MSU Technologies

SECURE RETRIEVAL-BASED LANGUAGE GENERATION

VAlue proposition

Security in large language models (LLMs) is crucial due to the potential for data breaches, malicious attacks, and the generation of harmful or biased content. LLMs are trained on vast datasets containing sensitive information, making them prime targets for attacks. These attacks can lead to data leaks, privacy violations, and reputational damage. Moreover, LLMs can inadvertently memorize and reproduce sensitive data from their training corpus, potentially exposing intellectual property or authentication credentials

Description of Technology

This technology is a comprehensive, end-to-end secure Retrieval-Augmented Generation (RAG) framework designed to protect sensitive information while integrating database knowledge into large language models. The framework’s novel integration of secure search, controlled document fetching, and end-to-end encryption paves the way for secure LLM-powered applications across sensitive industries such as healthcare, finance, and legal, where data protection and regulatory compliance (e.g., HIPAA, GDPR) are paramount. By combining fully homomorphic encryption (FHE) for encrypted searches and attribute-based encryption (ABE) for granular access control, it ensures both robust data confidentiality and efficient retrieval. By enabling continuous database updates, fine-grained access rules, and strong defenses against advanced security threats, this technology delivers a scalable, next-generation solution for privacy-preserving AI.

Benefits