AI, Privacy, and Data Leakage: A Study of Current DLP Shortcomings
Sunil Arora, Vishwas Manral, S Dupali, Parthasarathi Chakraborty · 2025
Artificial intelligence (AI) systems have transformed data processing, but they raise serious privacy concerns about data leakage and the unauthorized exposure of sensitive personal information. As AI models are increasingly deployed across various industries to process and analyze personal data, understanding the role of Data Leakage Prevention (DLP) tools in securing AI applications and data is critical. Traditional DLP tools that rely on static, rule-based controls are ineffective in handling dynamic and complex data flows within AI environments, especially with synthetic data usage. This paper explores the challenges of DLP solutions in the AI environment, particularly with synthetic data usage and the need for a modernized approach to DLP solutions.