Shadow Data Discovery addresses the critical problem of sensitive information evading traditional Data Loss Prevention (DLP) measures, exposing organizations to hidden risks. This feature employs advanced machine learning to automatically summarize and categorize documents, providing visibility into potentially sensitive data that conventional security tools might miss. You can use Shadow Data Discovery to uncover and manage shadow data, which includes highly unstructured research and development files, coded communications, merger and acquisition documents, and code repositories in unsupported languages.

Shadow Data Discovery integrates with your existing Enterprise Data Loss Prevention (E-DLP) workflows, allowing you to refine your Enterprise DLP profiles and Security policy rules based on newly discovered categories or sensitive data you weren't previously aware of. This adaptive approach ensures your data security strategy evolves alongside your changing data ecosystem. Shadow Data Discovery transforms large volumes of unstructured data into actionable intelligence, enabling your security analysts to swiftly assess and remediate risks.

By utilizing Shadow Data Discovery, you can more effectively safeguard your organization's sensitive information, reduce the risk of data breaches, and maintain compliance with evolving regulatory requirements. Shadow Data Discovery provides unprecedented visibility into your data landscape, helping you identify patterns in highly unstructured information and secure assets you might not have known existed. It enhances your overall data security posture, streamlines the identification of sensitive information, or allows your security administrators to gain deeper insights into your organization's data landscape.