Stop Insider Threats Without Drowning in False Alarms
Traditional insider risk management (IRM) tools take a passive approach. They alert security teams to threats but cannot stop them, and too many of those alerts turn out to be false positives. This datasheet shows how Cyberhaven combines data awareness with behavioral analysis to detect insider threats accurately, intervene before data leaves the organization, and give analysts the context to investigate quickly, across human and agentic workflows.
What's inside
Why traditional IRM falls short
IRM tools analyze behavior but cannot connect it to the data being handled or to events across time, so they generate alerts on activity that is not risky while missing real insider threats.
When IRM tools detect a user mishandling data, they only send an alert. They ingest and analyze event logs but have no way to intervene when data is at risk.
Alerts rarely include enough context to understand intent, so analysts investigating an incident have to hunt for additional detail beyond what the alert provides.
Cyberhaven redefines IRM
Combining behavioral analysis with data analysis makes detection more sensitive to real threats and quieter on everyday behavior.
Cyberhaven retains and correlates events across weeks or months, the way real insider threats actually unfold, not just in the hours around a single alert.
Cyberhaven does not stop at detection. It blocks exfiltration across every channel, including cloud, email, websites, removable storage, and Apple AirDrop.
The technology behind proactive protection
Data Lineage traces the complete flow of data, providing context about where it came from, who has interacted with it, and how it has been modified over time.
AI Classification applies advanced AI to lineage, context, and content, delivering higher accuracy and more effective enforcement than behavior or content analysis alone.
Cyberhaven Linea AI agents analyze billions of workflows and every piece of data to find and report on insider risks in real time.