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Cisco Unveils Frontier AI Red Teaming and Agent Observability to Secure the Agentic Enterprise

Monday, November 9, 2026 · Technology · GSM News

Cisco (NASDAQ: CSCO) announced on Nov. 9 at Cisco Live APJC in Melbourne, Australia, that it is adding Anthropic's Claude Mythos to AI Defense for red teaming AI applications and agents, empowering defenders to stress-test AI systems with frontier adversary reasoning.

Coupled with new Splunk Agent Observability and Cisco IQ advancements, Cisco said it is preparing enterprises for a new era of trust that requires securing, observing, and governing agents at machine speed. Cisco IQ, which leverages decades of vulnerability and troubleshooting data, now correlates live threat detection data with potentially exposed network infrastructure at machine speed to accelerate remediation, and enhanced Tokenomics connects agent cost directly with ROI in real time.

"You cannot scale what you cannot secure, govern, or observe," said Cisco President and Chief Product Officer Jeetu Patel. "By uniting frontier-level AI runtime protections with machine-speed agent quality controls, we are building a continuous trust loop: protecting agents from compromise while ensuring every token spent delivers measurable business value."

Cisco cited a global Splunk research report from last month in which 81% of ITOps and engineering professionals cited increased cooperation between security and observability teams. Since Cisco launched AI Defense in January 2025, it has helped hundreds of customers identify critical flaws in AI models, applications, and agents before they reach production; its adaptive red teaming allows security teams to quickly build customized, multi-step attacks on target systems to uncover specific risks, and Claude Mythos will augment this framework with advanced reasoning.

(Source: Cisco)

Source: Cisco

Published by GSM · USA (original Chinese edition)

Photo: 图:位于 San Jose 的思科总部园区。CC0,经 Wikimedia Commons。 / Photo: The Cisco main campus in San Jose. CC0, via Wikimedia Commons.

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