DENVER, September 15 — Cisco used its Splunk .conf stage in Denver to announce a set of Splunk innovations aimed at helping enterprises scale AI securely and cost-efficiently, headlined by an expanded partnership with NVIDIA to bring Splunk AI to on-premises customers. The centerpiece is Cisco AI POD for Splunk, the newest addition to Cisco Secure AI Factory with NVIDIA, a reference architecture built from Cisco AI PODs.
According to the announcement, it is available today and combines new AI runtime software, Cisco infrastructure, NVIDIA accelerated computing, and a Kubernetes-based architecture, pre-validated and optimized for Splunk AI workloads.
Built for data that can’t leave the building
Here’s what it means in plain terms. Think of it like a fully stocked workshop that arrives already assembled, wired, and tested, so a company doesn’t have to figure out which tools fit together before it can start building. Cisco says the POD is designed for customers who cannot move sensitive data to the cloud, extending self-managed AI to Splunk Enterprise customers across on-premises, private cloud, and air-gapped environments.
Partners Accenture, bitsIO, Wipro, and World Wide Technology are ready on day one to help customers stand it up on their own infrastructure. Splunk AI Assistant is available now, and Agent Launchpad is coming later this year.
Both run on this layer and are intended to bring ad-hoc agentic investigations and custom agent building, including for the agentic security operations center, to teams running Splunk in their own data centers.
Pick your model, keep your data
Customers can also self-host a selection of open and proprietary generative AI models for Splunk Enterprise workloads, including the Cisco Deep Time Series Model, Google Gemma 4, and OpenAI GPT-OSS 20B, with NVIDIA Nemotron open models in the coming months. The practical upshot is that teams can use the model best suited to the job without sending data outside their environment.
Jeetu Patel, President and Chief Product Officer at Cisco, said one of the biggest roadblocks to enterprise AI today is that it is too hard to deploy, and that running Splunk AI on infrastructure customers already trust lets them move faster with confidence and control.
Justin Boitano, Vice President of Enterprise AI at NVIDIA, said enterprises need to bring AI where their data lives, especially when security and sovereignty requirements require critical workloads to stay on-premises, and that enabling Splunk AI workloads to run with NVIDIA Nemotron open models on NVIDIA accelerated computing brings AI agents directly to Splunk.
Watching the bill — and the threats
Separately, Cisco announced a new Tokenomics solution in Splunk Agent Observability that tracks AI spending and coding agent usage in real time, tying adoption to business value. Splunk also introduced security innovations intended to give teams a faster, safer path to scaling the agentic security operations center, and Splunk and AWS formalized a multi-year agreement to co-develop security solutions built to outpace AI-driven attacks.
For enterprises weighing how much of their AI stack to keep in-house, the coming months will show whether pre-validated, on-premises systems like Cisco AI POD for Splunk become the default path for sensitive workloads — and whether the arrival of Agent Launchpad and additional Nemotron models widens the set of jobs teams are willing to hand to agents inside their own walls.
