Summary
On October 13, 2026, GitHub added AI Scan metrics to security overview, letting organization and enterprise admins track how many AI Scan findings are created and resolved on pull requests, remediation rates, which repositories produce findings, trends over time, and how often developers apply autofix suggestions. The CodeQL pull requests page is renamed Code scanning on pull requests with separate CodeQL and AI Scan tabs.
What changed
Administrators can now observe the impact of AI Scan for pull requests in security overview: counts of findings created and resolved, remediation rate, repositories producing findings, trends over a selected period, and autofix adoption. The CodeQL pull requests page becomes Code scanning on pull requests, split into CodeQL and AI Scan tabs. AI Scan is in public preview on github.com for GitHub Code Security / Advanced Security customers and is not supported on GitHub Enterprise Server.
Why it matters
Giving security leaders adoption and remediation metrics for an AI-driven scanner lets them measure whether AI code-security findings actually get fixed, the governance signal needed to justify rolling AI security review out across an organization.
Evidence excerpt
"Organization and enterprise administrators can now observe the impact of AI Scan for pull requests in security overview." Metrics cover findings created and resolved, remediation rate, repositories, trends, and autofix adoption; the CodeQL pull requests page is renamed to Code scanning on pull requests with CodeQL and AI Scan tabs.