Solution
Security operations and security data
A modern security operations centre depends on two things: clean data that costs less to store and search, and enough analyst capacity to investigate what it finds. AI agents can now take on much of the investigation work, and a data fabric can control what reaches the SIEM.
Too many alerts, too much data
Log volumes grow every year and SIEM pricing follows. Analysts triage alerts by hand and many are never investigated. Detections drift as the environment changes, and migrations between platforms take months.
What good looks like
- Data collected once, filtered, enriched and routed to the right tool.
- Lower SIEM ingestion and storage cost without losing coverage.
- AI-assisted investigation that attaches its evidence to each verdict.
- Continuous threat hunting and detection tuning.
- Freedom to change SIEM or data lake without a rebuild.
Vendors that deliver this through Evanssion
| Vendor | Role in this solution |
|---|---|
| Arambh Labs | AI agents that investigate alerts, hunt threats and tune detections |
| Silent Push | Preemptive intelligence that enriches alerts and hunts |
Questions to ask when choosing a solution
- Which share of alerts does it close without human help, and with what evidence?
- Which sources and destinations does it integrate with?
- Can it run in our own environment?
- How is the SIEM cost reduction measured?
Frequently asked questions
What is an AI SOC?
An AI SOC uses AI agents for tasks such as alert triage, investigation and hunting, while analysts review and decide on the important cases.
What is a security data pipeline?
It is software that collects logs and telemetry, filters and normalises them, and sends them to the SIEM, data lake and other tools.
Will AI replace SOC analysts?
It is better described as shifting their time from repetitive triage to investigation, hunting and improving detections.