To add GitHub code scanning to a repository you already maintain, open the repository’s Security tab, choose Code scanning, and enable a setup GitHub offers for your code. For eligible repositories, default setup is the low-maintenance starting point: GitHub creates and runs CodeQL analysis for supported languages. Let the first scan finish, then review each alert in context. Start with confirmed high-severity findings, dismiss only alerts you have investigated, and make the fix part of normal pull-request work.
Know what code scanning does—and what it does not
Code scanning looks through your repository’s code for patterns that may indicate a security problem, such as unsafe data handling or a vulnerable coding practice. GitHub presents results as code scanning alerts, usually with a severity, a description, and a path to the relevant code. It is useful on an established project because it gives you a repeatable review surface instead of relying only on memory during a release.
It is not a guarantee that the application is secure. A scanner can miss a problem, and an alert can be a false positive in the context of your application. It also is not the same as secret scanning (for exposed credentials) or Dependabot alerts (for vulnerable dependencies). GitHub’s code scanning overview explains those boundaries and the alert workflow.
Before you enable anything, make sure you have the repository permissions needed to manage security features. Availability can also depend on the repository’s visibility, its languages, and your organization’s GitHub plan. If a control described below is not visible, check your organization’s security settings or GitHub’s current plan documentation instead of trying to work around it.
Start with default setup when it is available
For many existing repositories, default setup is the sensible first choice. It is designed to keep CodeQL analysis running without asking you to write a workflow from scratch. In the repository, go to Security (or Security & analysis, depending on the interface), find Code scanning, and select the option to set it up. GitHub will show which languages it can analyze and the setup it plans to use.
Read that summary before confirming. Check that the important application language is listed and that the repository’s default branch is the branch you expect. Default setup may be enough for a straightforward app, library, or service. If your project needs a custom build, unusual generated code, specific query suites, or a tightly controlled schedule, choose an advanced CodeQL workflow later. GitHub’s guide to configuring default setup is the best current reference because labels and eligibility change.
Do not turn it on and immediately judge it by the first few minutes. The initial analysis needs a workflow run. Open the Actions tab to see whether it started and whether a build or configuration problem needs attention. Once the run completes, return to Security and open Code scanning to see the alerts.
Read the first alerts before changing code
The first scan of a long-lived repository can feel noisy. Resist the urge to close everything just to get to zero. Open one alert at a time and answer four questions:
- Is the affected code reachable in the shipped application or a relevant supported environment?
- What input reaches the flagged line, and is it actually controlled or validated?
- Does the alert describe a real risk for this language and framework?
- Is there a safe, testable change that removes the risky behavior?
Use GitHub’s alert details as a starting point, not a verdict. The path and code snippet show where the analysis found a pattern; your codebase explains whether it matters. Look at nearby validation, call sites, tests, and deployment configuration. If you need a second opinion, have a teammate review the proposed change rather than treating a generated explanation as proof.
For a small team, a simple rule works well: investigate confirmed high-severity alerts first, then address medium-severity findings that are exposed or easy to fix. Keep lower-risk items visible in a backlog with an owner or review date. This turns a one-time cleanup into useful maintenance without freezing feature work.
Fix, test, and use a precise dismissal reason
When an alert is real, make the smallest change that addresses its cause, add or update a test where it makes sense, and open a pull request as usual. The next analysis run should help confirm whether the finding is gone. Keep the pull request focused: a security fix is much easier to review when it is not mixed with a large refactor.
Sometimes an alert is not actionable. Perhaps the code is test-only, the input cannot reach the line in your deployment, or the finding is a known false positive. In that case, dismiss it with the most accurate reason GitHub offers and leave a short explanation that a future maintainer can understand. “Not relevant” without context is an invitation for someone to repeat the investigation later. Never dismiss an alert just because the suggested fix is inconvenient.
If you use an AI tool to draft a remediation note or test idea, verify it against your code and the official documentation. The same habit applies to everyday planning: our guide to turning a spoken idea into an action plan shows why corrections and constraints matter before you act on generated output.
Make alert review part of the repository routine
After the initial pass, assign a lightweight rhythm. Review new code scanning alerts when a pull request is opened or during a weekly maintenance block. Keep an eye on failed scanning workflows too; a quiet dashboard is not reassuring if analysis has stopped running. If a change in languages, build tooling, or repository structure makes results incomplete, revisit the setup rather than assuming it still covers the project.
Document three decisions in the repository’s contributing notes or team wiki: who watches new alerts, how quickly high-severity findings are reviewed, and what evidence is needed before an alert is dismissed. That is enough process for many small projects. It resembles any good safety checklist: clear actions beat a heroic last-minute scramble. For an example outside software, see our practical home fire escape plan, which also centers on a short plan people can follow under pressure.
Code scanning earns its place when it helps your team notice and fix real issues earlier. Enable the simplest supported setup, learn from the first batch instead of gaming the count, and keep the review habit alive with every change.