Cyberhaven

Your AI agents can now plan, act, and move data on their own. Most security tools were never built to see that.

Data movement across AI apps is up 80%. What's your visibility on that data?

When we started Cyberhaven, data lineage in security was an idea most people hadn’t heard of. Today it’s everywhere, and every DLP vendor seems to have “lineage” on their slide. Honestly, that makes us proud. We pioneered data lineage for data security, and it’s a bit like watching your kid grow up and seeing the whole neighborhood try to copy their style. But not all lineage is the same. Building lineage that holds up at enterprise scale is hard. It means: - A purpose-built graph engine handling roughly a trillion nodes, fast enough to block in real time - Endpoint sensors that cover every way humans and AI agents move data, while using a median of 0.2% CPU - AI that understands business context, so DLP stops being a game of thousands of exceptions and endless false positives Volodymyr Kuznetsov, Radu Banabic, and I wrote up what we’ve learned from years of building this, including what it really takes to tell lineage that works apart from lineage that only looks good in a demo. Because not all lineage is the same - https://lnkd.in/gy5hPPEK #DataSecurity #DLP #DataLineage #AI #Cybersecurity

Security teams running separate tools for DLP, insider risk, and AI security lose context between them, and agents can exploit those blind spots at machine speed. Cyberhaven connects lineage, identity, and behavior in one place, cutting investigation time by 5x and false positives by 90%.

Employee use of GenAI applications stalled between December and June. Data moving through those same tools grew 80% year over year. Cyberhaven's mid-year report lays out what that shift means for enterprise security programs.

Legacy DLP tools inspect files at a single point and miss data with no recognizable pattern: source code, product designs, business plans, meeting recordings. Cyberhaven traces the full journey of every sensitive file, cutting false positives by 90% so security teams can act on real risk instead of sorting through false alarms.