Enterprise Data Protection

Case Study: Fortune 500 insurance company automates discovery and classification of sensitive data

Case study

Property & casualty (P&C) insurers have unique needs that require robust, fully automated data discovery and classification capabilities such as data consolidation, discovery of unstructured data and data segmentation. To properly manage, protect, and leverage its vast portfolio of property, casualty, and other holdings, this Fortune 500 insurance company had made significant investments in several data privacy and security solutions.

However, without a fully autonomous sensitive data discovery and classification engine to drive the optimal performance of these various solutions, they were very concerned that incomplete and inaccurate data was not only diminishing the efficacy of the tools they were using but also that the existence of dark data was leaving them vulnerable.

Read this case study to see how comforte SecurDPS helped the P&C insurance company to automate discovery and classification of sensitive data while integrating with their existing DLP and GRC solutions.

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