Speakers
Min-Hank Ho
VP, Product Management
Sushant Rao
SVP, Marketing
Overview
The rise of generative AI has led to a surge in the collection and concentration of sensitive data in a small set of data stores within organizations. As companies leverage large language models (LLMs) to increase productivity and unlock insights, the risk of data breaches and non-compliance escalates due to the lack of awareness of the sensitive data and PII in the data stores used for GenAI. Traditional solutions of using regex and rule-based systems to discover such sensitive data is tedious, costly, and prone to errors. Now, the latest AI technologies offer a better way.
This webinar will explore new approaches to discovering sensitive data stored in both unstructured and structured cloud data stores that are used for GenAI pipelines. We will also provide practical guidance on mitigating data exposure risks after discovering the sensitive data and discuss how to streamline key aspects such as eliminating writing manual rules, generating data security policies, and ensuring your data is never shared outside your own infrastructure environment.
