NETSCOUT SYSTEMS INCNTCT

NETSCOUT patent portfolio includes technologies for AI and machine learning-driven analytics and agentic AI workloads.

“In June, NETSCOUT was awarded its 750th patent for “Systems and Methods for Performing Computer Network Service Chain Analysis.” The patent portfolio covers a broad spectrum of technologies, including packet capture and real-time analysis at carrier and enterprise scale; DDoS attack detection, classification, and automated mitigation; mobile network performance monitoring, 5G service assurance, and radio access network observability; network detection and response; artificial intelligence and machine learning-driven analytics; adaptive threat detection; and smart data that is primed for AI and agentic AI workloads.”

NETSCOUT extended Omnis AI Insights to enable CSPs to deploy agentic AI.

“In February, the Company announced the extension of its Omnis™ AI Insights solution to communications service providers (CSPs), delivering a critical data foundation for the adoption of agentic AI across customer experience and network operations. By transforming raw network data into AI-ready smart data, CSPs can deploy AI agents to enhance customer experience, enable predictive maintenance, and strengthen network security, driving greater operational efficiency, lower costs, and reduced risk.”

NETSCOUT deployed its Omnis AI Insights Solution with AI agents at TM Forum event for self-healing telecom operations.

“▪ In early July, NETSCOUT announced its recent participation in TM Forum’s NeuroNOC Catalyst event that explored how AI agents, closed-loop automation, and high-quality network data can enable self-healing operations across telecom environments. NETSCOUT deployed its Omnis AI Insights Solution at the event, consisting of Omnis AI Sensor and Omnis AI Streamer, to deliver 5G Standalone Radio Access Network (SA RAN) and Packet Core high-fidelity telemetry essential for effective AI-driven operations. The solution enabled network operations center engineers to identify subscriber registration issues, pinpoint the root cause via a curated large language model, and execute remediation steps with minimal manual effort. The key takeaway was strong empirical validation of the need for high-quality curated data to drive effective AI solutions.”