GitHub’s model for secret detection: Finding more secrets with less noise
GitHub’s context-aware secret classifier improves detection quality, raises weighted acceptance by 11.5%, and reduces median scan execution time by…
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Secret protection must scale with software
Developers aren’t becoming more careless; they’re being outpaced. The tools that let developers create more software should also take on more of the…
How to evaluate LLMs before production
These are the lessons we learned evaluating LLMs for real-world secret scanning. The post How to evaluate LLMs before production appeared first on T…
How GitHub used secret scanning to reach inbox zero
GitHub had 20,000+ secret scanning alerts across 15,000 repositories. Here's how we separated signal from noise, built remediation workflows, and re…
Making secret scanning more trustworthy: Reducing false positives at scale
Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The po…