Rover AI careers

The Problem: Security incidents don't begin when the alert fires. With the average threat dwell time reaching 241 days, standard 30 to 90-day hot retention limits leave security teams and AI agents completely blind to initial compromise vectors. But keeping years of data searchable on traditional SIEMs requires massive, prohibitively expensive search clusters.

Our Solution: Rover is a SIEM and Security Data Lake built for retention economics. We allow teams to search and analyze their full security history as if it were hot—without rehydration, moving data, or managing search clusters. Cold Storage. Hot Intelligence.

How we do it:

Object-Storage-Native LSM: Rover writes inverted indexes directly alongside full-fidelity security data in customer-owned object storage. No hot index tier.
Serverless Query Engine: Search and analytics execute on-demand, fanning out across ephemeral workers that disappear when the work is done. No always-on compute.
Signal Mesh: A stateful engine that evaluates detection queries continuously as data arrives, producing instant signals and alerts.
Zero Rehydration: Historical data is instantly queryable in sub-seconds.