Realm.Security builds a security data pipeline for security operations teams. It sits between log sources and the tools that consume them, reducing, enriching and routing security data so that SIEM bills fall without detections going dark.
The problem it targets is familiar to most SOCs: log volumes keep growing, SIEM ingest is priced by volume, and moving data between tools usually needs data engineers. Realm checks every cut against the detections a team actually runs, keeps a full searchable copy of what was removed, and delivers the rest to SIEMs, data lakes and AI SOC tools.
At A Glance
| Vendor | Realm Security, Inc. |
|---|---|
| Category | Security data pipeline for security operations |
| Deployment | Managed service; Realm runs the infrastructure |
| Agentic AI | Realm Clarity AI recommendations; data prepared for AI SOC agents |
| Customers | Dow, Vensure Employer Solutions, National Association of REALTORS, Main Line Health, Revolution Medicines |
| Certifications | SOC 2 Type II |
| Headquarters | Boston, United States |
| Market classification | Security Operations |
What Realm.Security Does
Realm gives a SOC control over which data goes where, without writing pipeline code or opening tickets with an engineering team. Teams keep, cut and route the data they choose, and every change is judged by one test: do the detections the team relies on still fire? Cost reduction becomes a series of small, reviewed decisions rather than a risky one-off project, and data removed from the SIEM stays available for investigations. Because Realm runs collection and the infrastructure itself, the engineering load on the SOC stays low.
Products
Realm Platform
The core pipeline controls data from source to destination. Teams set up routing without code while Realm manages collection. Events are enriched in the pipeline with geo and ASN context, and records go out as open JSON, so the data is not tied to any one destination tool.
Realm Clarity AI
Clarity AI is an advisory agent: nothing changes until a person approves its suggestions. It learns a customer’s sources and destinations, then recommends what to keep and what to cut, showing its reasoning for each suggestion. Typical candidates for reduction include high-frequency DNS events, duplicates and verbose fields. For SOC leads under budget pressure, it turns a manual log review into a list of approvable changes.
Detection Integrity
Detection Integrity answers the main worry with log reduction: losing visibility. Before a reduction goes live, it is checked against the detections the customer runs, and the fields those detections need are protected and mapped to MITRE ATT&CK.
Privacy Guard
Privacy Guard finds and masks PII, PHI and PCI data in transit. Because it keeps the record structure intact, redacted data still works with existing parsers and detections, which matters for teams with privacy obligations on security logs.
Data Haven
Data Haven keeps a complete, searchable copy of history, including the data cut from the SIEM. It lets teams lower ingest costs while keeping long-term retention for hunting and investigations.
How It Works
Realm operates the infrastructure as a managed service, while the SOC keeps control over every decision about its data. Changes move through a five-step loop:
| Component | What it does |
|---|---|
| Recommend | Clarity AI proposes changes. |
| Approve | Approved rules first run in a pending state against live data, so their effect can be tested before production traffic is touched. |
| Execute | The data engine applies the approved rules across collection and routing. |
| Retain | Data Haven stores the full history. |
| Prove | The platform shows that detections still fire on the customer’s own data after the change. |
Agentic AI Capabilities
Data For AI SOC Agents
Realm also prepares data for AI SOC tools. Events arrive parsed, enriched and ready to query, with IP intelligence and commercial threat intelligence added while the data is still in stream. Agents therefore query context-rich events directly instead of running their own enrichment. The approach is bring your own agent: Realm feeds the data lake and the agents a team has already chosen rather than replacing them. Realm has shown a live threat hunt run with Claude on Realm-optimised data in Databricks.
Integrations
| Area | Integrations |
|---|---|
| Example sources | FortiGate, Okta, CrowdStrike, Palo Alto, O365, Zscaler |
| SIEM | Microsoft Sentinel |
| Data lake | Databricks |
| AI SOC partners | 7AI, Dropzone, Vega, Intezer |
Company Background
| Headquarters | Boston, United States |
|---|---|
| Founders and leadership | Peter Martin (Co-Founder and CEO), Jeff Kraemer (Co-Founder and CTO) and Sanket Choksey (Co-Founder) |
| Funding | A reported $15 million Series A led by Jump Capital, with Accomplice and Glasswing Ventures also investing |
Note: This overview is compiled from Realm.Security’s publicly available information as of 4 November 2026. It is provided for information only. NHI Mgmt Group does not endorse any vendor, and nothing on this page represents the opinions of NHI Mgmt Group.