Iago S. Rodrigues — Software Engineering Specialist & DevSecOps Engineer.