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The talk argues that securing AI workloads is largely an extension of Kubernetes security, and it covers AI-specific risks: non-deterministic agent behavior, GPU supply chain exposure, shared compute, and defenses such as prompt firewalling and agent identity. For AI readers, the useful point is that production AI risk sits in the infrastructure layer as much as in the models, so teams running agents or large GPU clusters should treat orchestration and identity controls as core safety work. The description is a conference-talk summary, so its claims are the speaker's perspective rather than measured findings.
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Presentation: Exploding GPUs

InfoQ36 min de cititArticolul și perspectiva sunt în engleză.

Andrew Martin discusses why AI security is fundamentally tied to Kubernetes security. He shares insights into the evolving AI threat landscape, covering non-deterministic agent behavior, GPU supply chain vulnerabilities, shared compute risks, and practical defense strategies - from prompt firewalling to agentic identity - to help secure cloud-native AI workloads in production environments. By Andrew Martin

Andrew Martin: The pithy version of this title, Exploding GPUs, is the hook. The issue is the AI Infrastructure Challenge. I'm here to convince you that AI security is still Kubernetes security, and then make a handful of caveats that may explode into an armful or a bucketful. I'm Andy, founder and CEO of ControlPlane. We are cloud-native security engineering. For the last eight or so years, almost nine years, we have been working with regulated industries to secure next generation technologies. Ten years ago, that was taking Docker onto the public cloud. Then it became Kubernetes. The first regulated system we deployed was 1.2, which you may remember is no ABAC, no RBAC, no nothing. Since then, I've had the great pleasure of writing and training for O'Reilly, for SANS, and various other things for various other organizations. Securing the world's cloud-native infrastructure, focusing on regulated organizations with square peg round hole solutions for which there is generally no silver bullet. As you'll be pleased to know, this talk does not preach those things. What will we look at? What are the challenges we see in AI security? How do these differ from what we have in Kubernetes fundamentally?…

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