How to secure Kubernetes in the age of AI workloads

How Azure Kubernetes Service secures AI agent workloads across networking, policy, image scanning, and runtime detection on shared GPU clusters.

I've been thinking a lot about AI and security, but also about infrastructure fundamentals that have to apply to all your workloads, so there are some themes that keep coming up in what I'm writing at the moment: about how different workloads look these days, about dependencies and provenance and isolation and trust and identity and auth, about GPUs finally dragging their tardy selves to the management party - and about how doing the necessary work will improve all the non-AI workloads too. This time it's looking at cluster hardening on AKS.

Because I write for different sites, there are pieces of my thinking in this area in multiple places: you could read this piece and think about a couple of others:

Microsoft

Kubernetes

AI

cluster hardening

Azure Kubernetes Service AKS

Agent gateway

zero trust

identity

authentication

policy

networking

registries

sandbox

Hyperlight

GPU

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