Audit & remediate your existing file sensitivity labels

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No longer rely upon users to ensure file classification accuracy

Historically, validating the accuracy of labels while identifying unlabeled files has been a manually intensive process.

For various reasons, users may not have applied the correct sensitivity label to files resulting in unknown corporate risk. Even automated labeling solutions such as Microsoft MIP or Box Shield need to be periodically “audited” to ensure labeling accuracy, check for dated data, or even back file data that may be missing labels.

Automate and continuously protect your data assets

DryvIQ enables organizations to validate the accuracy of sensitivity labels while identifying unlabeled files — and automatically applying accurate labels utilizing artificial intelligence driven classification and sensitivity discovery.

The label generated by the policy is compared to the existing label on each document to identify a match or mismatch. Mismatched or missing labels can be identified and, if necessary, can be automatically applied to the document. Validating that the correct label are applied to ensure data loss prevention and digital rights management policies can operate effectively.

"DryvIQ allows for the collaboration we need."

  • PolicyLink collaborates reliably across remote sites, provides a real two-way bridge from the field to internal systems and vice versa.
See how they did it

DryvIQ enables our corporate file structure to be flawlessly and securely transplanted to the field and to all our collaborating partners.

Montana Rane, Senior Network Engineer

Establish your baseline & evaluate the strength of your compliance

DryvIQ uncovers these vulnerabilities by performing an advanced sensitivity label audit. It compares existing files classification labels against the results of DryvIQ’s artificial intelligence classification engine — and applies labels to files where none are present.

It reports on the existing classification and enriches the files with additional metadata, including the new suggested classification. DryvIQ delivers a comprehensive report detailing overall statistics and results of the audit, while listing the documents that have discrepancies between the suggested labels and their existing classification.

Increases DLP & DRM investment effectiveness

Data Loss Prevention (DLP) solutions utilize sensitivity labels to restrict movement of files based on specific policy criteria. By improving the accuracy of sensitivity labels, DryvIQ  mitigates the risk of data loss.

Digital Rights Management (DRM) solutions utilize sensitivity labels to automatically encrypt files and control access privileges at rest, in use, and during transmission. By improving the accuracy of sensitivity labels — the DryvIQ platform ensures files are effectively protected.