NETSCOUT Unveils Data Platform to Transform AI Network Operations
TECHWORLD ·
✦ AI Summary
NETSCOUT said on the 15th that it has unveiled the "NETSCOUT data platform."
The platform analyzes enterprise-wide network packets in real time and delivers them as smart data containing operational context.
The company said the solution complements the limits of MELT data and supports operational intelligence and automation by creating AI-ready data.
NETSCOUT said on the 15th that it has unveiled the "NETSCOUT data platform." The launch is designed to help reduce the cost of AI-based network operations.
The platform can analyze enterprise-wide network packets in real time. The analysis results are delivered as smart data for operational context. Smart data is built by combining Early Semantic Extraction and Context Optimization at Source.
The platform treats all Digital Interactions, transactions, and user experiences as objects of observation. It also provides the ability to transform network packets into high-precision, compact, and contextualized evidence. This enables large-scale screening and management for Observability, SA, cybersecurity, and AI purposes.
The solution focuses on changing how operational data is used to produce high-quality AI-ready data in a form that AI can use. The supporting capabilities cited include natural-language access to operational evidence, reducing the amount of data used for AI processing value, improving signal density, and automation based on high-precision real-time operational intelligence.
The company said existing MELT (Metrics, Events, Logs, Traces) data has characteristics of sampling, aggregation, and distribution, so it needs to be reconstructed. It also said reconstructing existing MELT (Metrics, Events, Logs, Traces) data can increase inference usage, computing usage, and token usage, while also raising the risk of generating inaccurate recommendations.
In response, the company emphasized that the solution is a large-scale DPI and source-based analysis utilization solution. It added that the solution complements the limitations of the existing MELT (Metrics, Events, Logs, Traces) approach and provides the foundation for turning real network observability activities into high-quality AI-ready data, helping enterprises quickly identify problems, make accurate decisions, and efficiently scale AI-based operations.
Sanjay Munshi, NETSCOUT COO, said that realizing AI benefits across the enterprise is not possible by simply adding more models. He added that it can be achieved through Context Engineering, which provides the right operational context before inference begins.
NETSCOUT said its technology converts observed Digital Interactions into Grounded-truth Evidence. It added that in internal tests, compared with using MELT data alone, AI token usage was reduced by more than 25%, while MTTK also fell by more than 75%. Based on this, NETSCOUT said it can help operational intelligence increase customer confidence in decision-making, reduce the cost of AI-based analysis, and secure the control needed to move from AIOps recommendations to safe autonomous operations.
Source: TECHWORLD · Kim Hye-jin
Original: https://www.epnc.co.kr/news/articleView.html?idxno=406947
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Source: TECHWORLD
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