Data Visibility & Lineage
Tracks the full life-cycle of data (origin, movement, evolution) across endpoints, SaaS, cloud, and browsers.
Relies primarily on "inline" traffic inspection; blind to data movements that occur locally or outside of the monitored proxy path.
In addition to AI-driven classification, Cyberhaven uses data lineage to understand the "why" and "where" of data, drastically reducing false positives by recognizing benign work flows.
Heavily dependent on static rules (regex/keywords); often creates high noise levels, as it cannot distinguish between risky behavior and routine business tasks.
Provides a time-sequenced narrative of events (recording every movement, transformation, and user interaction to provide a complete, forensic audit trail) allowing analysts to see exactly what happened in seconds.
Requires security teams to manually piece together disparate logs across network and cloud gateways, which can increase the workload for security teams and mean-time-to-resolution (MTTR).
Cyberhaven offers a robust IRM solution that is part of a true unified AI & Data Security Platform. It can capture human-centric signals (clipboard, screen capture, file renaming) to stop exfiltration before it leaves the device.
Network-focused architecture struggles with malicious activity occurring "off-network" or via local peripherals without consistent proxy routing.
Built and delivered as a unified platform designed for understanding data as it moves and transforms which leads to less manual policy tuning over time.
High overhead for policy management; requires frequent tuning of SSL/TLS inspection bypasses and complex regex sets to maintain business continuity.
Employs adaptive, behavioral-based coaching and justifications to modify user behavior in real-time.
Primarily binary (block/allow) at the network edge, which can disrupt business-critical workflows when policies are overly aggressive.
.avif)
.avif)
