DataBahn

What DataBahn does

Databahn is redefining how enterprises manage the explosion of security and operational data in the AI era. Our AI-powered data pipeline and fabric platform helps organizations securely collect, enrich, orchestrate, and optimize enterprise data—including security, application, observability, and IoT/OT telemetry—for analytics, automation, and AI. With native support for over 500 integrations and built-in enrichment capabilities, Databahn streamlines fragmented data workflows and reduces SIEM and infrastructure costs from day one. The platform requires no specialist training, enabling security and IT teams to extract insights in real time and adapt quickly to new demands. We've helped Fortune 500 and Global 2000 companies reduce data processing costs by over 60% and automate more than 80% of their data engineering workloads. The Databahn platform combines four core products into one intelligent data fabric, spanning collection, routing, insight, and automation, restoring control with a single, intelligent data pipeline management platform built for security at scale.

Products

• Smart EdgeSmart Edge makes data collection simple and reliable. Supporting both agent and agentless deployment, it securely collects data from cloud, on-prem, or vendor sources with advanced edge analytics. It allows instant onboarding of new sources, reduces costs, and ensures data resilience.
• HighwayHighway simplifies how data moves through your ecosystem. It intelligently cleans, transforms, and routes data across any destination, reducing volume, controlling cost, and keeping your pipeline data consistent, governed, and free from vendor lock-in or rigid dependencies.
• CruzCruz automates your data engineering workflows end to end. It discovers sources, normalizes formats, monitors pipeline health, and handles troubleshooting autonomously. With Cruz, you reduce manual work, improve reliability and control, and build a foundation that scales intelligently.
• ReefReef turns raw data into clear, contextual insights using advanced graph-database technology. It correlates signals across domains and powers AI-driven agents for security, operations, and analytics—helping you make faster, smarter decisions with data you fully own and trust.

Key capabilities

• AI-powered data pipeline and fabric platform
• Securely collect, enrich, orchestrate, and optimize enterprise data
• Native support for over 500 integrations
• Built-in enrichment capabilities
• Streamlines fragmented data workflows
• Reduces SIEM and infrastructure costs
• No specialist training required
• Real-time insight extraction
• Automated data integration and management
• Optimized data costs
• Seamless Data Collection & Integration (500+ plug-and-play connectors, agent/agentless methods)
• Optimize SIEM & Data Storage (AI-based filtering, low-cost storage routing)
• Real-Time Pipeline Visibility & Insights (24/7 monitoring, anomaly alerts, built-in failover)
• Complete Data Ownership & Governance (AI-powered metadata tagging, automated data quarantine, data sovereignty)
• Optimized Data Quality & Resilience (Automated validation, cleansing, enrichment)
• Comprehensive Data Governance (Reef-powered metadata tagging, compliance tracking, audit-ready data lineage)
• Simplified Data Control & Democratization (Automated transformations, sensitive data quarantine, intuitive access management)
• AI-driven Filtering
• Schema Drift Management
• Cost Optimization
• Autonomous Parsing
• Pipeline Automation
• Proactive Monitoring
• Contextual Graph Database
• Multi-Source Correlation
• AI-Ready Data
• Unified Collection Architecture
• Guaranteed Data Continuity (mesh architecture, auto-queuing, load balancing, failover)
• End-to-end Visibility and Control (AI-powered telemetry health monitoring, fault detection, lineage tracking)
• Flexible Deployment (agent and agentless data collection)
• Scalable Mesh Design (200TB+ daily ingestion proven)
• Edge Filtering and Analytics (suppress irrelevant logs, micro-indexing)
• Smart Volume Control
• Seamless Data Lineage
• Data Consistency (transformation to CIM, OCSF, CEF, UDM)
• Data Independence (vendor-agnostic orchestration)
• Curated Content Library
• Fault Tolerance
• Modular design
• Elastic (5 million messages per second per data plane)
• Self Healing (automated failure detection, rerouting, recovery)
• Loss-Less Data Transmission
• Automated Watchtower (telemetry health monitoring, device visibility, sensitive data protection, threat detection, incident response)
• Automated data collection & ingestion
• SIEM cost reduction & storage optimization
• Telemetry tracking and pipeline resilience
• Simplified data ownership & governance
• Data Collection & Ingestion
• Enrichment Across Domains
• Multi-Source Correlation
• Data Transformation
• Custom Data Overlays
• Smart Data Filtering
• Unified Data Management
• Cost-Optimized Storage Tiers
• Intelligent Data Routing
• Full Data Visibility
• Predictive Cost Management
• Decoupled Ingestion & Ownership
• Parallel Log Routing
• Intelligent Volume Control & Archiving
• 500+ Plug-and-Play Connectors
• Multi-Destination Delivery
• Volume Control Library & Recommendations
• Real-Time, Head-to-Head Testing
• Dummy Data Without Risk (synthetic data generator)
• Format-Native Compatibility (Elastic, UDM, CIM, OCSF)
• PII masking
• Compliance tagging

Use cases

• Analytics, automation, and AI for enterprise data
• Securely collect, enrich, orchestrate, and optimize enterprise data
• Streamlining fragmented data workflows
• Reducing SIEM and infrastructure costs
• Extracting insights in real time
• Adapting quickly to new demands
• Automating data integration, management, and optimization
• Managing Security Data
• Managing Application Data
• Managing IoT / OT Data
• Managing Observability Data
• Optimizing SIEM & Data Storage
• Maintaining continuous pipeline visibility
• Ensuring complete data ownership & governance
• Automating data validation, cleansing, and enrichment
• Gaining complete visibility into application data
• Securely controlling application data access
• Seamless IoT/OT Data Integration
• Automating data integration, management, and optimization for security data
• Automating data integration and management for application data
• Filtering telemetry data, obtaining real-time analytics, improving data quality for IoT/OT data
• Effortless collection and management of observability data
• Data collection and optimization from the edge
• Data orchestration (cleaning, transforming, routing)
• Automating data engineering workflows
• Turning raw data into contextual insights
• Deploying custom AI-driven agents for security analysis, vulnerability management, and strategic insights
• Centralizing & optimizing security data for AI implementation & enhanced analysis (with Amazon Security Data Lake)
• Faster and smarter data collection & ingestion (with Databricks)
• Lowering Securonix pricing, optimizing SIEM deployments & streamlining SIEM migration
• Simplifying observability data management
• Optimizing Devo SIEM deployments, reducing costs, & improving ROI
• Cybersecurity Data Orchestration in Snowflake
• Empowering security teams with data orchestration, data management, and data governance via security data fabric
• Revolutionizing Microsoft Sentinel Architecture & Cost Management (seamless data ingestion, threat hunting & cost control)
• Delivering a smarter and slimmer SIEM with lower licensing costs, cheaper data storage, and easier threat identification and hunting
• Slashing Google SecOps SIEM pricing & maximizing ROI
• Security Data Engineering
• Threat Intelligence Enrichment before SIEM
• Log Management (deciding what goes to SIEM, cold storage, or dropped)
• SIEM Migration (Google SecOps, Palo Alto XSIAM, Exabeam, ArcSight, Microsoft Sentinel, QRadar)
• OCSF Normalization
• Scaling Log Infrastructure
• Data Onboarding
• Data Visibility & Coverage
• Autonomous In-Stream Data Intelligence (AIDI)
• Snowflake Streaming (eliminating bottlenecks and dependencies)
• ETL vs. Data Pipelines
• Maintaining OCSF Compliance
• Flow Data Ingestion
• Enterprise Observability vs Security Telemetry
• Making Security Data Actually Work for the Enterprise
• OCSF Normalization Breakdowns
• AI Agents Security Incidents and related CVEs
• SIEM Cost Reduction
• Evaluating SIEMs (real-time, head-to-head testing, dummy data, format-native compatibility)
• Onboarding new sources in minutes (for Google SecOps)
• Reducing ingestion costs (for Google SecOps)
• Enriching and normalizing telemetry in-flight (for Google SecOps)
• Full pipeline control (for Google SecOps)
• Automating data transformation into multiple formats (JSON, KVP, CEF, LEEF)
• Aligning data across different vendor-native models (OCSF, CIM, ECS, ASSIM, UDM)
• SIEM Optimization (fewer false positives, intelligent insights, reduced log ingestion)
• Data Ownership and Control (vendor-agnostic, choice of storage)
• Migrating to Microsoft Sentinel (evaluating, migrating, connecting 3rd party sources, volume reduction, data formats, data governance)
• Building or expanding Amazon Security Lake (enrichment, OCSF+Parquet conversion, third-party data ingestion, log volume reduction, schema drift handling, sensitive field tagging)

AI approach

DataBahn leverages AI for various aspects of its data pipeline management platform, including AI-powered data filtering, metadata tagging, autonomous parsing, pipeline automation, proactive monitoring, and AI-driven agents for security analysis and vulnerability management. The platform aims to make data AI-ready and uses AI to optimize data flow, reduce costs, and automate data engineering workflows.

Tech named: AI-powered filtering, AI-based filtering, AI-powered metadata tagging, AI-driven agents, AI-ready data, AI-powered data identification, AI-powered scalability, AI-powered telemetry orchestration, Agentic AI-powered intelligence, AI-powered analytics, AI-powered insights, AI-generated templates, AI-native data fabric, AI-powered data fabric platform, AI-powered data management, AI-Driven Security, AI Agents

Industries served

• Software Development
• Investment Management
• Cybersecurity Technology
• Renewable Energy Services
• Airlines
• Medical Device Manufacturing
• Electronics
• Semiconductor & Software
• Educational Institution

What it says sets it apart

• AI-powered data pipeline and fabric platform
• Redefining how enterprises manage security and operational data in the AI era
• Securely collect, enrich, orchestrate, and optimize enterprise data for analytics, automation, and AI
• Native support for over 500 integrations
• Built-in enrichment capabilities
• Streamlines fragmented data workflows and reduces SIEM and infrastructure costs from day one
• Requires no specialist training
• Enables security and IT teams to extract insights in real time and adapt quickly to new demands
• Proven cost reduction (over 60% data processing costs, over 80% data engineering workloads automation)
• Complete data pipeline platform: every source, every destination, every bit of data collected, transformed, enriched, and routed
• Transforms entire telemetry data lifecycle into a source of real-time insight, agility, and value
• Ultimate data fabric built for security data
• Automates data integration, management, and optimization to save millions in costs and countless hours
• Guaranteed time-to-value from deployment (14 days)
• Lossless and resilient data collection
• AI-powered metadata tagging, automated data quarantine for sensitive information, and complete data sovereignty
• 10x faster data onboarding and solution integration for application data
• Automates data validation, cleansing, and enrichment for instant usability and reliability
• Reef-powered metadata tagging, compliance tracking, and audit-ready data lineage
• Automated transformations, sensitive data quarantine, and intuitive access management for application data
• Filter telemetry data, obtain real-time analytics, improve data quality and manage pipelines visually for IoT/OT data
• Effortless collection and management of observability data from across locations with smarter analytics and visibility
• Foundation for AI-ready, autonomous data pipeline management
• Restores control with a single, intelligent data pipeline management platform built for security at scale
• Smart Edge makes data collection simple and reliable with agent and agentless deployment, advanced edge analytics
• Highway intelligently cleans, transforms, and routes data, reducing volume and controlling cost, free from vendor lock-in
• Cruz automates data engineering workflows end to end, including discovery, normalization, monitoring, and troubleshooting
• Reef turns raw data into contextual insights using advanced graph-database technology, powering AI-driven agents
• Built by Data Experts who experienced the frustrations of disconnected tools and complicated integrations
• Unifies ingestion, enrichment, AI readiness, governance, and scalability into a single platform
• Simple and Intuitive to Use, no technical background or special training needed
• Rapid Time to Value, gets data flowing fast and provides immediate insights
• End-to-End Thinking: optimizes the full pipeline from collection to action
• Precision Over Hype: engineers solutions that work cleanly, reliably, and at scale
• Ownership-Driven: treats customer outcomes like their own, taking responsibility for performance
• Built to Evolve: modular products, flexible architecture, forward-thinking mindset
• AI-powered scalability for real-time detection of threats, anomalies, and insights at the edge
• Constant Monitoring and Observation for self-healing without coding or engineering expertise
• Omnidirectional Data Delivery: channels relevant vs irrelevant log streams to appropriate destinations
• Vendor agnostic orchestration engine
• Smart Pipelines that Grow with you, adapting to new patterns with Cruz
• Optimized SIEM and UEBA operations by reducing irrelevant data ingestion
• Intelligent in-stream filtering routes security-relevant data to SIEM, compliance data to cold storage (Google SecOps)
• Enriches telemetry in-flight with metadata, threat intelligence, and structured data for faster search and investigation (Google SecOps)
• Continuous monitoring, schema drift detection, and silent source alerts for pipeline health (Google SecOps)
• Governance built into the pipeline with PII masking, data lineage, and compliance tagging (Google SecOps)
• Architecture that stays vendor-neutral, decoupling collection from destination
• 900+ volume reduction rules and AI suppression cut irrelevant logs before SIEM
• Standardized, Enriched Data parsed and normalized into formats and data models for downstream tools
• Full Pipeline Control: track data lineage, replay archived logs, prove compliance
• Smart Data Tiering: routes high-value data to SIEM/SOAR, compliance logs to cost-efficient storage
• Multi-destination Delivery: fork data to SIEMs, analytics platforms, or lakes simultaneously
• Adaptive AI Filtering: continually learns usage patterns and recommends suppression or routing
• Decoupled Ingestion & Ownership for SIEM migration: ingest, normalize, and catalog logs into a central fabric you control
• Parallel Log Routing for SIEM migration: stream identical—or filtered—data to both legacy and new SIEMs
• Intelligent Volume Control & Archiving for SIEM migration: forward only security-relevant events to SIEM, archive rest to low-cost storage
• Real-Time, Head-to-Head Testing for SIEM evaluation: compare correlation accuracy, detection gaps, and response times in real time
• Dummy Data Without Risk for SIEM evaluation: use built-in synthetic data generator to simulate high-fidelity log streams
• Format-Native Compatibility for SIEM evaluation: deliver logs in the exact schema each SIEM expects without manual transformation
• Auto-convert logs into native formats Amazon Security Data Lake expects (OCSF, Parquet)
• Catch format changes early to prevent downstream detection or dashboard failures (Amazon Security Lake)
• Detect and isolate sensitive data during ingestion to support compliance and governance (Amazon Security Lake)

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.