Enterprises need to provide access to sensitive data while controlling against the unauthorized disclosure of that information from inadvertent leakage, insider threats, and outside attacks targeting data. Work-from-home and bring-your-own-device initiatives pose increased DLP challenges, and new collaboration platforms and GenAI applications have opened new avenues for data leakage. Additionally, the proliferation of cloud services poses threats for data exfiltration, while intellectual property and trade secrets take new forms that do not lend themselves to conventional DLP solutions.
Although DLP is a top investment category when it comes to data security, enterprises continue to struggle to classify data and control against data loss. Whether an enterprise DLP solution or DLP functionality within another security technology, current offerings generate considerable false positive alerts that distract teams that must evaluate and respond to such alerts. Existing approaches relying on regular expression (regex) rules can be brittle and require considerable maintenance, while current DLP solutions frequently encounter scaling and performance issues. Furthermore, complex data types like software code or health sciences data can be difficult to categorize.
To gain insights into these trends, Enterprise Strategy Group surveyed 370 IT and cybersecurity professionals in North America (U.S. and Canada) involved with identity security technologies and processes.
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