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 today's episode, Craig Thomas, Senior Solutions Engineer Wallarm, returns to the show to dive into why

runtime behavior is the critical blind spot, and what CSO should demand if they want to move from policy to control.

Craig, welcome back to the show. Thank you for being in a return episode on Code Story.

Yeah, great to be back. I know last time we talked about rogue AI, so excited to dive in deeper about the threats and how to solve some of the AI problems today. Absolutely.

Really digging and really enjoying this series we're putting together here. The title for today is what's missing in AI security today. We're going to dive into runtime behavior, being a critical

blind spot, and what CSO should demand. And let's dive right into it. So security teams

are used to detecting incidents and responding after the fact, right? We've covered that quite a bit

in this series. Why is that model becoming insufficient for AI-driven systems? Yeah, great question,

Noah. First of all, just kind of like the frame this whole discussion, right, that traditional security really was built to answer one question. Who gets access? Firewall rules, identity access policies, network segmentation, all that's fundamentally controlling the gate. But AI has to move that problem past the gate. And so you asked about detect and respond to systems.

Traditional IRB assumes a human attacker moving at human speed.

You have time to detect, triage, contain that attack.

But as we talked about before in some of the other episodes,

AI compresses that timeline dramatically.

Model can XFiltrate, manipulate corrupt data in milliseconds versus algorithms. some of the other episodes, AI compresses that timeline dramatically. Model connects will trade,

manipulate corrupt data in milliseconds versus hours, days, or even weeks. And so this incident with

AI may not look like an incident. Looks like normal API calls, normal outputs, and traffic.

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