CipherCloud Chronicles 9: Docs Journey-Reassuring Data Classification with CipherCloud

Every document’s journey into the cloud world is different. Files containing corporate sensitive data have to be treated with extreme caution because loss of data from a well-orchestrated data breach can incur heavy penalties for the targeted organization, while resulting in loss of credibility.

Document’s Journey into cloud

A new document has entered the cloud world. It seems to be out of place. All the documents are lined up in a queue with different labels attached to them. The newbie has a lot of unanswered questions. What are these labels for? Who is assigning it to them? A veteran document comes to his aid and starts explaining the document classification process, and how CipherCloud CASB+ has topped the game of protecting sensitive data in the cloud.

CipherCloud DLP hawk for sensitive data

Data Loss Prevention (DLP) is the key technology for identifying and protecting sensitive data across clouds and devices. CipherCloud’s industry-leading Cloud Access Security Broker (CASB+) solution offers advanced, out-of-box and custom DLP policies to identify and classify data across all sanctioned cloud applications on Office 365, Slack, G Suite, Box, Salesforce, AWS, and more.

Reassuring Data Classification with CipherCloud CASB+

CipherCloud CASB+ DLP extends data classification and governance to any document in any cloud, providing complete visibility into the huge volume of unstructured data collaborated between different users and reducing the risk of data leak or loss. Users can classify the documents as internal, external, sensitive, financial, PII, and so on, helping the DLP engine to take appropriate action.

CipherCloud CASB+ integrates with leading data classification solutions, such as Microsoft Azure Information Protection (AIP) and TITUS Classification. Users can apply labels to either classification type, seamlessly extending enterprise data protection to the cloud. CipherCloud’s policy workflows supports recognition of documents with multiple classification labels by multiple engines, and can help in contextual reclassification.

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