Data Can Leave Before Anyone Notices
Departing employees start moving data long before their last day, and most security tools only see isolated events: a download here, a USB copy there. This whitepaper shows how data lineage and Linea AI connect those signals into one clear picture, using a real customer exfiltration case, so security teams can intervene before data leaves.
What's inside
A Real Departing-Employee Exfiltration Case
A sales manager accepted a new role, then copied large portions of local data and exported email to a personal USB drive.
Resume uploads and new-offer communications were early signals that, on their own, looked routine.
Cyberhaven connected the job-search activity to the later data movement, and the team intervened before the employee's last day.
Why Traditional Tools Miss This Pattern
Most tools rely on static classification and visibility limited to one channel, like cloud storage or email.
Evaluated in isolation, actions like a download or a USB copy often look benign, even when they're part of a larger pattern.
By the time a fragmented signal triggers an alert, the data is often already outside the organization.
How Data Lineage and Linea AI Catch It
Data lineage tracks the full lifecycle of a file, so it stays connected to its sensitive source even after it's copied, renamed, or transformed.
Linea AI correlates early signals, like job search activity, with later data movement to explain what happened and why it's risky, in plain language.
Security teams can investigate at the level of users, files, and datasets, and build dashboards that surface active risk before data leaves.