Hello listeners. Today we are dropping another episode in our series, the AI Control Loop,

how enterprises govern the AI they've already deployed, sponsored by our friends at Wallarm.

Wallarm is the AI control platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CSOs the

governance they need and CIOs the speed they demand.

Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps using

patented AIML-based threat detection and blocking that operates at production speeds.

In his follow-up appearance on the Code Story podcast, Tim Ebbers, field CTO at Wallarm,

discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks

like at the runtime level, and what accountability becomes possible once all four stages are in place.

Tim, welcome back to the show.

Excited to have you back on a follow-up episode on Code Store.

Thanks again for being on the show.

I appreciate being here.

It was fun last time and looking for more fun this time.

Absolutely. We're going to have a ball. So today's episode is entitled, Detection is Not Enough.

And we're going to talk about how detection alone is insufficient for AI-driven systems.

And jumping around into it, security teams are used to detecting incidents and responding after the fact.

Why is that model insufficient for AI-driven

systems? I think as you look at how fast AI is developing not only on the bad actor side,

but also on the code side, we're putting out a whole lot more code, and AI can, on the bad actor's

side, can go through that, those endpoints so much faster than they can today when it was all human done.

Sure, we had automation before, and they could have lots of IPs hitting an endpoint.

...