The all-in-one GitHub
Advanced Security Alternative
As security shifts left, choosing the right SAST tool becomes critical. GHAS offers built-in code scanning with rule-based detection, but Corgea takes it further with next-gen AI—cutting false positives and improving detection accuracy.
Feature Comparison
GHAS
Corgea
SAST Scanning
Static Rules
AI-Native
Business & Code Logic Scanning
X
✔
Broken Auth Scanning
X
✔
Malicious Code Scanning
X
✔
Secrets Scanning
✔
✔
False Positive Rate
>30%
<5%
AI-powered False Positive Detection
X
✔
Auto-fixes
Accuracy Not Reported
+90%
Custom policies in natural language
X
✔
Advanced Blocking Rules
✔
✔
Automated SLA Management
X
✔
Developer Tools (IDE Extensions, CLI)
X
✔
API Access
✔
✔
Industry’s Lowest
False Positives Rates
Corgea uses LLMs to understand code contextually, catching complex issues like business logic flaws with <5% false positives across 20+ languages.
GHAS (CodeQL) is a query-based engine. While great for known vulnerability patterns, it requires expert tuning to reduce false positives, especially for custom business logic or less common languages.
AI Powered Policies
Without the Complexity
Corgea lets teams define policies in plain English with PolicyIQ—no custom rule writing needed. Business risk is baked into every scan.
GHAS policies require writing CodeQL queries or using prebuilt queries. Customization is possible but needs deep CodeQL knowledge, making it less approachable for most teams.
Auto Fixes
That Actually Work
Corgea integrates into PRs and CI/CD with AI-generated patches and minimal false positives, boosting dev velocity.
S
GHAS does not offer SAST autofixes today. Its focus is on detection, not automated remediation. Teams typically need to manually review and fix issues.
Testimonial
This is groundbreaking stuff that everyone should be paying attention to!
James Berthoty
Analyst @ Latio Tech
The all-in-one GitHub
Advanced Security Alternative
As security shifts left, choosing the right SAST tool becomes critical. GHAS offers built-in code scanning with rule-based detection, but Corgea takes it further with next-gen AI—cutting false positives and improving detection accuracy.
Industry’s Lowest
False Positives Rates
Corgea uses LLMs to understand code contextually, catching complex issues like business logic flaws with <5% false positives across 20+ languages.
GHAS (CodeQL) is a query-based engine. While great for known vulnerability patterns, it requires expert tuning to reduce false positives, especially for custom business logic or less common languages.
AI Powered Policies
Without the Complexity
Corgea lets teams define policies in plain English with PolicyIQ—no custom rule writing needed. Business risk is baked into every scan.
GHAS policies require writing CodeQL queries or using prebuilt queries. Customization is possible but needs deep CodeQL knowledge, making it less approachable for most teams.
Auto Fixes
That Actually Work
Corgea integrates into PRs and CI/CD with AI-generated patches and minimal false positives, boosting dev velocity.
GHAS does not offer SAST autofixes today. Its focus is on detection, not automated remediation. Teams typically need to manually review and fix issues.