Use Case · Cybersecurity

Security analytics on a data lake, without the cost ceiling.

Security visibility lives or dies on data. But modern environments generate an overwhelming volume of logs, telemetry, and events, far more than traditional tools can analyze in real time without runaway cost. Teams face a painful trade-off: keep less data and lose visibility, or keep everything and watch compute bills explode.

A data lake approach breaks that trade-off, decoupling inexpensive, near limitless storage from on demand compute. AI agents then triage alerts, correlate signals, and investigate threats across the full dataset as events stream in. Detection becomes both more effective and more affordable: full retention, real time analysis, and far less noise reaching your analysts.

• check_circleReal time correlation across full fidelity log and telemetry data
• check_circleDecoupled storage and compute, keep everything, pay only to analyze
• check_circleAgentic triage and investigation that cuts alert fatigue