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AI Security Validation

How proof, prioritization, remediation, and validation make AI-powered security analysis more useful.

What raw AI security analysis can do well

Modern LLMs can reason locally about code, surface plausible issues, and suggest fixes for well-known patterns.

Where raw AI findings can break down

Volume grows quickly. Many findings lack proof, prioritization, or evidence that the suggested fix actually closes the vulnerable path.

Why LLMs need structured evidence

Cross-file relationships — routes, handlers, permissions, data flows, guards — are easy to miss when a model only sees a window of code.

What Galen© adds to AI security workflows

Galen© maps the security-relevant structure of an application so AI analysis has the evidence it needs to reason about real risk.

From possible issue to validated correction

A finding becomes useful when it includes proof, priority, remediation context, and a validation step after the fix lands.

Raw AI output vs Telhawk / Galen© output

Raw AI output
  • May produce many possible findings
  • May miss relationships across routes, handlers, permissions, and data flows
  • May suggest fixes without validating correction
Telhawk / Galen© output
  • Shows affected code path
  • Shows data flow
  • Identifies missing guard or control
  • Prioritizes the issue
  • Supports remediation
  • Validates whether the fix worked
Compare Raw AI vs Telhawk

See a side-by-side breakdown of raw AI scan output and proof-backed Telhawk findings.

View Comparison
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