451 Research: Gigamon Fuses Network Telemetry with Agentic AI to Accelerate Threat Detection

AI accelerates threat detection by analyzing trusted network-derived telemetry to surface suspicious behavior, add context to alerts and guide faster investigations. In the report, Gigamon Insights is positioned as an agentic AI application that helps teams ask questions, analyze telemetry and receive context-rich answers within existing workflows.

Network-derived telemetry gives organizations visibility into traffic across physical, virtual, cloud and container environments. According to 451 Research, this closes blind spots that log-centric tools miss, including encrypted East-West traffic, application-layer metadata and behavior tied to lateral movement and command-and-control activity.

Gigamon combines trusted network data from the Deep Observability Pipeline with agentic AI through Gigamon Insights. Analysts can ask natural language questions, use predefined prompts or run free-form queries to investigate issues and receive guidance directly within SIEM, observability and cloud workflows.

Network-derived telemetry helps uncover encrypted traffic threats, East-West lateral movement, command-and-control activity, application-layer behavior and anomalous network patterns that log-based tools may miss. The report specifically highlights visibility into encrypted East-West traffic and network behavior as a key advantage.

The report notes that traditional log-based SIEM tools struggle to detect lateral movement and encrypted threats operating below the application layer. Network-derived telemetry complements those tools with additional context that improves AI-driven detection, investigations and operational visibility.

Gigamon Insights integrates with leading platforms including Elastic Security, Splunk, AWS and Google Cloud services. Rather than replacing existing tools, Gigamon is positioned as an enabler that enriches current security and observability workflows with trusted network-derived telemetry.

Network-derived telemetry is enriched data created from network traffic across physical, virtual, cloud and container environments. The Deep Observability Pipeline transforms, enriches and optimizes this telemetry before delivering it to security, cloud and observability tools.

Deep observability turns raw traffic into trusted, actionable telemetry that AI can analyze more precisely. The report says Gigamon Insights is built on Application Metadata Intelligence, which extracts close to 6,000 traffic-protocol and application-related metadata attributes to support detection of threats, anomalies and performance issues.

Gigamon AI Traffic Intelligence identifies and classifies traffic from more than 40 generative AI and LLM engines without deploying agents. This gives organizations visibility into sanctioned and unsanctioned AI usage across public, private, virtual and container environments to support governance and spending optimization.

451 Research notes that Zero Trust and microsegmentation require continuous validation of East-West and encrypted traffic flows. Network-derived telemetry supports this by exposing communications and behaviors that endpoint-centric approaches may miss.

The report indicates that combining AI with network telemetry helps organizations accelerate threat detection and troubleshooting, reduce investigation time and improve visibility into encrypted and East-West traffic. It also strengthens AI governance and increases the value of existing security and observability investments.

As more enterprise traffic becomes encrypted, attackers can exploit gaps in visibility. 451 Research emphasizes that network-derived telemetry provides an advantage by exposing behaviors and metadata that help detect lateral movement, command-and-control activity and other threats log-centric tools may miss.

Gigamon Insights uses a flexible LLM architecture, with support for private hosted models in the future and bring-your-own enterprise LLMs so organizations maintain full data privacy and control. The report notes this addresses data residency and sovereignty requirements critical for regulated industries such as financial services and healthcare.