Using Large Language Models to Improve Data Loss Prevention in Organizations

Explore how large language models can enhance data loss prevention in organizations in this 25-minute conference talk by Asaf Fried from Cato Networks. Discover the limitations of traditional pattern-based matching methods in detecting sensitive information and learn about a new data loss prevention capability that utilizes natural language processing and large language models. Gain insights into detecting and blocking document transfers based on sensitive categories such as tax forms, financial transactions, patent filings, medical records, and job applications. Understand the challenges of identifying document categories without specific keywords or patterns, and see how full-text analysis through data-driven methods can overcome these obstacles. Delve into the practical applications of NLP and LLMs in improving organizational data security and control over sensitive information transfer.