Typically, data from business, IT and cybersecurity systems is siloed with limited cross-correlation to provide business risk insights. However, DRI’s core function involves converging this data, siloed in such a way that data insights from each repository can be correlated to provide greater awareness of data value, data flows, ownership and trust. In this case study in partnership with Tag Cyber–a renowned cybersecurity research analyst firm– we introduce a four-step data risk intelligence (DRI) methodology for improving the data environment of an enterprise. In this report you will learn:
- What is Data Risk Intelligence (DRI)
- The DRI process: Inventory, Analysis, Monitoring, Action
- How to improve data visibility, protect what matters most and purge stale and toxic data