Bare Metal Cyber

DevSecOps in continuous integration and continuous delivery pipelines can sound like a buzzword salad, but the problem it tries to solve is very real. You have code moving from a developer’s laptop to production many times a day, and a lot of that journey is now automated. If security still depends on side conversations, slow reviews, or manual sign-offs, it simply cannot keep up. This narration is part of the Tuesday “Insights” feature from Bare Metal Cyber Magazine, developed by Bare Metal Cyber, and it focuses on what DevSecOps inside those pipelines really looks like when it is working for you instead of against you.

At a simple level, DevSecOps in CI/CD pipelines means this: the same automation that compiles, tests, and deploys code also runs meaningful security checks and routes clear feedback back to the people making changes. It is an approach to delivery, not a product. Instead of security living as a separate stage at the end, it is woven into the normal path a change follows, from a pull request, through build and test, into staging, and finally production. The goal is not perfection. The goal is that every trip through the pipeline gives security a fair chance to spot issues and stop or shape risky changes before they land.

You can think of it as a mix of culture, process, and tooling that stretches across development, operations, and security teams. On the people side, it asks everyone to accept that they share responsibility for risk, not just the security group. On the process side, it changes how pull requests, approvals, and exceptions work, because security expectations become visible parts of those workflows. On the technology side, it shows up as jobs in the pipeline that scan code, dependencies, containers, and infrastructure definitions, and then either pass the baton or stop the run when something serious appears.

It also helps to be clear about what DevSecOps in CI/CD pipelines is not. It is not “we have a pipeline, so we are secure now.” It is not “we dropped one static analysis job into the build and never tuned it.” It is not “security reviews happen a week after deployment, when someone has time.” Those patterns can all coexist with the DevSecOps label on a slide deck, but they do not change how risky code actually moves. Real DevSecOps shows up when changing the code means changing the risk checks at the same time, because they live in the same path.

The flow usually starts the moment a developer opens a pull request. The version control platform becomes the first gate, and that gate is automated. Branch protections enforce code review, required checks must pass, and tests run before anything can merge. Security enters quietly at this point through things like secret scanning, simple static analysis, and linters that look for patterns known to lead to bugs. Results are presented right next to normal test failures, so a security issue feels like any other quality issue: something to fix before the change can move forward.

Once code merges, the continuous integration system takes over. It compiles the code, runs unit and integration tests, and builds artifacts or container images. Security stages sit side by side with these steps. Software composition analysis checks open source libraries for known vulnerabilities. Container scanners inspect base images for issues. Infrastructure-as-code checks validate cloud templates or Kubernetes manifests against basic security rules. Each of these stages returns structured results the pipeline understands. If a high severity issue appears, the job fails, and the change stops in a very visible way.

For this to work, a few big assumptions have to be true. Code must live in a shared version control system with consistent branching patterns, so that checks can be defined once and applied repeatedly. Pipelines must be defined as code, reviewed and versioned in the same way as the application. Identities and permissions around pipeline systems need to be solid, because an attacker who can change the pipeline can silently remove your guardrails. Most importantly, leadership has to treat a security finding that blocks a build as normal quality control, not a reason to bypass the system whenever a deadline looms.

In everyday work, DevSecOps in CI/CD pipelines shows up as a set of patterns teams use over and over. One of the most common is what many people call “shift-left hygiene.” Secret scanning, simple static checks, and dependency scans run on every pull request. They catch easy issues before they grow: exposed keys, known-vulnerable libraries, or unsafe coding patterns that repeat across the codebase. For developers, this feels like an extra set of tests that happen automatically rather than a separate process to remember.

Another pattern focuses on the artifacts and infrastructure that carry your code into production. Pipelines build from a small set of approved base images. They scan those images on each run and reject builds when critical vulnerabilities show up. At the same time, infrastructure templates are checked to ensure that things like encryption, network exposure, and identity configuration meet simple rules. Over time, people stop thinking “will someone remember to check this” and start thinking “if the pipeline passed, the basic guardrails are in place.” The pipeline becomes a kind of living standard that each service inherits.

There are also more advanced patterns that require deliberate investment but pay off over time. Some teams use policy-as-code tools in their pipelines to enforce organizational rules automatically. For example, they might require that internet-facing services always have certain protections enabled, or that services touching sensitive data go through extra checks before deployment. Others add lightweight dynamic security tests that probe staging environments after each significant change. A realistic quick win is to add secret scanning and dependency checks on pull requests for one critical application. A more strategic move is to define a standard pipeline template with built-in security stages and then encourage or require teams to adopt that template whenever they create a new service.

The benefits, when this approach is working, start with consistency. Every change, big or small, encounters the same security checks. There are fewer special paths where a release slips around the usual process. That consistency reduces the chance that a risky change takes the “short way” into production. It also makes security visible to engineering teams, because pipeline failures and logs live in places they already watch, rather than in separate dashboards that only security specialists ever open.

Another benefit is faster learning. When a security issue breaks the build, the feedback comes right next to the code that caused it. Developers can fix it while the context is fresh in their minds. Over time, the team learns which patterns cause trouble, which ones keep them out of trouble, and how to encode that knowledge into tests and rules. Those tests and rules then spread through shared pipeline templates, so that other teams benefit without having to repeat the same mistakes.

There are real trade-offs and limits, though. Every new stage in the pipeline adds some time to the build, and heavy scans can cause slow feedback if they are not tuned. Someone has to own these tools, adjust their rules, and handle false positives, or the team will quickly lose trust in the results. Cultural friction appears when security stages are seen as arbitrary or unhelpful, especially if they block important releases without clear explanations. And some risks never show up in pipeline checks at all, such as subtle business logic flaws or attacks that target people rather than systems. DevSecOps in CI/CD pipelines is powerful for guardrails and known patterns, but it does not replace deeper practices like threat modeling or careful review of high-risk changes.

When this approach fails, it often fails quietly. One pattern is “tool-first adoption.” A scanner gets plugged into the pipeline with default settings. Nobody owns tuning it or responding to its findings. Builds fail randomly, developers learn to ignore those failures, and exceptions become routine. After a while, everyone knows the tool is noisy and unreliable. It is technically present in the pipeline, but its output does not change any decisions, which means it is not really contributing to security at all.

Another failure mode is shallow integration. Security jobs are present in the pipeline on paper, but they are optional, disabled in practice, or easily bypassed under time pressure. Teams might maintain a second, faster pipeline that skips security checks for “urgent” fixes. They might accept “temporary” exceptions that never expire. On a slide, the organization can say it has DevSecOps. In reality, high-risk changes still reach production with little scrutiny, and the guardrails the pipeline is supposed to enforce are not truly binding.

Healthy DevSecOps in CI/CD pipelines feels different day to day. A pipeline that fails for a security reason is not an emergency; it is an expected event that people know how to handle. Teams can explain, in plain language, what each security stage in the pipeline does and why it is there. When they need an exception, it is rare, documented, time-limited, and clearly owned. Over time, you see fewer incidents tied to obvious mistakes like exposed secrets or stale dependencies, because those issues are caught long before deployment.

There are a few positive signals that show this approach is genuinely taking root. Security findings appear right beside unit test failures in pull requests, and people act on them without a security team nudging them each time. Developers open pull requests specifically to fix security issues they noticed earlier, not just because a policy told them to. New projects start from pipeline templates that already include security stages, rather than bolting them on after the first incident. In conversations, people talk about “our guardrails” rather than “security’s gate,” which is a small language shift that reveals shared ownership.

At its heart, DevSecOps in CI/CD pipelines is about treating security as an ordinary part of how code moves, rather than as a separate, last-minute hurdle. It lives in the same version control systems, build jobs, deployment stages, and monitoring tools that your teams already use, and it tries to make security checks feel as routine as running tests. For a working security or IT professional, the most useful question is simple. When you trace the route a change takes from a developer’s keyboard to production, where does security already live, where is it completely absent, and what is one concrete step you can add without breaking delivery?

If you can answer that question honestly and then act on it, you are already moving away from buzzword DevSecOps toward a pipeline that quietly and reliably reduces risk every time it runs. That is the real promise of DevSecOps in CI/CD pipelines: not a new label, but a more predictable, more transparent way to bring security along for every release instead of trying to catch up afterward.