Network and Resource Isolation in AI Sandboxes
Network access is one of the most powerful and dangerous capabilities granted to AI systems. Without isolation, models can be manipulated into exfiltrating data, scanning internal services, or interacting with untrusted external endpoints. AI sandboxes must enforce strict network policies that default to denial and explicitly allow only required destinations and protocols. Resource isolation, including CPU, memory, and storage limits, is equally important to prevent denial-of-service conditions or runaway execution. These controls should be applied dynamically based on agent role and context rather than statically configured per deployment. In environments where AI agents interact with decentralized networks, resource and network isolation help prevent unintended propagation across nodes or chains.
Consider using
- Cloudflare - edge DLP and network policy controls for AI traffic
- AccuKnox - zero trust eBPF runtime enforcement for containerized AI workloads
- gVisor (Google) - user-space kernel for granular syscall and network mediation
- Operant AI - API discovery and runtime network access controls for agent ecosystems