Connecting SecOps to value (and data)
Unlock the full power of Sentinel with DataBahn's Security Data Fabric. Connect with non-Microsoft sources with ease, filter and reduce log volumes, and save your SOC data engineering effort.
Get started with SecOps and DataBahn
Enterprise security teams prefer Google SecOps for its powerful analytics and AI capabilities, and its GCP-linked scalability and data processing speeds.With DataBahn, SOCs can seamlessly collect data from third-party sources and even custom applications without needing to parse, normalize, or transform incoming data - all while reducing log ingestion by ~50%
400+
Plug-and-Play connectors to add non-Microsoft sources
50%
Lower log volumes and Sentinel license costs
80%
Reduction in manual effort in data parsing & transformation
Supercharge your sentinel SIEM
Compare SecOps head-to-head against your existing SIEM to measure its effectiveness for you
Accelerate your migration to Google SecOps and reduce your time-to-value from months to 14 days
Effortlessly connect your third-party data sources to SecOps with no coding or data wrangling effort
Reduce cost and optimize SecOps effectiveness with seamless and intelligent log reduction
Parse, Normalize, and Transform data from popular and custom applications with ease for seamless data flow
Track, manage, and have visibility of data across sources for enhanced enrichment and correlation
Your starting point for all things DataBahn
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Have Questions?
Here's what we hear often
A Data Fabric is an architecture that enables systems to connect with different sources and destinations for data, simplifying its movement and management using intelligent and automated systems, a unified view, and the potential for real-time access and analytics. Data Fabrics manage security, application, observability, and IoT/OT data to automate and optimize data operations and engineering work for security, IT, and data teams. Data Fabrics are also an umbrella term for Data Pipeline Management (DPM) platforms, which are tools and systems that enable easier collection and ingestion of data from various sources. DPM platforms are evaluated by how many sources and destinations they can manage and to what degree they can optimize the volume of data being orchestrated through them.
DataBahn goes beyond being a data pipeline by delivering a full-stack data management and AI transformation solution. We are the leading DPM solution (maximum number of integrations, most effective volume reductions, etc.) and also deliver AI-powered improvements that make us best-in-class. Our Agentic AI automates data engineering tasks by autonomously detecting log sources, creating pipelines, and parsing structured and unstructured data from standard and custom applications. It also tracks, monitors, and manages data flow, using backups and flagging any errors to ensure the data flows through. Our system collates, correlates, and tags the data to enable simplified access, and creates a database that enables custom AI agent or AI application development.
Our volume reduction functionality is completely under the control of our users. There are two modules - a library of volume reduction rules that will reduce SIEM and observability data. Some of these rules are absolutely guaranteed not to impact their functioning (data deduplication, for example) while others are based on our collective experience of being less relevant or useful. Users can absolutely control which data goes where, and opt to keep sending those volumes to their SIEM or Observability solution. The AI-powered insights layer will review and make suggestions based on
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