AI, Privacy, and Data Leakage: A Study of Current DLP Shortcomings

‍Published in the 6th International Conference on Artificial Intelligence, Robotics and related fields (2025).‍

AI-driven systems are reshaping how organizations process and analyze sensitive information, but they also introduce new risks around privacy and unintended data exposure. This research highlights how traditional Data Loss Prevention (DLP) tools—largely built on static, rule-based policies—struggle to keep up with the dynamic and complex data flows common in modern AI environments. The paper also discusses the growing challenges posed by synthetic data and emphasizes the need for a more adaptive, next-generation approach to enterprise DLP.

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Published on IEEE Xplore (2025). Full paper available via IEEE digital library here.