Aaron Levie, Steven Sinofsky & Martin Casado: How Do You Secure a World of AI Agents?
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What historical lessons do we need to learn to secure AI agents?
If you regulate AI too early, you actually don't solve anything. You still just kind of have the same risk, ultimately. You
willed the thing into being, but you haven't figured out
how to control it.
The problem we have now is this rift between the labs and the security community that keeps coming to two conclusions. Sloppy. And
you're not complete in what you're telling us happened. An employee is like 10% chance of species extinction. The post is very reasonable, but the atmospherics are not.
Agent swarms completely flip that. These are just roaming, you know, drones. But like, time 10,000, and they will easily mistake a good task for a bad one. So now we need a whole layer
internally that just is tracking way more about what authentications are being done, what APIs are being done. The U.S., about 15 years ago, stopped leading in tech antitrust. The problem is that Europe is going to lead with that because they have nothing to lose. This could change the nature of software fundamentally. The center of innovation has just moved. This is the signal that...
The early internet was riddled with viruses, worms, and security failures. We didn't stop building it. We learned how to make it safer. What should AI take from that history? In this episode, I sit down with Aaron Levy, Steven Sanofsky, and Martim Casado to debate AI safety, regulation, and what changes when agents start operating across the software we use every day. We get into why agents could force a rethink of permissions and cybersecurity, what decades of software security can teach today's AI labs, and why regulating a technology before we understand how it actually fails can create problems of its own. And we look at a broader shift already underway. As models mature, some of the most important AI innovation may increasingly happen outside the frontier labs, in the systems and software built around them.
Guys, welcome back to the podcast.
Thank you. Didn't think we'd ever do this again. I
can't believe it. This
is great. I mean, Martine's just building these $100 billion companies too busy for this podcast.
Or at least taking credit for
it, as VCs do. Exactly. We have a lot to discuss today, but Aaron, why don't we start with you? Pacing the frontier. How have you reacted and reflected on what's happened there and just the discourse that's followed? Oh
boy, I think we should start with Martina on this one. You were fighting lots of good ground wars. Maybe I'll say one thing that we probably all agree with and then we can figure out where we maybe kind of fracture off. I think we would agree that any AI lab right now at the frontier should be building in the safest way possible with the highest degree of governance and security and whatever your definition of alignment is. This is an incredibly important area of research. It's an incredibly important area for the diffusion of AI. You're not going to have AI diffusion without extremely high quality products that can be trusted by enterprises and that aren't kind of constantly hacking systems. So when at least I read the Dario post, I actually didn't disagree with almost anything because it was all about how do you have better security of these systems, sandboxing, better testing.
There's going to be some debates around the embedded nature of the testers and do you agree with who those are and does the industry all align on that? But I think actually all of the major points were probably salient and appropriate. Then the only question is, does this get sort of use or leveraged to do things that maybe we don't agree with, which would be like a slowdown of AI dramatically because of regulatory controls that would not make it easy to compete with the frontier labs or do politicians end up taking the message and run with it and maybe even worse outcomes happen. It's used to ban data centers far faster and whatnot. I think the actual substance of the topic is actually incredibly important and I think very important for AI advancement in general.
Then the question is, what do you do about it?
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Chapters
8 chapters
1
What historical lessons do we need to learn to secure AI agents?
0:00–5:14
2
How did early internet viruses and aviation safety shape today’s AI risk thinking?
5:14–11:50
3
Why do AI agents force a rethink of permissions, authentication, and the security stack?
11:50–18:49
4
What is the difference between “pacing” AI development and a full pause, and why does it matter?
18:49–25:53
5
How should regulators address the novel cybersecurity risks introduced by AI swarms?
25:53–33:10
6
What new access‑control models are needed for AI‑driven software and services?
33:10–41:12
7
How will probabilistic programming and stochastic software change the future of AI integration?
41:12–48:41
8
What are the key takeaways and next steps for safely scaling AI agents in the real world?
48:41–55:50
Speakers
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