Realm Security + Scanner: The data layer for an AI-led SOC

Security data has outgrown the systems built to hold it. The SIEM bill now decides what a security team keeps, and the first casualties are high-volume network, cloud, and firewall telemetry, exactly what an investigation reaches for when something goes wrong. Plans to put AI to work in the SOC inherit that gap. An agent cannot reason over data that was dropped, and it cannot investigate data that is slow and expensive to query.

Realm and Scanner take that problem apart from opposite ends.

Realm is the security data pipeline upstream. It connects your sources, builds an understanding of what every field means and how the data gets used downstream, and shapes the same source data for each destination. Lean, detection-relevant data goes to the SIEM. Full-fidelity data lands in S3 buckets you control.

Scanner is the security data layer downstream, built for teams that want complete visibility. It indexes that data in your buckets, with no ETL and no copies to manage, and makes it behave like it lives in a SIEM: searchable in seconds and covered by continuous detections, for a fraction of the cost.

What arrives in the lake is the difference

Realm delivers every event as one consistent JSON record, from every source, not just the transformed version of it. The raw log travels next to the parsed fields. IPs, users, hosts, and emails are tagged as OCSF observables on the way in, so cross-source pivots never wait on a mapping project. Enrichments like geo-ip, CMBD, HRIS, and/or threat intelligence arrive attached to the event, and every record carries its provenance, which makes the answer traceable. Structure without losing the original, which matters to both the agent and the auditor.

That record is only worth what you can get out of it, and that is where Scanner takes over. Full-text search and native nested-field access make every part of the record queryable the moment it lands, and Scanner's detection rules run against the stream as it arrives, so retained data is covered, not just kept. Analysts pivot on the observables, auditors pull the pristine raw event, and a two-year-old log comes back as fast as yesterday's, for cents per query. Investigation stays iterative instead of rationed.

Ready for the agentic SOC

An agent reads structured fields, not raw text, and a question that spans firewall, identity, and endpoint data only gets answered if those sources already share join keys. Realm calls this Agent Ready Data. The data is SOC-ready when it lands, with no engineering project sitting in between. Scanner serves that same record whether the question comes from an analyst at a keyboard or an agent over MCP.

That combination is what makes an agentic SOC practical rather than aspirational. An investigating agent does not know which query will matter until it runs it, and it runs many. Realm gets the data there in a shape a model can reason over, and Scanner's query economics let the agent ask its twentieth question as freely as its first.

The SIEM gets smaller, not weaker

The SIEM keeps the job it is good at, running alerts and driving analyst workflow, and stops being the place everything has to live. The rest sits in storage you own, in open JSON you can read with or without either of us. Vensure cut its daily FortiGate firewall log volume 83% and saved $254K a year in Sumo Logic costs, with zero detection gaps.

See it on your own data

Both halves of this are provable. Realm's 7-Day Assessment runs against your sources and detections and shows what would go where, and what it would save.  Once the data is gathered and enriched by Realm, test out Scanner’s agentic search and detection capabilities on your data in a 14-Day Proof of Value: get vastly more visibility at significantly less cost compared to traditional SIEMs.