A team can add security analysis to a pull request and still need to decide who will act on the result. AI Scan makes that separation explicit. On October 15, GitHub announced general availability of the feature, which complements CodeQL in languages and frameworks without equivalent coverage, including PHP, Shell and Terraform.

Its findings are advisory: they do not fail the code scanning check or block merges through ruleset requirements. A permitted merge does not establish that every security signal has been resolved. That distinction should reach the people reviewing code before an administrator enables the feature.

AI Scan adds review signals without becoming an automatic gate

The feature must be enabled alongside code scanning. Findings carry an AI identifier in the pull request and may include an explanation and proposed fix. Pull requests from forks and those created by Dependabot are excluded.

MnzAI Labs' assessment is that the potential benefit lies in surfacing questions a reviewer might otherwise miss. Consider an infrastructure change combining environment variables, shell commands and deployment configuration. Even if the main application uses another language, that change could create an exposure path worth investigating.

This is an illustrative review scenario, not a vulnerability demonstrated by the release. The team still needs to establish whether an input can reach the suspicious operation, whether another layer already constrains it and what the proposed fix would change. A persuasive model explanation cannot establish those facts by itself.

The official documentation limits the analysis to pull requests: it does not scan the entire repository or convert findings into security backlog alerts. It also acknowledges false positives. Those boundaries matter when interpreting both a reported issue and a screen with no findings.

Coverage and billing belong in the enablement decision

Eligibility depends on repository ownership and visibility. Public repositories qualify; private and internal organization repositories require Code Security or Advanced Security. A Copilot license is not required. Usage consumes Code Security AI Credits.

Billing has a practical coverage consequence. The documentation says a hard budget with no available credits can prevent new findings. A team needs to distinguish a scan that found no issues from scanning that stopped because its budget ran out.

A pilot should therefore track costs and coverage alongside result quality. Counting comments can reward noise. Examining which findings led to justified changes provides a better way to judge whether the tool is creating a useful security practice or another inbox.

Every finding needs a next step

MnzAI Labs' editorial recommendations for getting started are:

  • Assign ownership: decide who investigates a finding and when that investigation becomes a condition for approving the change.
  • Keep the reasoning: record why the finding was fixed or dismissed, supported by code and test evidence.
  • Watch the gaps: track excluded pull requests and coverage interruptions caused by configuration or budgets.

Agentic Autofix serves another stage by preparing proposed fixes for alerts. It should not be confused with AI Scan, nor should an advisory finding become permission to change code without review.

For an engineering leader, the useful question extends beyond whether scanning is enabled. It includes who receives the result, how they investigate it and what evidence supports integrating the change.