Most AI SOC projects stall on the data, not the agent
Security teams want to point AI agents at their lake. Getting the data there is rarely the hard part. What arrives is raw vendor log, a firewall event as one long string, and the agent has to work out its structure on every question.
The usual fix is to write the parsing up front and keep it current as vendors change their formats. That is a data engineering project nobody staffed for, and it’s usually where the AI use case dies. Realm closes that gap on the way in.
What's inside
- 01Why AI SOC projects stall on the dataRaw vendor logs arrive as one long string, and the parsing project to fix it is where most AI use cases die
- 02What lands in your lakeOne record per event with the raw log, parsed fields, OCSF observables, Realm metadata, and in-stream enrichments
- 03How Realm does itRealm lake format for every source, plus schema intelligence that catches vendor format drift before it breaks a parser
- 04Your SIEM and your lake want opposite thingsOne pipeline sends the SIEM lean, detection-relevant data and sends the lake structured, AI-ready data
- 05Customer proofA global manufacturer running one of the largest Sentinel deployments now points its own agents at firewall data in its lake