Traditional data loss prevention strategies often rely on static rules and pattern-matching — an approach that can miss subtle but serious risks. In a recent article, Tim Addison, Principal Product Manager at Microsoft, explores how DLP Analytics in Microsoft Purview helps organisations move beyond a checkbox approach to data protection.

By using machine learning to analyse information-sharing activity, DLP Analytics identifies security gaps, validates policy effectiveness, and recommends proactive improvements. With support for trainable classifiers, behavioural detection, and risk-based policy tuning, it gives security teams a smarter, more adaptive way to manage data loss prevention at scale.

Read the full article by Tim Addison