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The explosion of generative artificial intelligence (GenAI) usage has multiplied the challenges for security and the management of hybrid infrastructures. In this context, the California-based company Gigamon has announced the first step of an ambitious multi-year strategy focused on artificial intelligence, aiming to provide AI-powered deep observability for modern cloud environments.
The company, specializing in network visibility and cybersecurity, has introduced two major innovations: AI Traffic Intelligence, a solution that offers real-time visibility into the traffic of generative AI models (LLMs), and GigaVUE-FM Copilot, a generative AI assistant that facilitates the management and configuration of Gigamon deployments.
Both solutions integrate into its Deep Observability Pipeline, enhancing organizations’ ability to detect hidden threats, such as the unauthorized use of AI services — known as shadow AI — and streamline the operation of their hybrid network environments.
Seeing the Invisible: The Silent Threat of Uncontrolled AI
According to the latest Hybrid Cloud Security Survey 2025, conducted by Gigamon with over 1,000 global IT and security leaders, one in three respondents reports that network traffic has doubled due to AI workloads, while 55% admit that their current tools fail to detect modern threats.
“Good intentions are no longer enough. If you can’t see the AI being used in your network, you can’t protect it,” stated Michael Dickman, Gigamon’s product director. “That’s why we’re integrating AI capabilities directly into deep observability: to close blind spots and provide effective governance over these new technologies.”
The AI Traffic Intelligence tool provides detailed visibility into traffic generated by 17 AI engines, such as ChatGPT, Gemini, or DeepSeek, and allows for the monitoring of additional user-defined models. It operates agentless and can function even with encrypted data in transit, enabling the detection of unauthorized AI activities, reducing risks, and optimizing costs.
Key capabilities include:
- Identification of shadow AI (unauthorized AI usage).
- Monitoring of GenAI usage in public, private, virtualized, and containerized environments.
- Real-time telemetry collection from the network for data-driven decisions.
- Policy-based governance over the use of AI models.
GigaVUE-FM Copilot: AI Helping Humans Understand the Network
Additionally, Gigamon has introduced GigaVUE-FM Copilot, a generative AI-based assistant integrated into its management platform. Through a natural language interface, Copilot enables IT and security teams to configure, operate, and troubleshoot their Gigamon environments without the need to be tool experts.
The assistant extracts information from technical guides, documentation, and release notes to provide accurate and contextualized responses. This reduces dependency on level 3 support, accelerates onboarding of new users, and enhances operational efficiency.
Highlighted benefits include:
- Configuration and troubleshooting with conversational AI.
- Instant access to documentation and best practices.
- Improvement in the productivity of multidisciplinary teams (Security, IT, DevOps).
- Facilitating self-service even for non-technical users.
A Long-Term Strategy to Protect AI with AI
Both solutions are just the beginning. Gigamon has confirmed that this is the first step of a multi-year strategy, aimed at providing operational intelligence to its deep observability platform. While AI Traffic Intelligence is already available for GigaVUE Cloud Suite customers, GigaVUE-FM Copilot is in early access, with general availability expected in the second half of 2025.
This announcement comes at a time when the uncontrolled growth of AI is generating new security gaps, and organizations demand tools that offer control without stifling innovation. As noted by Chris Konrad, global cybersecurity vice president at WWT: “AI is accelerating digital transformation, but it also introduces risks and challenges. Gigamon helps regain visibility and control in hybrid cloud environments.”
An Increasing Need: Deep Observability as a Pillar of Modern Security
More and more, deep observability — which combines network data with logs and metrics — is establishing itself as a key component in modern security, especially against AI models that generate opaque and hard-to-track traffic. According to the same study, 88% of IT and security leaders consider this visibility essential to scale and protect GenAI deployments.
In a world where technological innovation advances faster than security teams can monitor it, Gigamon proposes a clear response: more visibility, more automation, and decisions based on reliable data.
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