As enterprises rapidly adopt Large Language Models (LLMs) to transform their operations, they face a critical dilemma: unleashing AI's full potential requires processing sensitive data, yet current cloud-based solutions lack verifiable privacy guarantees. This document presents the case for provable privacy—a paradigm shift from trust-based to technically demonstrable data protection—as the essential bridge between AI innovation and enterprise security requirements.
Artificial Intelligence, particularly Large Language Models, has evolved from experimental technology to strategic imperative. Organizations across sectors are embedding LLMs into their core operations—from code generation and data analysis to customer service and decision support. This transformation is backed by substantial investment, with most enterprises planning significant increases