We challenged every assumption and developed an Advance DLP for scale and accuracy to enhance data protection.
Traditional data loss prevention tools fall short in safeguarding critical data, disrupt regular activities, and rely on outdated technology, causing frustration for both administrators and end users.
By relying solely on content analysis, DLP tools fail to accurately identify critical data and produce false positive alerts, wasting analysts' time.
Due to false positives that hinder users from completing their tasks, the prevention features of DLP tools are frequently disabled. Additionally, their outdated technology slows down computers and disrupts cloud applications.
Security teams invest significant time and resources to fine-tune DLP content policies to minimize false positives. Furthermore, traditional DLP tools necessitate on-premises software and databases.
We detect critical data that traditional DLP tools miss and safeguard it across all exfiltration channels using a single product and policy.
We combine content analysis with data movement — providing visibility and tracking the data’s origin, its history, and the individuals who have handled it—to more accurately determine which data is important and which is not. This approach reduces false positives generated by common content patterns like phone numbers and emails.