Aegis: Rethinking Kubernetes Security for AI Workloads

Description and use

<p>I've been working on something I've wanted to explore for a while: <strong>how do we actually secure AI workloads running inside Kubernetes?</strong></p> <p>Not just the containers they run in, but also the tools they can use, the resources they can access, the network destinations they can communicate with, and the things they do at runtime.</p> <p>Think about an AI agent running inside a Kubernetes cluster.</p> <p>It might need to read GitHub issues, communicate with external APIs, access internal services, or interact with other infrastructure components. These capabilities are useful, b

Problem

<p>I've been working on something I've wanted to explore for a while: <strong>how do we actually secure AI workloads running inside Kubernetes?</strong></p> <p>Not just the containers they run in, but also the tools they can use, the resources they can access, the network destinations they can communicate with, and the things they do at runtime.</p> <p>Think about an AI agent running inside a Kubernetes cluster.</p> <p>It might need to read GitHub issues, communicate with external APIs, access internal services, or interact with other infrastructure components. These capabilities are useful, b

Outcome

<p>I've been working on something I've wanted to explore for a while: <strong>how do we actually secure AI workloads running inside Kubernetes?</strong></p> <p>Not just the containers they run in, but also the tools they can use, the resources they can access, the network destinations they can communicate with, and the things they do at runtime.</p> <p>Think about an AI agent running inside a Kubernetes cluster.</p> <p>It might need to read GitHub issues, communicate with external APIs, access internal services, or interact with other infrastructure components. These capabilities are useful, b

Evidence

Aegis: Rethinking Kubernetes Security for AI Workloads

Source