Vector Search and Queryable Encryption in .NET: Engineering Secure AI Systems at Scale

A comprehensive technical deep-dive for .NET architects and senior engineers on building production-grade vector search systems with encryption-in-use. Explores the intersection of semantic search, LLM embeddings, and privacy-preserving computation in enterprise environments where regulatory compliance and performance cannot be compromised. Executive Summary Contextual Importance: The Convergence of Three Inflection Points The enterprise software landscape is experiencing a profound transformation driven by three simultaneous inflection points. First, the explosive growth in unstructured data — high-dimensional vector representations derived from text, images, and audio. Second, the transition of privacy-preserving AI from academic curiosity to regulatory mandate (GDPR, HIPAA, and the EU AI Act). Third, the strategic challenge of Vector Search and Encryption : the need to perform mathematical similarity operations on data that must remain protected. Target Technologists: Who Needs This