Why Data Classification Alone Can’t Prevent Data Loss
Summary
The article argues that static data labels can miss human-driven data loss, and describes Darktrace’s behavioral AI approach to detecting risky data movement across email and other tools.
Key points
- The article says modern data moves across cloud services, SaaS applications, endpoints, and distributed workforces, making it harder to monitor and protect.
- It argues that classification based on known patterns can miss intent, context, and human behavior behind data loss.
- Darktrace says its DLP approach learns normal user and data activity to detect anomalies such as misdirected email, insecure cloud links, or suspicious insider activity.
- The company says its system can take configurable actions and can ingest Microsoft Purview sensitivity labels to add context.
- Outbound data-loss activity can also help identify possible account compromise and inform understanding of a user’s inbound and lateral email activity.
Article Details
- Topic
- Using behavioral AI to detect and prevent data loss beyond traditional data classification
Vendors
Products
Darktrace / EMAILDarktrace / EMAIL’s DLP add-on continuously learns in real time, enabling:Darktrace / IDENTITYtheir inbound, outbound, and lateral mail, as well as messaging (and into collaboration tools with Darktrace / IDENTITY), every interaction informs its continuous learning of normal.Microsoft PurviewEnhanced context: While Darktrace doesn’t require sensitivity data labeling, it integrates with Microsoft Purview to ingest sensitivity labels and enrich its understanding of the data – for even more accurate