Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

I'd like to send a big thank you to our friends at Rubrik for supporting the podcast and sponsoring today's episode. AI agents are transforming how work gets done. They analyze data, trigger workflows, and automate decisions. But that same speed that automates productivity can automate mistakes. Most teams have no visibility into agent behavior. Rubrik changes that. Rubrik agent cloud unifies visibility, control, and recovery so you can unleash agents, not risk. With Rubrik agent cloud, organizations can monitor agent actions in real time, govern behavior with policy based guardrails, and rewind mistakes before they cascade. Learn more at rubrik.com. That's rubrik.com. I recently attended a major enterprise tech conference speaking on a couple of panels about scaling AI agents. Naturally, the topic of risk came up a lot, and the default answer was usually some combination of static guardrails and human approval. In theory, this sounds simple enough. Block the dangerous stuff, and when something looks risky, put a human in the loop. But agents put pressure on both sides of this model. Static rules are hard because agents are creative. They don't just follow a fixed path through software. They plan, improvise, call tools, and find workarounds. And human approval is hard because agents can operate much faster than we can. So the question isn't whether we need guardrails and oversight. Of course, we do. The question is what that should look like when agents are operating at scale across high stakes tools, databases, and workflows. I spoke about this with Dev Rishi, GM of AI at Rubrik, where he and his team are building infrastructure to secure and govern agents in enterprise environments. He shared an example from his personal experience that gets at why this is such a tricky problem. But then we saw some more sophisticated types of things that were going wrong. One thing we noticed was that Cloud Code was really trying to post internal source code to a public repo rather than private. And so we saw this kind of relatively frequently, and there's even instances where, like, if we looked back through the audit logs, we saw, like, the check, check, check. And so technically, it felt that it had gotten the human in the loop approval for it. And we even saw one crazy instance where Cloud Code tried to get around this, like, blocking we were doing of, like, GitHub public gist. And rather than, like, doing this as a text in text out system where it was, like, alright, I'm posting to this URL, it spun up a browser window, and we just started to see mouse clicks on certain coordinates. And we noticed that one of the coordinates actually was for a public gist. I'm Sam Charrington, and this is the Twimble AI podcast. For over a decade, I've been exploring the ideas and innovation shaping the future of AI through conversations like this one that help you understand what's real, what's next, and what matters. …