DataBahn Co-Founder reveals why traditional data pipelines are failing AI initiatives and how modular architectures are reshaping cybersecurity

Enterprise data engineering has a problem. Despite massive investments in data infrastructure, most organizations are drowning in what DataBahn Co-Founder and President Nithya Nareshkumar calls a “hairy ball of data engineering” — tangled networks of complex pipelines moving petabytes of largely unnecessary data across fragmented systems.

With a fresh $17 million Series A round, DataBahn is betting that the solution isn’t better models or more storage, but fundamentally rethinking how enterprises collect, process, and route their data. The startup’s enterprise-grade platform promises to bring order to data chaos while positioning organizations for AI success.

The Data Pipeline Crisis Hiding in Plain Sight

Nareshkumar, drawing from her founding team’s collective 60+ years in cybersecurity and enterprise data, describes a familiar enterprise scenario: “You have data producers and data consumers, and depending on the size of the enterprise, you have hundreds of thousands of these data producers and consumers that are completely disconnected and fragmented.”

The connecting tissue between these systems? An army of data engineers building and maintaining pipelines that collect telemetry from thousands of systems, cleanse and enrich data, transform it into various formats, and route it to multiple destinations. The problem isn’t just complexity — it’s waste.

“A significant portion of this data is not even needed to go into these consuming applications or systems,” Nareshkumar explains. This inefficiency becomes exponentially more problematic as enterprises attempt to implement AI initiatives built on poor-quality, unstructured data foundations.

AI-Native Pipeline Management

DataBahn‘s approach centers on what they call “agentic AI” — intelligence built directly into the data pipeline rather than bolted on afterward. Their platform features over 500 connectors for out-of-the-box data collection, combined with intelligent parsing, enrichment, and selective routing capabilities.

The company’s AI-native assistant, Cruz, represents a significant departure from traditional pipeline management. “Traditionally, data pipelines just move data,” Nareshkumar notes. “What Cruz does is change that by actively analyzing the data as it flows through us, automatically detecting issues like broken parsers, schema drift, duplicate logs and even unnecessary volume that’s not required for the AI models.”

This real-time intelligence enables Cruz to flag inefficiencies, suggest optimizations, and in many cases, take corrective actions without manual intervention. Nareshkumar describes it as “your data-engineer-in-a-box that continuously monitors and tunes the pipeline to get you cleaner data, lower costs, and better outcomes.”

Breaking the SIEM Monolith

Beyond general data engineering, DataBahn is positioning itself at the center of cybersecurity‘s architectural transformation. Traditional Security Information and Event Management (SIEM) systems have become catch-all repositories where organizations dump everything — whether needed or not — primarily to check compliance boxes.

“In this model, you don’t own your data — you’re renting access to your own telemetry inside someone else’s ecosystem,” Nareshkumar explains. “You’re paying for storage, search, and compute on top of all that, which has turned the SIEM into a massive monolith.”

DataBahn’s solution involves sitting in front of the SIEM, collecting, parsing, enriching, and selectively routing only security-relevant data — typically 20-30% of total telemetry — for detection and correlation. The remaining data flows into security data lakes in the enterprise’s chosen format and compute environment, whether Snowflake, Databricks, AWS, or on-premises infrastructure.

This architecture enables what Nareshkumar calls a “headless cybersecurity stack” where “SIEMs do less ingestion and more detection, data lakes become your source of truth, analytics comes to your data not the other way around, and enterprises get full control, flexibility and visibility of their data.”

Beyond Cybersecurity: The Broader Enterprise Data Fabric

While cybersecurity remains DataBahn’s primary focus, the platform’s architecture is fundamentally domain-agnostic. The company is seeing traction in observability and IT operations, where traces, logs, and metrics from applications and infrastructure can overwhelm platforms like Datadog and Prometheus with similar inefficiencies.

“DataBahn can filter down, sample and enrich that data at the edge itself, reducing cost before it gets to your observability solutions while routing the rest of the data into cold storage,” Nareshkumar explains.

The platform also supports AI enablement across any data workload, transforming information into structured, schema-aligned streams compatible with LLMs and supporting open formats like Parquet, Delta, and JSON for seamless integration with modern data platforms.

Enterprise Traction and Future Vision

DataBahn’s rapid growth — from a small seed round in November to Series A within months — has been fueled by Fortune 100 and even Fortune 10 customer adoption. Nareshkumar attributes this traction to three factors: a team with deep conviction who’ve “lived the pain” firsthand, a product that delivers real solutions, and “incredibly engaged and obsessed customers” who become champions and advocates.

The company’s immediate roadmap centers heavily on AI enablement. “Enterprises are truly grappling with how do we make this data AI ready,” Nareshkumar notes. “Our product focus is going to be around building AI agents at their fingertips through this AI-enabled data.”

The Data-First AI Strategy

DataBahn’s thesis ultimately rests on a simple premise that many enterprises are learning the hard way: “AI is only as good as the data it’s trained on,” as Nareshkumar puts it. “If you don’t get this first mile of your data journey right, the AI downstream is limited, biased, and sometimes just flat out wrong.”

As enterprises continue investing billions in AI initiatives, DataBahn’s approach suggests that the real innovation opportunity isn’t in models or compute, but in the fundamental infrastructure that feeds those systems. For organizations struggling with data sprawl, security compliance, and AI readiness, rethinking the pipeline architecture may be the missing piece in their digital transformation puzzle.