Cisco has introduced the Cisco Data Fabric, a next-generation architecture designed to help enterprises harness the full potential of their machine data with AI. Built on the Splunk platform, this innovation significantly lowers the cost and complexity of managing machine data at scale while enabling advanced AI applications. These include training custom AI models, powering agentic workflows, and correlating machine and business data streams to generate actionable insights for faster, smarter decision-making.
“Organizations everywhere are sitting on a gold mine of machine data that’s been too complex, cumbersome, and costly to leverage for AI, until today,” said Jeetu Patel, President and Chief Product Officer at Cisco. “From sensor readings and factory metrics to checkout system data and event updates from apps, servers, networks and more, machine data drives how businesses operate. Splunk revolutionized data and analytics for the cloud. And now, the Cisco Data Fabric is poised to do the same for AI by making it possible for enterprises to build AI models with their own proprietary machine data.”
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Purpose-built for the AI era, the Cisco Data Fabric allows businesses to innovate at speed, enhance security, and achieve greater agility. By unifying machine data from across the enterprise, the framework delivers turnkey solutions that cut costs and streamline operations, overcoming the challenges of distributed data management at scale.
“Our goal is to give customers the fastest, most secure path from data to action,” said Kamal Hathi, SVP and GM, Splunk, a Cisco company. “By embedding AI across the platform and embracing open standards, we’re not just helping organizations analyze information faster we’re enabling them to anticipate change, scale innovation without unnecessary complexity, and deliver digital services that are more resilient, adaptive, and responsive to the needs of their users.”
Unlocking the Power of Machine Data for AI
The Cisco Data Fabric transforms raw machine data into intelligence that drives operational efficiency, faster decisions, and innovation. Intelligent edge data management powers advanced filtering, shaping, and tiering, while federation capabilities provide near real-time insights across domains. Its AI-driven experience layer, featuring agentic assistants, reduces administrative workload and accelerates problem resolution.
Key capabilities include:
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Operate on Machine Data at Scale: A unified foundation to transform data across edge, cloud, and on-premises into actionable insights for SecOps, ITOps, DevOps, and NetOps.
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Cross-Domain Real-Time Search and Analysis: Federated analytics across Amazon S3, Apache Iceberg, Delta Lake, Snowflake, Microsoft Azure, and more (with additional sources coming in 2026).
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Flexible, Open Architecture: Supports hybrid and cloud environments with strong compliance to open standards, integrations, and self-service innovation.
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Unlock Proprietary Data Value: The upcoming Time Series Foundation Model will power anomaly detection, forecasting, and automated root-cause analysis. Combined with the Splunk Machine Data Lake and AI Toolkit, this transforms proprietary machine data into a fuel source for AI innovation.
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Unify Human and AI Experiences: Cisco AI Canvas integrates with Splunk Cloud Platform to provide a collaborative “virtual war room” for faster investigations, real-time collaboration, and advanced visualization all within a unified interface.
“The Cisco Data Fabric addresses a critical pain point in today’s AI-driven enterprise: the challenge to quickly and securely unify vast streams of machine data for continuous resilience,” said Archana Venkatraman, senior research director, cloud data management, IDC. “By enabling a federated approach that eliminates data movement, it provides a pragmatic solution for organizations operationalizing AI at scale. Its focus on real-time search, coupled with a repository for AI-ready data, provides tangible value by reducing complexity and time to insights. This unified architecture is a strong step toward helping customers build more resilient and trustworthy AI systems.”
