Events, RSAC Conference, RSAC Conference Features
Vice President Singer discusses how custom AI models improve security, cost, and control
Matthew J. Schwartz (euro infosec) •
March 24, 2026
General purpose artificial intelligence models were not built for cybersecurity, and that gap is emerging. Yaron Singer, vice president of AI and security at Cisco’s Foundation AI, says organizations using large off-the-shelf models face three compounding challenges: limited customizability, data privacy risks, and the prohibitive cost of running oversized models on narrow, specific tasks.
The answer lies not in scaling up the model, but in building it correctly. Smaller models trained solely on cybersecurity data are domain-specific in nature, can be fine-tuned for precise tasks, and can be deployed without routing sensitive data to external providers. The combination of control, privacy, and cost efficiency makes it better suited for security operations than general-purpose alternatives.
“By deploying the model and the technology associated with it, we’re basically helping to free up some of the analyst’s capacity, because what would normally take a month can now be done in 10 minutes. That’s a game-changer,” Singer said.
This video interview features an interview with Information Security Media Group. At RSAC Conference 2026, Singer also discussed:
- How Foundation AI’s expanding portfolio of base, inference, and enterprise search models enables the development of agent security systems.
- The role of AI agents in SOC environments and workflow automation.
- Why production AI systems require ongoing maintenance as models age and APIs evolve.
Singer leads AI and engineering for Cisco Security’s Foundation AI team, driving secure AI technology across Cisco and Splunk. Previously, he co-founded Robust Intelligence, an AI security pioneer acquired by Cisco, where he built deep expertise across AI, machine learning, cybersecurity, and applied research.
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