GitHub AI Scan for Pull Requests Is Generally Available: What It Checks
AI Scan complements languages and frameworks not currently covered by CodeQL, but its findings are advisory and cannot automatically fail checks or block merges.
Key takeaways
GitHub made AI Scan for pull requests generally available on October 15, 2026. When code scanning and the repository's effective AI Scan setting are enabled, it automatically reviews pull request changes and complements CodeQL for languages and frameworks that CodeQL does not currently cover, including PHP, Shell or Bash, Terraform HCL, Dockerfiles, Java JSP, and C# Blazor. Findings appear beside CodeQL results with an AI indicator and may include a risk explanation and suggested fix. They remain advisory: they do not fail the code scanning check and cannot block a merge through rulesets. This article explains repository eligibility, excluded fork and Dependabot pull requests, Code Security AI Credit billing, and a human-review workflow for confirming reachability, impact, tests, and residual risk before accepting a suggestion.
Direct answer
AI Scan adds an AI security review to pull requests, especially for languages and configuration formats not currently covered by CodeQL. It is neither an automatic approval nor an automatic gate: findings do not fail the check or block merging, so teams must reproduce the risk, review the fix, and run existing tests.
What this means for everyday users
Record repository type, entitlement, AI Scan setting, credits, reachability, fix review, test evidence, human decisions, and excluded pull-request sources.
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Summary
AI Scan works best as an additional signal layer, not a new automatic judge. Converting findings into reproducible evidence, minimal fixes, and human acceptance is what actually reduces code risk.
Sources
FAQ
What is this ENHE AI article about?
GitHub made AI Scan for pull requests generally available on October 15, 2026. When code scanning and the repository's effective AI Scan setting are enabled, it automatically reviews pull request changes and complements CodeQL for languages and frameworks that CodeQL does not currently cover, including PHP, Shell or Bash, Terraform HCL, Dockerfiles, Java JSP, and C# Blazor. Findings appear beside CodeQL results with an AI indicator and may include a risk explanation and suggested fix. They remain advisory: they do not fail the code scanning check and cannot block a merge through rulesets. This article explains repository eligibility, excluded fork and Dependabot pull requests, Code Security AI Credit billing, and a human-review workflow for confirming reachability, impact, tests, and residual risk before accepting a suggestion.
Why is this AI update worth watching?
AI Scan for pull requests is generally available. It complements current CodeQL coverage gaps. Findings are advisory and cannot automatically block merges. Usage consumes Code Security AI Credits.
What does it mean for everyday AI users?
Record repository type, entitlement, AI Scan setting, credits, reachability, fix review, test evidence, human decisions, and excluded pull-request sources.
Where can readers continue learning on ENHE AI?
Readers can continue with ENHE AI software apps, AI skill tutorials, and AI account service guidance to turn the news signal into practical action.



