The AppSec industry has spent decades in a hard conflict between coverage and precision in threat detection. Classic SAST‑tools generate noise that takes more time to sort through manually than actual threat work. The release of Claude Code Security by Anthropic shook the cybersecurity industry: traditional vendor capitalizations dropped, and the CEO of major player Snyk declared the company’s future must be defined by an AI‑-centric leader.
The market has redefined what makes a security tool valuable. Previously, value was measured in supported rules and languages. Today the formula has changed: what matters is the chain — find, explain, help fix. This is where LLMs enter the stage — not as a replacement for classic analyzers, but as an additional interpretation layer. This is how a new category forms: AI SAST.
This article covers how LLMs work with code, why “feeding a repo into a prompt” is a bad idea, which engineering metrics actually matter, and how we research and implement autonomous defect discovery and remediation capabilities for SourceCraft Security products.
