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When you ship a software update, a code commit usually starts a sequence of automated checks and controlled handoffs—not an immediate trip to production. CI builds the change and runs quick tests; later stages may test and secure the build, approve a release, deploy it gradually or all at once, and monitor how it behaves. A green CI result means the change passed the checks that ran, not that it has been released or proven defect-free.
What happens after a code commit?
A commit records a change to version-controlled source code or configuration. In a typical delivery pipeline, automation responds to that change and moves a build through stages. Teams vary the exact sequence, checks, and approval rules according to their system and risk.
- Integrate and build: CI retrieves the change, combines it with the codebase, and compiles or otherwise transforms it. It resolves dependencies and creates a deployable artifact.
- Run initial checks: Quick automated tests and other configured checks provide early feedback. If a required check fails, the pipeline may stop and block promotion until the issue is resolved.
- Test and assess the artifact: The build can move through more extensive functional, security, and operational checks. The goal is to find problems before release, not to prove that none exist.
- Prepare and authorize a release: Teams can record changes, gather evidence that required checks passed, publish the artifact to an approved repository, and confirm readiness. A human approval or scheduled release window may still be required.
- Deploy and verify: The selected artifact is installed and configured in production, then checked to confirm the deployment completed as intended.
- Monitor and respond: Teams observe service health and user-facing behavior, and use their response plan if the update causes problems.
These stages are part of the software supply chain. NIST describes CI/CD pipelines as taking software through stages such as build, test, package, and deploy in its SP 800-204D, published February 12, 2024.
What does CI passing—and “CI/CD”—actually mean?
Continuous integration (CI) is the practice of integrating changes frequently and using automation to build and test them. A successful CI run says that the change passed the checks configured at that point. It does not, by itself, mean that the change is approved, released, or running for users. DORA recommends keeping changes small and self-contained and branches short-lived so integration problems can be found sooner (DORA: Continuous integration).
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Continuous delivery means keeping software in a state where it can be released on demand. Continuous deployment goes further: released artifacts are deployed to production automatically. Organizations use “CI/CD” differently, so the label alone does not tell you whether a commit goes live without approval. A commit may pass CI and still wait for additional tests, a release decision, a maintenance window, or authorization (DORA: Continuous delivery; NIST SP 800-204D).
What gets tested and checked before release?
The pipeline’s exact checks depend on the application, its users, and the consequences of failure. They may include:
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- Functional checks: unit and integration tests, regression tests, smoke tests, and acceptance tests that exercise expected behavior at different levels.
- Security checks: static or dynamic analysis, dependency and vulnerability scanning, secret scanning, infrastructure-as-code checks, and fuzz testing.
- Release controls: evidence that required checks passed, confirmation that the artifact is approved, and readiness or authorization checks.
Tests provide evidence only for the behavior and conditions they cover; passing them cannot guarantee that production will be free of defects. A failing check may block promotion under the team’s release policy. NIST’s Notional Reference Model for DevSecOps describes security testing, artifact controls, and release-management activities as parts of the delivery lifecycle.
Why promote the same artifact between environments?
An artifact is the packaged output of a build—the files or image intended for deployment. A reliable pipeline records and promotes a known artifact through later stages rather than silently rebuilding different bits for testing and production. Rebuilding can introduce differences in dependencies, configuration, or build inputs, making it harder to know what was actually tested.
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DORA’s CI guidance calls for authoritative, numbered, repeatable build packages (DORA: Continuous integration). NIST’s model includes artifact signing and verification, as well as provenance generation and verification (NIST Notional Reference Model for DevSecOps).
How does a team choose a production rollout?
A rollout strategy determines how the new version reaches production capacity or users. The appropriate choice depends on the system’s architecture, risk, and ability to operate the release—not on a universally best method.
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| Strategy | How exposure changes | Operational consideration |
|---|---|---|
| Rolling | Instances or capacity are updated in batches, so old and new versions may coexist during the rollout. | Monitor each stage and be prepared to halt or reverse the rollout if health degrades. |
| Blue/green | Two production environments are maintained; traffic is directed to the new environment when it is ready. | Requires the ability to operate both environments and manage the traffic switch. |
| Canary | A limited portion of traffic or users receives the new version before wider promotion. | Define which health or user-impact signals pause promotion, and ensure the limited exposure is meaningful for the system. |
NIST’s reference model names rolling and blue/green deployment approaches and lists canary in deployment management (NIST Notional Reference Model for DevSecOps). Whichever strategy is used, monitoring is still necessary: a controlled rollout limits or structures exposure but cannot guarantee that a change will work as expected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens if the update breaks production?
Teams monitor service health, performance, security, and user-facing behavior after deployment. If signals indicate a problem, responders may stop further rollout, restore traffic to a previous version, or use another recovery procedure. Whether rollback is safe or straightforward depends on the system and the change; a plan should explain what to do, who acts, and how to verify recovery.
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Database changes need particular care. DORA recommends treating schema changes as version-controlled scripts and making them visible across the delivery lifecycle (DORA: Continuous delivery). Reverting application code does not necessarily reverse a database migration or undo data changes, so teams need a recovery approach suited to the migration as well as the application release.
How can you tell what produced a deployed artifact?
Provenance records information about an artifact’s origin: what entity built it, what process was used, and what inputs went into the build. SLSA describes increasing levels of build trustworthiness and protection against tampering. In its version 1.0-rc2 specification, L1 provenance can help identify the source version and build process; L2 uses a hosted build service that generates and signs provenance (SLSA security levels, version 1.0-rc2).
Provenance is useful only as part of a broader assurance process. Its presence does not prove that software is safe: teams must verify the provenance and consider the trustworthiness of the build process and inputs. Not every organization implements SLSA or the same supply-chain controls; NIST’s reference model describes provenance and artifact verification as available DevSecOps practices (NIST Notional Reference Model for DevSecOps).
What is the point of shipping updates more often?
The objective is to make changes smaller, easier to assess, and safer to release—not to maximize deployment frequency at any cost. DORA cautions: “Increasing the frequency of deployments without improving processes and architecture is likely to lead to higher failure rates and burned out teams.” (DORA: Continuous delivery)
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