Everyone is asking whether AI coding tools can replace their security stack. We ran the experiment so you don't have to.

We found that the bottleneck isn't model capability. LLMs are genuinely good at reasoning about security. The bottleneck is proof. There's a significant gap between "the model flagged something suspicious" and "I have a verified, triaged, deduplicated finding I can act on across 200 services." Closing that gap requires an execution layer that most teams are still building and maintaining by hand.

In this webinar, we break down what that execution layer actually looks like: orchestrating multi-step tasks, managing context across long-running loops, building tooling per test type, and handling false positives without burying your team in noise.

We'll show what we learned running Claude Code, Neo, and the most widely used security testing tools against the same targets. If you're evaluating AI security tooling or working through the build vs. buy decision, this one is worth your hour.