NETSCOUT Expands Data Platform to Support AI-Driven Network Operations

As enterprises increasingly deploy artificial intelligence across IT operations, the quality and context of the data fed into AI systems is becoming critical to both performance and cost. NETSCOUT Systems, Inc. (NASDAQ: NTCT) is expanding its data platform to provide what it describes as a trusted operational evidence layer for enterprise AI, observability, AIOps, cybersecurity and service assurance.

The NETSCOUT data platform observes digital interactions and converts network packets into compact, contextualised evidence in real time. By retaining operational context at the point of observation, the company aims to give AI systems higher-quality information without requiring them to reconstruct events from fragmented telemetry.

The move addresses a growing challenge for enterprises adopting AI for operational decision-making. Traditional metrics, events, logs and traces (MELT) remain important sources of information, but telemetry is often sampled, aggregated or distributed across multiple platforms. AI systems may then need to piece together what happened before they can determine what action to take.

That additional processing can increase inference and compute requirements, consume more AI tokens and potentially affect the accuracy of recommendations.

Research from Gartner suggests that organisations prioritising semantics in AI-ready data could improve agentic AI accuracy by up to 80% and reduce costs by up to 60% by 2027. The importance of contextual data is expected to increase further as AI systems evolve from assisting human operators to making decisions and taking actions autonomously.

“Unlocking the benefits of AI across the enterprise will not be achieved by adding another model. It will succeed through context engineering: giving AI the right operational context before reasoning begins,” said Sanjay Munshi, Chief Operating Officer, NETSCOUT.

According to Munshi, NETSCOUT’s internal testing showed more than a 25% reduction in AI token consumption compared with using MELT-only data, along with more than a 75% reduction in mean time to knowledge (MTTK). “Compact, context-rich operational intelligence helps our customers improve decision confidence, lower the cost of AI-driven analysis, and establish the control required to move from AIOps recommendations toward safe, autonomous operations,” he added.

Building an AI-Ready Data Layer
The expanded platform is designed around two capabilities that NETSCOUT says can improve the quality and efficiency of operational data supplied to AI systems. The first is early semantic extraction, through which NETSCOUT derives operational meaning from network packets at the point of observation. This preserves information that may otherwise be lost through conventional telemetry processing.

The second is context optimisation at source, which delivers a more concentrated set of relevant operational information. By increasing the density of useful context, NETSCOUT aims to reduce the amount of data AI systems need to process while retaining the information required to understand service behaviour.

Together, the capabilities create an operational evidence layer that can complement existing observability environments and feed human operators, analytics platforms, large language models, copilots and AI agents.

Smart Data is embedded across NETSCOUT’s solutions and can also be integrated into existing enterprise data and AI workflows, allowing organisations to use network-derived operational intelligence within their existing technology environments.

Supporting Enterprises Across the AI Maturity Curve
NETSCOUT’s approach is designed to support organisations at different stages of their AI and operational transformation. For NetOps, SecOps, DevOps, SRE and service teams, the platform can provide access to detailed operational evidence through natural-language interfaces, potentially helping teams investigate incidents and identify root causes more quickly.

The company also sees data efficiency as an important consideration as AI adoption expands. Reducing low-value information while increasing the density of relevant context can help organisations manage the growing costs associated with telemetry, storage, AI tokens and compute.

For organisations progressing towards autonomous operations, independently observed and explainable network evidence can provide an additional foundation for AI-driven recommendations, governance and auditability before automated actions are introduced. The same operational context can support use cases spanning observability, cybersecurity, service assurance, cloud and data centre transformation, and business-service resilience.

By providing visibility across hybrid, multi-cloud, containerised, virtual and physical environments, NETSCOUT says its platform can help teams and AI systems identify hidden dependencies, distinguish infrastructure problems from application issues, detect protocol and security exposures, and assess the operational impact of individual events.

Data Quality Becomes Critical as AI Adoption Accelerates
The expansion comes as enterprises increasingly move from AI experimentation towards operational deployment. IDC expects 80% of agentic AI use cases to require real-time, contextual and broadly accessible data, reinforcing the importance of data architectures capable of supplying AI systems with reliable information when decisions need to be made.

For NETSCOUT, this represents an extension of its established deep-packet inspection technology into the growing market for AI-driven operations. As access to AI models becomes increasingly widespread and differences between models narrow, the quality, completeness and efficiency of the data provided to those models could become a more important differentiator.

NETSCOUT’s strategy is therefore focused not simply on providing another AI model, but on supplying the operational context that models and AI agents require to make informed decisions. For enterprises, the approach offers a way to introduce AI into existing operational environments without abandoning established workflows or visibility. For technology partners, the network-derived intelligence can provide an additional source of contextual information for analytics and automation.

As enterprises move from AI-assisted operations towards increasingly autonomous IT environments, the ability to provide trusted, real-time and context-rich operational data could become as important as the AI models themselves.