More resolved security findings sounds encouraging. But does it mean the code is safer? A new GitHub reporting update makes that question worth asking—and makes careful interpretation more important than a reassuring headline number.
What GitHub announced
On October 13, 2026, GitHub announced AI Scan metrics in security overview. According to the supplied changelog excerpt, organization and enterprise administrators can now observe AI Scan activity for pull requests through metrics that include how many findings were generated and resolved.
The excerpt is truncated, so it does not establish the complete metric list or explain precisely how GitHub classifies a finding as resolved. The supported news is narrower: administrators have new visibility into generated and resolved findings. It is not evidence of a measured improvement in software security.
Understanding the terms
A pull request proposes code changes for review before they are incorporated into a project. A scanning finding flags a potential issue for investigation; it should not automatically be treated as a confirmed vulnerability.
For readers interpreting the dashboard, generated findings describe reported output from scanning. Resolved findings describe findings counted as resolved by the reporting system. Without the metric definitions, avoid assuming that every resolution represents a particular kind of code repair or verification process.
That distinction matters. A dashboard summarizes records; investigating the underlying code answers a different question: was the problem understood and adequately addressed?
Analysis: turn counts into useful questions
The following recommendations are practical analysis, not additional capabilities claimed in GitHub’s announcement.
**Compare like with like.** Before comparing two periods, check whether the repository scope and time windows are equivalent. Record relevant changes in scanning coverage or development activity. A higher finding count could reflect a different amount of inspected work rather than a deterioration in code quality. A lower count, by itself, cannot establish an improvement.
**Inspect representative resolutions.** Select examples and trace each from the original finding to the relevant change and review evidence. Ask what the finding identified, what changed, and which checks support the conclusion that the underlying issue was addressed. Do not substitute a resolved label for that investigation.
**Assign follow-up ownership.** Decide who investigates findings that remain open and who reviews uncertain resolutions. A useful team discussion should end with specific next steps, not merely a comparison of totals. If a finding needs further investigation, identify the responsible reviewer and the evidence needed to close that question.
**Explain uncertainty when sharing results.** A report can state that more findings were counted as resolved while separately describing what was actually verified. Keep those statements distinct, especially when communicating with people who do not work directly with the code.
The practical takeaway
The announcement provides a starting point for examining AI Scan findings, beyond simply asking whether scanning is enabled. Its value should be assessed through the investigations and decisions it supports.
Use the new counts to identify questions, prioritize follow-up, and organize review. Reserve claims about successful repairs or reduced security risk for conclusions supported by code-level evidence. More visibility can support better judgment; it does not replace it.