Within today’s organizations, both the volume of content and the number of applications deployed to manage that content are rapidly expanding.
These rising siloes are casting shadows over growing piles of unstructured data, obscuring sensitivity, security and ownership, and any other potential risks lurking within.
Personally identifiable information [PII], corporate trade secrets or other intellectual property may exist in unstructured data repositories without the knowledge of the organization. Sensitive content may not be properly protected and could represent a cost exposure risk to an organization.
The Dryv platform discovers and enables the identification of sensitivity level and assesses the risk criticality of enterprise content. The platform can discover critical content across various aspects:
Enable the continuous identification and classification of PII across files located in any of your connected content repositories.
Enable your IT team to identify sensitive and unprotected key resources unique to technology and protect them within the organization.
Enable the assessment of unknown content from an acquired organization to properly safeguard content that contains sensitive information.
Enable the assessment of content to identify a subset of documents that are relevant to a specific sub-organization that is being divested.
Validating the accuracy of sensitivity labels while also identifying unlabeled content is a manually intensive process. For various reasons, users may not apply the correct sensitivity label to content. Even automated solutions need to be “re-checked” to ensure labeling accuracy. Dated content, or back file data may also be missing required sensitivity labels.
The Dryv platform enables organizations to validate the accuracy of file sensitivity labels while also identifying unlabeled content—and applying accurate labels.
- The sensitivity label audit [SLA] utilizes A.I.-based classification and sensitivity discovery components to identify the appropriate sensitivity label for documents
- The sensitivity label generated by the SLA policy is then compared to the existing sensitivity label on each document to identify a match or mismatch
- Mismatched or missing labels can either be identified in a report and the identified sensitivity label can be automatically applied to the document
- Validating that the correct sensitivity label is applied ensures that the data loss prevention policy can operate effectively
of organizations are no longer confident they can detect and prevent loss of sensitive data