A CI/CD pipeline is the automated process that carries code from commit to production — building, testing and deploying without manual intervention. In 2026, the gap between teams that ship multiple times a day and teams that ship once a month comes down to exactly how mature this pipeline is.
Quick Overview
What you’ll learn:
- How to choose a CI/CD tool that fits your project’s scale
- What stages a mature CI/CD pipeline is built from
- How to secure a pipeline against the most common attack vectors
- The mistakes that most often delay a first rollout
Who this article is for:
- DevOps teams building their first pipeline
- Developers moving from manual deployments to automation
- Tech leads planning a CI/CD tooling migration
Reading time: 6 minutes
Building a CI/CD pipeline from scratch: choosing a tool
Choosing a CI/CD tool depends mostly on where your code already lives. GitHub Actions has become the default choice for GitHub-hosted projects in recent years — according to GitHub’s annual Octoverse report, it is one of the platform’s most intensively developed features, deeply integrated with the repository and the community action ecosystem. Jenkins, despite being one of the oldest tools in this space, remains popular in enterprise environments thanks to its enormous plugin ecosystem and full control over self-hosted infrastructure. GitLab CI wins where a team wants its repository, CI/CD and project management in a single tool.
For a new team without existing technical debt, the most common choice in 2026 is a tool integrated with the code hosting platform (GitHub Actions or GitLab CI) — less infrastructure to maintain, faster start, integration with a permissions system the team already knows.
Stages of a mature CI/CD pipeline
Regardless of the tool chosen, a mature pipeline goes through the same stages:
- Build — compiling or building an artefact (container image, package), with dependency caching to speed up subsequent runs.
- Unit tests and static analysis — fast tests run on every commit, linting and static application security testing (SAST).
- Integration tests — verifying components work together, often on a dedicated ephemeral environment.
- Dependency scanning (SCA) — detecting known vulnerabilities in third-party libraries before they reach the production image.
- Staging deployment — automatic deployment to a pre-production environment, often with automated smoke tests.
- Production deployment — with an acceptance gate (manual or automatic) and a deployment strategy (blue-green, canary, rolling update).
According to the State of DevOps Report, teams classified as “elite performers” deploy changes on demand, multiple times a day, with a very short lead time for changes — a direct effect of a pipeline that automates every stage above without manual gates beyond clearly defined acceptance points.
CI/CD pipeline security
A CI/CD pipeline has access to secrets (API keys, cloud credentials) and permissions to deploy code to production — that makes the pipeline itself an attack target. The core security rules: secrets stored in a dedicated manager (GitHub Secrets, HashiCorp Vault), never in repository configuration files; CI token permissions scoped to the minimum required (principle of least privilege); scanning container images and dependencies before every deployment; and separate runners for pull requests from forks, which should never have access to production secrets.
The most common mistakes when building a first pipeline
Four patterns repeat regardless of the tool chosen: no dependency caching, which repeatedly extends build time with no benefit; tests run sequentially instead of in parallel, even though most modern CI tools support matrix builds; treating the pipeline as a one-off project instead of code that requires code review and versioning on par with the application itself; and no clear rollback strategy — a pipeline that can deploy a change but cannot quickly revert it creates a false sense of safety.
Read Also
- Production Kubernetes Cluster Rollout: A Complete Guide — Kubernetes as the target deployment environment for a CI/CD pipeline
- MLOps in Practice: From Jupyter Notebook to Production — how CI/CD principles translate to ML pipelines
Develop Your Skills
Building your own pipeline is best started with the training GitHub Actions and GitHub Advanced Security. Check the programme and sign up to build these skills with EITT experts.
FAQ
GitHub Actions or Jenkins — which should you choose in 2026?
For a new project hosted on GitHub, GitHub Actions is the natural choice — no additional infrastructure to maintain and deep integration with the repository. Jenkins makes sense in enterprise environments with existing self-hosted infrastructure or compliance requirements that SaaS tools cannot meet.
How many stages should a mature CI/CD pipeline have?
At minimum six: build, unit tests with static analysis, integration tests, dependency scanning, staging deployment and production deployment with an acceptance gate — skipping any of these stages shifts risk from automation back onto manual verification.
How do you secure secrets in a CI/CD pipeline?
Secrets should go into a dedicated manager (such as GitHub Actions’ built-in Secrets or an external Vault), never into repository configuration files, and CI tokens should carry permissions scoped to the minimum required to complete the task.
Why should a CI/CD pipeline be treated like application code?
Because changes to the pipeline directly affect what reaches production — without code review and versioning of CI/CD configuration, a bug in the pipeline can ship unverified code just as easily as a bug in the application itself.