Posted on October 7, 2026 by Joe Stringer, Isovalent at Cisco & Jordan Rife, Google Mark your calendars. On Monday, November 9, CilliumCon kicks off at KubeCon + CloudNativeCon North America in Salt Lake City. It's the eighth time the event has run, and this year's agenda goes straight at the problems that show up when AI and GPU workloads push Kubernetes networking past what it was built for. AI and GPU workloads are stretching Kubernetes networking, and this year's CiliumCon agenda covers the problems that start to appear at that scale. Security is part of the story too.
The same AI capabilities are changing the pace of vulnerability discovery, as models like Mythos have shown, and CVEs can now surface far faster than teams can patch them manually. That puts runtime security front and center, with Tetragon giving teams a way to detect and respond to threats as they happen. CiliumCon began as a half-day co-located event at KubeCon + CloudNativeCon Europe 2023 in Amsterdam. It was created to give the growing Cilium community a dedicated space to connect, share production use cases, and align on technical roadmaps outside of the larger conference.
It brings end users, maintainers, and contributors together to focus on production insights, eBPF enhancements, and community collaboration. There's no other event that goes as deep into Cilium, its sub-projects, and its eBPF foundation. CiliumCon runs Monday, November 9, opening and closing with remarks from co-chairs Joe Stringer and Jordan Rife. In between, you'll hear from end users and maintainers: CiliumCon lines up with two cloud native themes: Cloud Native AI and Security .
Cloud Native AI: Several sessions deal directly with networking and security constraints specific to AI and GPU workloads, from IPv6 addressing to RDMA policy enforcement to debugging inference traffic. Security: The security sessions cover enforcement in the kernel, from sidecarless mTLS that authenticates pod traffic without app changes to Tetragon's runtime policies. These matter more as vulnerabilities surface faster and AI clusters need the same isolation as any other workload.