Cisco Data Fabric AI Observability – Defend at Machine Speed

Tapan Shah of Splunk explained how Cisco aims to close the trust gap in agentic AI by unifying observability and security on the Cisco Data Fabric. He covered federated data, correlation at scale, and agent observability capabilities for evaluations, tracing, tokenomics, and guardrails. He then demonstrated catching and correcting an AI support agent that issued an unauthorized refund. The session opened the defend at machine speed portion of Cisco Security’s appearance at Security Field Day 16.

Shah framed the problem around non-deterministic agent behavior, weak governance, and rising AI costs. He gave the example of an agent that leaves 15 servers unpatched with no explanation. Security can show which files were deleted and by whom, but only observability reveals whether a human or a “digital employee” acted, and why. The Cisco Data Fabric, powered by Splunk, ingests structured and unstructured data from any vendor. It can also federate queries to data where it resides rather than requiring centralization. On top of it, agent observability uses OpenTelemetry data to evaluate responses for hallucination, toxicity, bias, and relevance. It traces spans between agents and LLMs, tracks token costs with optimization suggestions, and enforces guardrails. It can be deployed in the cloud or on-premises.

Shah argued that guardrails matter because a crafted prompt can push agents into behavior they were never meant to allow. He cited an airline agent that granted him a $7,000 refund outside its 24-hour window. In a demo with Voltway, a fictitious retailer, a support agent issued a $1,200 refund against a $699.98 receipt. Inside Cisco Cloud Control, tracing flagged poor instruction adherence and correlated 15 signals, 13 of which affected five sessions tied to incorrect refund confirmations. The platform recommended a refund compliance policy automatically, without the user configuring it. Once enabled, the policy ran as a guardrail before the refund step, and the agent refunded the correct amount. Shah said the goal is not only to flag bad agent behavior but to improve it.