🔐 TruffleHog Secret Scanner Automation

Short summary

Case study: automating secrets scanning across 115 repositories using TruffleHog, Bash, jq, and Gemini for report generation. The workflow identifies exposed secrets, separates active (verified) from inactive (unverified) findings, masks sensitive values, and generates standardized JSON outputs. Lessons learned: automation is essential at scale, structured output separation accelerates remediation, and jq makes complex filtering manageable.

• •Automated secrets scanning across 115 repos using TruffleHog and Bash scripting with jq filtering
• •Classifies findings as active/inactive and masks sensitive data for improved prioritization
• •Demonstrates that automation is essential beyond a dozen repositories; standardized outputs accelerate remediation

Generated with AI, which can make mistakes.