As organizations increasingly integrate AI into their operations, the integrity of training data becomes paramount. While AI models promise enhanced efficiency and decision-making, they are only as reliable as the data they are trained on. This session delves into the often-overlooked vulnerabilities within AI training datasets, including the inadvertent inclusion of sensitive information and the threat of data poisoning attacks. We’ll explore how traditional security measures may fall short in safeguarding AI pipelines and introduce advanced strategies like Data Detection and Response (DDR) and Content Disarm and Reconstruction (CDR). These methodologies proactively identify and neutralize threats within data streams, ensuring that AI models are trained on clean, compliant, and secure datasets. Attendees will gain insights into: -The types of risks inherent in AI training data. -The principles and applications of DDR and CDR in mitigating these risks. -Best practices for implementing robust data security measures in AI development workflows.