As AI adoption accelerates, securing sensitive data during inference has become a critical challenge, particularly in regulated and privacy-sensitive environments. Fully Homomorphic Encryption (FHE) enables computation on encrypted data without ever exposing the underlying information, making it a breakthrough for secure, private LLM deployments. This session explores how FHE can be practically applied to scale large language models (LLMs) while maintaining strict data confidentiality. Dr. Walden "Wally" Rhines will cover recent advancements, performance innovations, and real-world use cases demonstrating how FHE unlocks scalable, privacy-preserving AI.