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

Ready to upgrade with a click?

Harden your software in less than 10 mins

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.