Identifying sensitive and regulated data inside unstructured data has always proved challenging: it’s difficult to accurately discover and classify sensitive data at scale, and scanning unstructured data is both resource heavy and slow to achieve results. Traditional methods of scanning enterprise data can take months or years: on average, 10 PB of unstructured data takes up to 14 years with one scanner, or 280 days with 100 scanners.

This creates significant issues for data compliance, security and governance since unstructured data (files, emails, spreadsheets, presentations, etc) often contain sensitive, critical, and regulated data about people, IP, accounts and more.

Enter BigID’s Hyperscan: a new, transformative ML-based approach to scan large volumes of unstructured data for faster time to value and deeper data insight.

With Hyperscan, BigID completely rethinks how unstructured data is scanned: significantly shortening the scan time of file systems, saving organizations up to 95% of scanning time, and empowering organizations to better manage, protect, and analyze their data at scale.

Hyperscan intelligently identifies where sensitive data is across an organization’s data landscape, enabling them to discover and classify their sensitive, personal, and regulated data faster and more accurately, while dramatically reducing scan time.

How? The patent-pending machine learning algorithm discovers hidden relationships between sensitive data in files and metadata, identifying if a file or data set contains sensitive data based on metadata only. By automatically identifying hotspots of sensitive data, this significantly reduces overall scan time required for discovery.

Why Hyperscan?

  • Scan unstructured data intelligently
  • Predict if a file or data set contains sensitive, personal, or regulated data
  • Minimize false negatives (and false positives)
  • Reduce overall scanning time by 95%
  • Configurable recall (false negatives) and precision (false positives)

BigID’s Hyperscan dramatically expedites the classification, cataloging and correlation of critical & sensitive data in high volume file stores like Office 365, Sharepoint, Box, GDrive, AWS S3, NetApp, EMC, HDFS for data compliance, privacy, remediation, access governance, cloud migration, minimization, and retention. Want to see how Hyperscan transforms data discovery on unstructured data?  Get in touch with our team of ML experts to see Hyperscan in action.