Gigamon is
advancing its deep observability strategy with new AI-powered capabilities designed to help enterprises manage rising complexity in hybrid cloud environments. The company has launched the first phase of a multi-year initiative that embeds artificial intelligence directly into its
Deep Observability Pipeline, starting with real-time visibility into generative AI traffic and an intelligent assistant for day-to-day platform management.
As organizations adopt generative AI at scale, security and IT teams are seeing exponential growth in network traffic and a sharp rise in blind spots. One of the biggest challenges is identifying where and how AI services are being used, especially when they operate outside sanctioned tools. A recent survey of more than 1,000 global leaders found that nearly a third had seen their network traffic double due to AI workloads, yet more than half said their tools weren’t keeping pace with detection needs.
Shadow AI Visibility: A Growing Requirement in Hybrid Environments
"Shadow AI is emerging as a top concern for security leaders,”
Chaim Mazal, Chief Security Officer at Gigamon told MSSP Alert. “It’s important to understand that shadow AI isn’t necessarily malicious, it just means that it’s not visible. Research shows that a majority of employees are using GenAI already to help them be more successful in their jobs. They are innovating rapidly. So two things are critical for leaders to understand: 1) you must not stifle the innovation, or do so at your own peril, and 2) in order to bring shadow AI into the light, some level of visibility is required.”
To address these issues, Gigamon has introduced AI Traffic Intelligence, which offers real-time telemetry on traffic from 17 leading GenAI and LLM engines, including the ability to track shadow AI activity without agents, even in encrypted environments. This allows organizations to detect unauthorized AI usage, apply governance policies, and manage costs more effectively across public cloud, on-prem, and containerized workloads.
"This is where Gigamon comes in,” Mazal added. “The introduction of AI Traffic Intelligence allows organizations to identify and classify GenAI and LLM traffic, including encrypted flows, without requiring intrusive endpoint agents. This helps uncover shadow AI activity and delivers actionable data to leaders, so they can make more informed decisions around their AI governance policy.”
As a channel-first company, Gigamon is also equipping partners to extend this visibility to their customers. "Messaging and training is being provided to channel partners, so they can be enabled to better advise and guide leaders confronting shadow AI,” Mazal noted. “With technology partners, they are the critical element which makes deep observability shine. For AI Traffic Intelligence in particular, technology partners have the tools necessary to aggregate and provide the proper reporting on shadow AI usage to leaders, based on the Gigamon network telemetry.”
GigaVUE-FM Copilot and What’s Next in Gigamon’s AI Roadmap
Gigamon has also announced
GigaVUE-FM Copilot, a generative AI assistant embedded into its Fabric Manager interface. Built to support onboarding, configuration, and troubleshooting, Copilot uses natural language queries to deliver relevant answers pulled from Gigamon’s documentation and guides. This helps Security, IT, and DevOps teams cut down on time spent navigating support tiers and improves access to operational knowledge regardless of user expertise.
According to Mazal, “This initial capability is aimed at existing Gigamon users, to help improve their own efficiency and effectiveness deploying and managing their visibility infrastructure. The existing support models are not expected to change at this time. Looking ahead, additional AI-based advancements from Gigamon are planned, which could play a more direct role in reducing Tier 3 intervention across complex hybrid deployments.”
The launch also comes as encrypted, ephemeral, and container-based traffic patterns become more prevalent. “We found that one in three Security and IT leaders have seen their network traffic more than double over the last two years due to AI workloads, yet 55 percent believe their current tools aren’t able to detect the evolving threats,” said Mazal. “By embedding AI into our Deep Observability Pipeline, we’re providing agentless, real-time visibility into encrypted and container-based traffic across hybrid environments. Our goal is to give NetOps and InfoSec teams the clarity, context, and control they need to stay ahead of risk and deliver operational excellence in the age of GenAI.”
Gigamon’s design also supports MSSPs managing large, multi-tenant hybrid environments. “Gigamon is built for scale, and handles some of the largest networks in the world, including subscriber networks for tier 1 mobile operators,” Mazal said. “This same ability to scale is equally available to AI Traffic Intelligence, so no matter how many workloads or how many platforms, Gigamon can extract, transform, and provide to technology partner tools for reporting.”
Mazal added that visibility into unsanctioned GenAI usage can directly support stronger policy enforcement and threat detection. “If you can’t see it, you can’t secure it. The Deep Observability Pipeline provides complete visibility into all traffic and workloads, which is critical to the security posture of any organization that is sufficiently advanced in cybersecurity. AI Traffic Intelligence plays a secondary role in threat detection, it can identify traffic to unsanctioned LLMs outside of the security safe zone, such as DeepSeek, TenCent, and Baidu, and allows for customer signatures to target additional LLMs.”
