A blueprint for zero-trust AI on Kubernetes

Are these AI security challenges unique? A blueprint for securing AI on Kubernetes Securing ingress to your endpoints (secure the front door) Control the traffic (Sorry this cluster is RSVP only) Observability (What is actually happening inside the house) The bottom line Posted on October 10, 2025 by Tigera CNCF projects highlighted in this post LLMs and AI are everywhere these days. Everyone wants to build the next big thing, ship it fast, and maybe even cash out and chill for the rest of their lives. The problem? Most open source AI projects are shared as is. They’re created with the best intentions, but their developers aren’t losing sleep over things like security guardrails or production hardening, that part is left for you to figure out. If that sounds like the boat you’re in, you’re in the right place. In this blog, we’ll look at how running AI on Kubernetes introduces some very real networking and security challenges, and what you can do about them before your experiment turns into a liability. Because here’s the thing: AI systems are complicated, sometimes even the people building them admit they don’t fully know why a model makes the decision it does. But at the end of the day, it’s still just bits moving around computers and network cables, and securing those bits, whether they’re API keys, training data, or model endpoints, is easier than you might think if you know the right patterns. Yes and no. On the one hand, the issues AI workloads face, unrestricted network traffic, exposed endpoints, leaked credentials, are the same classic security problems we’ve been wrestling with in IT for decades. On the other hand, they don’t just move data around, they handle sensitive training datasets, expensive inference workloads, and powerful APIs that can be abused in seconds. A stolen API key doesn’t just mean a small breach; it could mean thousands of dollars in cloud bills or a model that starts spilling the beans for the wrong confidant.