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DataBahn has raised $40 million in Series B funding as it looks to expand the infrastructure enterprises use to prepare, govern and deliver data to artificial intelligence systems.
Insight Partners led the round, with existing investors Forgepoint Capital, GTM Capital and S3 Ventures also participating. The investment brings the Dallas-based company’s total funding to $59 million, following a $17 million Series A announced in June 2025.
DataBahn plans to direct the new capital towards research and development, additional product capabilities and the continued expansion of its partner-led sales model. The company is positioning its platform as an “agentic data control plane,” an infrastructure layer intended to manage how enterprise data moves between operational systems, security platforms, storage environments and AI models.
The round comes as businesses are generating more telemetry than their existing security and analytics systems can economically process. At the same time, AI agents and copilots require access to reliable enterprise context if they are expected to make useful decisions or take actions on behalf of employees.
Moving Beyond Conventional Data Pipelines #
Traditional enterprise data pipelines generally collect information from a source and deliver it to a destination, such as a security information and event management platform, data warehouse or cloud storage system.
That model becomes less effective as the number of data sources, destinations and consumers expands. An enterprise may need to collect logs from cloud infrastructure, identity systems, endpoints, applications, industrial equipment and software-as-a-service platforms, while simultaneously sending different portions of that information to security tools, analytics systems, data lakes and AI applications.
Moving every available event into every downstream system can create substantial storage, processing and cloud data-transfer costs. It can also make it more difficult for analysts and AI models to locate the information that is genuinely relevant.
DataBahn inserts a management layer between those sources and destinations. Its platform can collect, filter, normalize, enrich and route information while it is moving, rather than requiring each downstream application to independently process the raw data.
The company says its technology currently supports more than 600 data sources and is designed to remain independent of any particular storage platform, security vendor or AI model. This allows customers to change destinations or use several systems without rebuilding the collection layer around each vendor’s architecture.
DataBahn initially focused heavily on cybersecurity telemetry but has since expanded its platform to cover application, observability and Internet of Things and operational technology data.
Preparing Enterprise Data for AI Agents #
The AI component of DataBahn’s strategy is not limited to adding a conversational interface to an existing data platform.
Its Cruz AI system functions as an agentic data engineer that assists with work traditionally handled through manually configured integrations and parsing rules. The software monitors incoming data schemas, identifies changes and can generate updated parsers and mappings when the format of a source changes.
This matters because enterprise data sources rarely remain static. Software vendors introduce new fields, alter event formats and update application programming interfaces. When those changes break a pipeline, security and data engineering teams may lose visibility until the integration is repaired.
DataBahn describes Cruz as a way to turn that process into an approval-based workflow. Instead of engineers building every connector or parser from the beginning, the system can analyse the changed source and prepare an updated configuration for review.
The platform also normalizes information into consistent schemas, including the Open Cybersecurity Schema Framework. Standardization can make data easier for security tools and AI systems to interpret because the same type of event is represented consistently, regardless of which product generated it.
For AI agents, the broader objective is to supply enough context to support a decision without copying an organization’s entire data estate into another platform. DataBahn can process information continuously while retrieving additional context when an application or agent needs it. That approach could become more important as enterprises move from generative AI systems that primarily answer questions to agents that initiate workflows, alter configurations or respond to operational events. The reliability of those agents will depend partly on whether the underlying data is current, correctly structured and governed.
Intelligence Moves Inside the Data Stream #
DataBahn has also been developing what it calls Autonomous In-Stream Data Intelligence, an architecture that applies analysis and decision-making while information is still moving through the pipeline.
The system is intended to do more than prepare data for analysis after it reaches a destination. It can evaluate data quality, identify missing information and determine how individual events should be handled in real time.
In a security environment, for example, a pipeline could enrich an incoming event with threat intelligence, route high-value telemetry into an analytics platform and move lower-priority information into less expensive storage. Applying those decisions before the data reaches a security platform could reduce ingestion costs without forcing an organization to abandon information that may later be needed for an investigation.
The company has argued that intelligence should sit inside the pipeline rather than being applied only after data has already been collected and stored.
This architecture also reflects a broader change in enterprise data management. The pipeline is becoming an active policy and orchestration layer rather than passive plumbing connecting two systems.
Enterprise Adoption Drives the Series B #
DataBahn reports that its revenue has increased by more than 400% year over year, with net revenue retention reaching 180%. It also claims zero customer churn and a 97% success rate across proof-of-concept deployments.
The company serves organizations in healthcare, financial services, manufacturing and transportation, including several Fortune 100 companies. Its disclosed customers include MVB Bank and the Canada Pension Plan Investment Board.
MVB Bank Chief Information Security Officer Parrish Gunnels said the platform helped the bank bring multiple data formats, regulatory requirements and audit controls into a common environment supporting its AI agents.
At the Canada Pension Plan Investment Board, the technology is being used to standardize the onboarding of security telemetry. The organization said this reduced the custom engineering previously required to connect new log sources and made it easier to identify systems that were not sending the expected data.
DataBahn has largely pursued these customers through channel and technology partners rather than relying exclusively on direct sales. Recent initiatives include a deeper integration with Microsoft’s (MSFT ) security ecosystem and an Asia-Pacific distribution agreement with cybersecurity distributor M.Tech.
Funding Targets a Growing Infrastructure Bottleneck #
The Series B will allow DataBahn to expand a product at the intersection of several increasingly expensive enterprise problems.
Security teams are collecting more telemetry to detect attacks and meet regulatory requirements. Data and observability teams are processing larger volumes of application and infrastructure information. AI teams now need access to both operational data and business context.
Each group can purchase additional storage and processing capacity, but that does not address the underlying duplication and fragmentation. Data may still be collected several times, transformed differently by each platform and locked into systems that make it costly to reuse elsewhere.
DataBahn is betting that enterprises will instead place an independent control layer between the systems producing data and those consuming it.
The opportunity is larger than reducing storage bills. An effective control plane could determine what information an AI agent is permitted to access, enrich that information with the necessary context and maintain a record of how the data was transformed and routed.
That would give the pipeline a central role in AI governance, particularly in regulated industries where organizations must explain the information used by automated systems.
Building the Data Foundation for Agentic AI #
The challenge for DataBahn will be demonstrating that the emerging “agentic data control plane” category is distinct enough from existing data pipeline, security data fabric and observability platforms to warrant another layer in the enterprise technology stack.
Large cloud and security vendors are also expanding their data routing, storage and AI capabilities. DataBahn’s counterargument is that a neutral platform can give customers greater control over where information is stored and which applications or models consume it.
Its ability to preserve that neutrality while integrating with a growing number of enterprise systems will be important as the company scales.
The Series B gives DataBahn additional resources to develop that architecture as AI agents become more deeply embedded in enterprise operations. The central premise is straightforward: organizations do not necessarily need to collect and copy more data. They need a better way to identify, govern and activate the information that matters at the moment it is required.
DataBahn plans to preview its next agentic data control plane capabilities at Black Hat USA 2026.