Bringing the Power of AI to Where Your Data Lives: A New Milestone with Cisco and NVIDIA

In today’s landscape, the ability to turn machine data into agentic action and intelligence is no longer a luxury—it is a core operational requirement. Machine data accounts for nearly 55% of global data and contains the operational context needed to investigate incidents, predict problems, and power AI-driven action.

Yet, for many of our public sector, highly regulated, and global enterprise customers, moving massive volumes of sensitive machine data to an external environment is simply not an option. Their security, privacy, sovereignty, and compliance requirements often mean that data needs to remain on-premises. The proprietary machine data that holds the greatest potential for generating AI value is the same data holding them back from leveraging AI.

Today at .conf26, I am proud to announce that we are expanding our strategic partnership with NVIDIA to bring accelerated AI capabilities directly to our Splunk Enterprise customers.

With the newest addition to Cisco Secure AI Factory with NVIDIA, Cisco AI POD for Splunk, we are removing the friction between meeting those requirements and putting your operational data to work for the AI-driven insights you need.

With this announcement, Splunk is expanding to on-premises accelerated computing for the first time with NVIDIA — creating a high-performance foundation for agentic AI workloads.

The Strategic Value of Local Execution

As customers move from AI pilots to production, they require infrastructure that supports high-performance model inference at scale without compromising data sovereignty. Cisco AI POD for Splunk makes that possible.

Cisco AI POD for Splunk includes new AI runtime software and Kubernetes-based architecture that powers Splunk AI. Cisco adds the trusted AI infrastructure, while NVIDIA accelerated computing ensures it all runs at machine speed.

The packaged software and compute system is pre-tested, pre-sized, and pre-validated for Splunk AI workloads, wherever your data lives.

Teams that run Splunk in their own data centers can use Splunk AI Assistant for ad-hoc agentic investigations (available now) and build no-code, customized AI agents in Agent Launchpad (coming later this year).

No single model is suited for every task. Access to models with different strengths lets customers apply the right model to each task for better performance and economics. Cisco AI POD for Splunk brings customer choice to AI model selection. Teams can choose from general purpose frontier models or specialized machine data models, without sending data outside their environment.

Customers can choose from a selection of self-hosted models, including the Cisco Deep Time Series Model, Google Gemma 4, and OpenAI GPT-OSS 20B available today, and NVIDIA Nemotron open models in the coming months.

Together, these capabilities help SecOps, ITOps, and NetOps teams anticipate issues earlier, reduce manual investigation, and safely put AI agents to work across their most critical workflows—all while maintaining control of their data, infrastructure, and AI services.

Flexible Paths to Deployment

We recognize every enterprise is on its own AI infrastructure journey. That’s why we give you a choice of deployment: Cisco AI POD for Splunk or use your own infrastructure.

Cisco AI POD for Splunk provides a fast path to deployment with its single-vendor configuration, allowing your teams to bypass the complexity of assembling and validating hardware, GPU, and software dependencies. It provides a reliable foundation that allows you to expand your AI usage as your needs grow.

The Cisco AI POD for Splunk is based on NVIDIA-Certified Systems, with support for NVIDIA AI Enterprise software and NVIDIA Nemotron open models. Cisco AI PODs represent the foundational building blocks of the Cisco Secure AI Factory with NVIDIA and provide a full-stack, modular infrastructure platform for enterprise AI.

For organizations that want to use their own infrastructure, Splunk’s new runtime software and reference architecture are available, serving as a powerful, Kubernetes-native software layer that enables Splunk AI workloads to run on NVIDIA accelerated computing in self-managed environments. Splunk Enterprise customers can now bring governed AI to sensitive machine data on premises, in private clouds, or in air-gapped environments without surrendering deployment control or sacrificing data sovereignty.

Driving Operational Outcomes

This partnership is designed to turn a strong AI foundation into tangible customer value using AI to solve our customers’ operational problems every day. By bringing these models and agents directly to the data on-premises with Splunk’s new runtime AI software and NVIDIA accelerated computing, our customers can expect to:

• Accelerate investigations: Use Splunk AI Assistant in agent mode to reason through complex requests, investigate issues across your local machine data, surface relevant insights, and help move from incident to resolution faster.
• Build governed agentic workflows: Use Agent Launchpad to build, deploy, and review custom agents with human review and approval points as needed to support responsible actions. Invoke agents right from your Splunk workflow.
• Apply the right model to the workload: Choose from supported models for general purpose tasks and forecasting and anomaly detection analytics, rather than relying on a single model for every task.

This brings ad-hoc agentic investigations and custom agent building for a broad variety of use cases including the agentic SOC to teams that run Splunk in their own data centers.

A Partnership Designed for AI

Cisco Secure AI Factory with NVIDIA and Cisco AI POD for Splunk brings the data platform, accelerated computing, and validated infrastructure capabilities together in a common foundation for agentic workflows. The result? Customers can now move from experimenting with AI to scaling it where their data lives, under the controls they require.

As AI continues to evolve, this partnership gives Splunk Enterprise customers a durable path to grow their AI maturity without rethinking how the infrastructure supporting it is deployed in production.

Splunk is also expanding its agentic SOC capabilities with agents and tools that help analysts focus on real threats, detect incidents faster, automate and respond at machine speed, and scale trusted adoption.

Cisco AI POD for Splunk is generally available now.

Learn more about how Cisco AI POD for Splunk can enable your organization to run increasingly sophisticated AI workloads where your machine data already lives here.

Find more information on Cisco Secure AI Factory with NVIDIA here: Cisco Expands Secure AI Factory with NVIDIA for the Rack-Scale Era.