Ai Boosts Efficiency But Exposes Critical Infrastructure To New Cyber Threats
The Intersection of Artificial Intelligence and Cybersecurity: Shaping the Future of Critical …
31. July 2026
Data Revolution Heralds New Era Of Ai-Fueled Efficiency With Agentic Control Layers
The Rise of Agentic Control Layers in Enterprise Data Management: How DataBahn is Revolutionizing the Way Organizations Prepare, Govern, and Deliver Data to Artificial Intelligence Systems
In an increasingly complex and interconnected world, organizations are facing unprecedented challenges in managing their vast amounts of data. As artificial intelligence (AI) systems become more pervasive in enterprise operations, the need for effective data management solutions has never been more pressing. Enter DataBahn, a Dallas-based company that has raised $40 million in Series B funding to build an agentic control layer for enterprise data.
DataBahn’s platform is designed to provide a neutral infrastructure layer between operational systems, security platforms, storage environments, and AI models. This approach aims to address the growing issue of telemetry overload in traditional enterprise data pipelines. With more data being generated than ever before, organizations are struggling to process and analyze it efficiently, leading to increased costs and decreased productivity.
The company’s platform collects, filters, normalizes, enriches, and routes information while it is moving, rather than requiring each downstream application to independently process the raw data. This approach not only reduces storage and processing costs but also makes it easier for analysts and AI models to locate relevant information.
DataBahn’s technology currently supports over 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.
The company initially focused on cybersecurity telemetry but has since expanded its platform to cover application, observability, and Internet of Things (IoT) and operational technology data. Its Cruz AI system functions as an agentic data engineer that assists with work traditionally handled through manually configured integrations and parsing rules.
Cruz analyzes incoming data schemas, identifies changes, and generates 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 (APIs). 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 scratch, the system can analyze the changed source and prepare an updated configuration for review.
The platform also normalizes information into consistent schemas, including the Open Cybersecurity Schema Framework (OCSF). 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, DataBahn’s broader objective is to supply enough context to support a decision without copying an organization’s entire data estate into another platform. The company processes information continuously while retrieving additional context when an application or agent needs it.
This 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.
DataBahn has also been developing what it calls Autonomous In-Stream Data Intelligence (ASDI), an architecture that applies analysis and decision-making while information is still moving through the pipeline. This system evaluates data quality, identifies missing information, and determines 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 argues 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, where the pipeline is becoming an active policy and orchestration layer rather than passive plumbing connecting two systems.
Enterprise adoption drives the Series B funding, with DataBahn reporting revenue growth of over 400% year-over-year and net revenue retention reaching 180%. The company claims zero customer churn and a 97% success rate across proof-of-concept deployments.
DataBahn 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 (CPPIB).
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 CPPIB, DataBahn’s technology is being used to standardize the onboarding of security telemetry. The organization stated 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.
The Series B funding targets a growing infrastructure bottleneck, where 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. DataBahn is betting that enterprises will instead place an independent control layer between the systems producing data and those consuming it.
This approach 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. With this funding and technology, DataBahn is poised to revolutionize the way organizations prepare, govern, and deliver data to AI systems, creating a more efficient, effective, and secure data management infrastructure for enterprises worldwide.
DataBahn’s Series B funding represents a significant milestone in the company’s mission to build an agentic control layer for enterprise data. With its cutting-edge technology and growing customer base, DataBahn is well-positioned to address the emerging challenge of telemetry overload and provide a more efficient and secure way for organizations to manage their data.
As AI continues to play a critical role in enterprise operations, the need for effective data management solutions will only continue to grow. DataBahn’s innovative approach to data governance and control offers a promising solution to this problem, and its technology has the potential to transform the way organizations prepare, govern, and deliver data to AI systems.
By providing a neutral infrastructure layer that can be easily integrated with existing systems, DataBahn is poised to become an industry leader in the emerging field of agentic data control planes. With its Series B funding, the company will continue to push the boundaries of what is possible in enterprise data management, empowering organizations to make the most of their data and unlock new levels of efficiency, productivity, and innovation.
https://aiwirenews.com/manulife-unveils-groundbreaking-ai-system-to-revolutionize-d9113e/
The company’s technology currently supports over 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 on cybersecurity telemetry but has since expanded its platform to cover application, observability, and Internet of Things (IoT) and operational technology data. Its Cruz AI system functions as an agentic data engineer that assists with work traditionally handled through manually configured integrations and parsing rules.
Cruz analyzes incoming data schemas, identifies changes, and generates 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 (APIs). When those changes break a pipeline, security and data engineering teams may lose visibility until the integration is repaired.
DataBahn’s platform collects, filters, normalizes, enriches, and routes information while it is moving, rather than requiring each downstream application to independently process the raw data. This approach not only reduces storage and processing costs but also makes it easier for analysts and AI models to locate relevant information.
https://aiwirenews.com/ai-systems-failures-escalate-as-organizations-struggle-to-814e63/
DataBahn’s technology is designed to provide a neutral infrastructure layer between operational systems, security platforms, storage environments, and AI models. This approach aims to address the growing issue of telemetry overload in traditional enterprise data pipelines.
DataBahn 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 (CPPIB).
https://aiwirenews.com/ai-s-secret-tab-how-to-spot-hidden-costs-before-they-sink-b0a96f/
DataBahn’s technology has already started to make a significant impact in the industry. The company plans to continue pushing the boundaries of what is possible in enterprise data management, empowering organizations to make the most of their data and unlock new levels of efficiency, productivity, and innovation.
https://www.unite.ai/ai-infrastructure-cyber-physical-systems-critical-infrastructure-security/