GitHub Adds AI Scan Metrics for Findings, Remediation, and Autofix Adoption
Security teams can evaluate AI Scan in organization and enterprise overview, but the preview is limited to github.com and eligible security plans, excluding GitHub Enterprise Server.
Key takeaways
GitHub added security-overview metrics for AI Scan on pull requests on October 13, 2026. Organization and enterprise administrators can now see generated and resolved findings, remediation rate, repositories producing findings, trends over a selected reporting period, and how often developers applied autofix suggestions. The CodeQL pull-request view is renamed Code scanning on pull requests and separates CodeQL and AI Scan into different tabs. The feature is a public preview on github.com, including GitHub Enterprise Cloud, for customers with GitHub Code Security or GitHub Advanced Security; GitHub Enterprise Server is not supported. This article explains how to run a bounded pilot, establish a baseline, sample true and false positives, compare repositories, review autofix rather than trusting it, and decide whether the tool improves real remediation work.
Direct answer
AI Scan can now be evaluated with organization-level metrics instead of individual pull requests alone. Pilot a small set of repositories and review findings, remediation, repository distribution, and autofix adoption together. No single metric proves quality; sample false positives, missed issues, and fix correctness.
What this means for everyday users
Record pilot repositories, baseline, plan, findings and remediation, closing reasons, autofix review, false positives, test results, human cost, and expansion decision.
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Summary
AI Scan metrics make organization-level impact easier to observe, but a dashboard is not a verdict. Expansion should depend on validated fixes, false positives, autofix quality, and human cost together.
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FAQ
What is this ENHE AI article about?
GitHub added security-overview metrics for AI Scan on pull requests on October 13, 2026. Organization and enterprise administrators can now see generated and resolved findings, remediation rate, repositories producing findings, trends over a selected reporting period, and how often developers applied autofix suggestions. The CodeQL pull-request view is renamed Code scanning on pull requests and separates CodeQL and AI Scan into different tabs. The feature is a public preview on github.com, including GitHub Enterprise Cloud, for customers with GitHub Code Security or GitHub Advanced Security; GitHub Enterprise Server is not supported. This article explains how to run a bounded pilot, establish a baseline, sample true and false positives, compare repositories, review autofix rather than trusting it, and decide whether the tool improves real remediation work.
Why is this AI update worth watching?
New metrics cover findings, remediation, and autofix adoption. CodeQL and AI Scan are shown separately. The preview targets eligible github.com customers. Metrics require human sampling and testing.
What does it mean for everyday AI users?
Record pilot repositories, baseline, plan, findings and remediation, closing reasons, autofix review, false positives, test results, human cost, and expansion decision.
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