We're struggling to control sensitive data flow across our various systems—on-prem, cloud storage (SharePoint, Google Drive), and remote endpoints. Our current Data Loss Prevention (DLP) solution is generating too many false positives and is hard to manage. What are the current best practices for reducing the risk of data exfiltration and minimizing disruption, especially concerning PII and intellectual property? What role should data classification play?
3 answers
The foundation of a successful, low-false-positive DLP program is rigorous and accurate data classification. Before you deploy a single policy, you need to use automated tools to discover and tag your PII, PHI, and intellectual property (IP) so policies only trigger on verified sensitive data. Modern strategies involve using Context-Aware DLP, where the policy considers not just what the data is, but who is accessing it, where they are accessing it from, and where they are trying to send it. You need a unified DLP solution that spans endpoint, network, and cloud to enforce consistent policy and prevent shadow IT from creating security gaps leading to data exfiltration.
It sounds like data classification is the main bottleneck. If we focus all our effort on getting the tagging and classification right, will that automatically reduce the false positive rate, or is there still a lot of manual tuning and exceptions handling required after deployment? I'm worried about user productivity drops.
Adopt a Cloud Access Security Broker (CASB) for cloud environments to act as a DLP enforcement point, and integrate it with your endpoint DLP solution to ensure a consistent policy is applied across the entire hybrid infrastructure.
Kevin’s point on CASB is spot on for hybrid cloud. It is essential for monitoring and controlling data flowing into and out of services like Google Workspace or Office 365, which often bypass traditional network DLP solutions. This combined approach is vital to stop unsanctioned data sharing (shadow IT) and prevent accidental or malicious data leakage.
Jason, correct classification is the biggest lever for reducing false positives. Policies based on content matching (like finding a specific keyword) generate high false positives. Policies based on a metadata tag like "CONFIDENTIAL-PII"—which was applied by an automated classification tool—are far more accurate and less intrusive. While some initial tuning is always needed, shifting the focus from constantly refining content-matching rules to refining the classification engine is a far more efficient long-term strategy for operational efficiency and improving user experience.