Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

While the industry focuses on LLM accuracy, technical decision-makers are facing a more complex challenge: How do we govern the autonomous AI agents that will soon operate across our entire infrastructure? Without a dedicated control plane, autonomous agents introduce significant risks to identity, authorization, and auditability. This session moves past the AI hype to discuss the architectural constraints and security patterns required to run agentic systems in highly regulated, enterprise environments.

Your team runs Kubernetes in production. You have clusters, workloads, and traffic flowing. But how much of your platform engineering time goes to maintaining the infrastructure between your tools - re-integrating after upgrades, synchronizing policies across a CNI, a service mesh, an ingress controller, and an observability stack that were never designed to work together? That operational overhead is the integration tax, and it grows with every cluster you add.

If you run a couple of clusters managed by a single team you probably don't notice the way using different tools for CNI, network policy, service mesh, observability, threat detection, and compliance can add up. As you scale, however, this 'integration tax', often hidden in engineer time lost to upgrade coordination, stretched MTTR due to separate L3 and L7 policies, and onboarding drag from multiple tools and query languages, becomes costly.

Your team runs Kubernetes in production. You have clusters, workloads, and traffic flowing. But how much of your platform engineering time goes to maintaining the infrastructure between your tools - re-integrating after upgrades, synchronizing policies across a CNI, a service mesh, an ingress controller, and an observability stack that were never designed to work together? That operational overhead is the integration tax, and it grows with every cluster you add.