Separation of workloads before AI wasn’t easy, but it was far more straightforward. Servers sat in data centers, VLANs segregated packets, firewall rules managed ingress/egress, and physical cables separated packets to and from their destinations.

Now, flip on an agent, and it has access to almost everything by default.

Enterprises cannot operate with that default, and in this article, you’ll learn how to implement true sandboxing at every layer that matters.

When you run a container (think Docker), it’s a lightweight package that runs as an isolated process. But that isolation has limits. Containers share the host kernel, and those on Docker’s default bridge can communicate with one another. A container cannot directly access host files by default, but a writable bind mount can let it modify or delete the host files exposed through that mount.

If you rely on a container alone for agent isolation, you may leave important boundaries unprotected. Thinking of containers as locked down by default is a major security risk. When running workloads in Kubernetes, you have tools like NetworkPolicy, service mesh for secure service communication, and SecurityContext to ensure containers run in a governed way.

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That’s why having default security out of the box, easily turned on, and policy enforcement built into your AI sandboxing solution is key to enabling all teams. Without it, every organization risks thinking security exists, only to face a harsh reality.

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