Policy-Aware LLM Gateways at Kubernetes Edge

Abstract

As Large Language Models (LLMs) move from centralized cloud architectures to distributed Kubernetes edge environments, the need to ensure compliance, efficiency, and data security arises. While traditional gateways focus on routing and load balancing, they are not equipped with the policy-awareness required for responsible LLM operations in different data jurisdictions. In this paper, we introduce Policy-Aware LLM Gateways (PALG), an architectural framework that incorporates fine-grained policy enforcement, adaptive routing, and data compliance right into the Kubernetes-based LLM deployments. PALG combines a declarative policy engine with edge inference pipelines to dynamically enforce privacy, locality, and regulatory constraints while still allowing for latency and resource utilization optimization. With the use of policy-driven routing, requests are sent to nodes that are not only compliant but also have low latency, thereby lowering the chances of data exposure across different boundaries. An experimental evaluation on a hybrid cloud–edge testbed reveals that PALG is capable of reducing latency by 28%, improving policy adherence by 35%, and decreasing data exposure by 40% as compared to the conventional API gateways. Multi-region healthcare inference case study is used to demonstrate further how PALG can maintain HIPAA-compliant routing even under varying network conditions. Essentially, this work is a proof-of-concept that shows embedding policy intelligence within LLM gateways is a scalable and reliable way to achieve secure, compliant, and high-performance edge AI deployments.