DtSR Episode 647 - Shiran Bareli AI as More Than Next Gen DLP

This episode explores the challenges of data classification in cybersecurity and how AI, specifically Large Language Models (LLMs), are revolutionizing this field. Against the backdrop of the historical inadequacy of traditional methods like regexes and heuristic rules in handling the exponentially growing volume and variety of data, the discussion centers on the limitations of achieving high precision in data identification. More significantly, the interview delves into how LLMs, combined with other techniques like statistical validation, offer a more holistic approach by considering the context of data—its location, creator, and relationships with other data points—to improve accuracy. For instance, the guest explains how their AI system uses "soft labels" and learns from existing data to classify sensitive information without directly handling sensitive data itself. As the discussion pivoted to practical applications, the guest highlights the ability of AI to identify critical data within compromised email accounts and analyze access patterns across platforms like M365, enabling more purposeful security measures. This means organizations can proactively identify and mitigate risks, moving beyond reactive responses to data breaches and improving overall security posture.