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Explorer

Explorer gives you a landscape view of data objects and locations so you can spot where sensitive information concentrates. Use it to move from a high level posture view into the labels, locations, and files that need attention.

View the data landscape​

  • The data is presented in two panels:
    • The left panel shows datastores grouped by connector type.
    • The right panel shows data objects grouped by Data Type by default.
  • Bubble size on the left is the number of datastores and on the right is the number of data objects. Larger bubbles mean more items.
  • Hover or select a bubble to see the label and count before you drill down.
  • Explorer shows managed datastores. It does not show data in motion.

Change the grouping to find risk​

  • Group data objects by any available label set such as Datasets, Data Type, Data Provenance, Data Pattern, or Data Sensitivity to surface high risk categories.
  • Group locations by connector type in EA. Support for grouping by additional location label sets to enable deeper views will be introduced in a future release.
  • Use grouping changes to compare how objects spread across repositories and labels.

Filter to focus on sensitive data​

  • Apply filters to hide noise and keep only the labels or locations you need.
  • Combine grouping and filters to isolate a slice, such as critical sensitivity objects in SharePoint or OneDrive.
  • When you click a bubble, Explorer applies the filter automatically so you can continue the investigation.

Drill into labels and objects​

  • Click on a bubble to see matching datastores or data objects.
  • Use the actions menu next to a bubble to view all entities, open label details, or open a new page with the full list of objects and labels.
  • Navigate from Explorer into the Data page to view the full object list for the selected item.
  • Open label details to see definitions and understand why items match.

How admins use Explorer​

  • Start posture reviews here to see which repositories hold the largest sensitive object counts.
  • Pivot between label sets to identify patterns, such as internal provenance combined with high sensitivity.
  • Use the applied filters from Explorer when moving into Data so the same context stays in view.