Every detector descends from someone's bad day.

Neural learning. Not a buzzword.

Traditional security tools have a static rulebook. A human writes a rule, deploys it, and it never changes. The tool checks the same patterns forever, missing anything that wasn't anticipated on day one.

Our engine works differently. Every scan feeds back into the corpus. Every new exploit we discover — through simulation, back-testing, or live incident ingestion — creates a new data point. The detector network adjusts its weights. New edges form. Old, weak connections fade.

Think of it like a biological immune system. The first time you encounter a pathogen, it takes a moment to respond. The next time the same attacker shows up wearing a different coat, the system recognises the pattern instantly — because it changed the last time it saw it.

This is how AuditHunt finds vulnerabilities that don't exist in any database yet. The engine doesn't wait for the exploit to be named. It finds the pattern first.