Securing Edge Intelligence: Integrating Local LLMs with Zero‑Trust Kubernetes Networking
Introduction Edge intelligence—running sophisticated machine‑learning workloads close to the data source—has moved from a research curiosity to a production‑grade requirement. The rise of local large language models (LLMs) on edge devices (industrial gateways, autonomous drones, retail kiosks, etc.) enables low‑latency inference, privacy‑preserving processing, and offline operation. However, exposing powerful LLMs at the edge also expands the attack surface: compromised devices can become vectors for data exfiltration, model theft, or lateral movement across a corporate network. ...