NetScout is stepping up to enhance enterprise infrastructure visibility through a smart data-driven observability strategy. The core of the initiative is to convert network traffic into metadata that AI can utilize, connecting performance management, security analytics, and AIOps into a single operational flow.

NetScout announced a strategy to enhance infrastructure visibility using Smart Data in Seoul on the 27th. The company explained that it will support collaboration among DevOps, NetOps, SecOps, and ITOps organizations by precisely analyzing network and application data in an increasingly complex digital infrastructure environment.

Smart data is the transformation of raw network traffic into multidimensional data that can be utilized for operational and security analysis. It is characterized by structured information designed to identify service performance, user experience, application behavior, and signs of network anomalies simultaneously.

Existing observability has been centered around log, metric, and trace data. However, while logs are strong for analyzing individual events, it is difficult to grasp the overall flow, and although metrics show numerical changes, they have limitations in root cause analysis. Trace also describes the call relationships between systems, but it does not show the entire actual state of the network.

NetScout utilizes Adaptive Service Intelligence (ASI) technology to complement this limitation. ASI analyzes packet data to extract both quantitative metrics and contextual information, and processes them into a smart data format that is easy for AI and automation systems to utilize.

This data is applied to the integrated monitoring solution 'nGeniusONE' and the network detection and response solution 'Omnis Cyber Intelligence (OCI)'. nGeniusONE supports the identification of network blind spots and shadow IT, while OCI is utilized in conjunction with SIEM tools to identify abnormal network behavior and potential threats.

NetScout also emphasized that smart data can be integrated with existing operational platforms such as Splunk, Kafka, and ServiceNow. Through this, companies can expand the scope of data utilization while maintaining their existing analytics environments, and improve the accuracy of AIOps-based operational automation by keeping service dependency maps and CMDBs up to date.