Emerging regulations such as GDPR and increasing incidence of data breaches such as those at Equifax are bringing a firm’s handling and processing of sensitive data such as personal data of its customers and employees into focus. Enterprises need to now be able to discover and manage sensitive data usage to answer compliance and regulatory reporting requirements and to prevent any reputational damage in the event of a data breach. In this talk, we will outline how using the foundation of open source technologies such as Apache Ranger, Apache Atlas and the recently announced Hortonworks DataPlane Service platform components data stewards, analysts, and data engineers can better understand their sensitive data assets across multiple data lakes at scale. We will demonstrate how enterprises can get a comprehensive 360-degree view of their sensitive data including where such data is located, who is accessing what data and how frequently, when was such data accessed, deleted, moved, how is the data protected, and where did this data come from. In addition we will show how such data can be discovered and profiled to understand their characteristics. We will also demonstrate organization and classification use cases for such sensitive data to facilitate their curation into collections for various business purposes and how such collections can be aggregated and summarized to provide a single view of sensitive data footprint in an enterprise from risk management and audit/compliance/forensics perspectives.