NETSCOUT DATA PLATFORM TRANSFORMS AI-DRIVEN NETWORK OPERATIONS AND CONTROLS COSTS

(MENAFN- Active DMC) Dubai, UAE., September 8, 2026 – NETSCOUT® SYSTEMS, INC. (NASDAQ: NTCT), a leading provider of observability, AIOps, cybersecurity, and DDoS attack protection solutions, expands its data platform to provide the trusted operational context required to build the foundation for enterprise AI. The NETSCOUT data platform observes digital interactions, converts packets into high-fidelity, compact, contextualized evidence in real time, and curates that evidence at scale for observability, service assurance, cybersecurity, and AI.

This addresses a growing barrier to enterprise AI adoption: increasingly capable models still cannot deliver reliable operational decisions when the data supplied to them is incomplete, noisy, fragmented, or stripped of context. Traditional metrics, events, logs, and traces (MELT data) remain important, but often require AI systems to reconstruct what happened after telemetry has been sampled, aggregated, or separated across tools. That increases inference, compute requirements, token consumption, and the risk of an inaccurate recommendation.

Gartner predicts that by 2027, organizations that prioritize semantics in AI-ready data will increase their agentic AI accuracy by up to 80% and reduce costs by up to 60%. Agentic AI outcomes depend on context, including semantic representations of data. The need for trusted context becomes even more consequential as AI agents progress from advising operators to taking autonomous action.

"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. “NETSCOUT turns observed digital interactions into grounded-truth evidence. Through our own internal testing we experienced more than a 25% reduction in AI token consumption compared with MELT only data, and more than a 75% reduction in 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.”

Architecture Designed for the AI Era

IDC expects 80% of agentic AI use cases will require real time, contextual, and widely accessible data and states that the goal is to create a trusted, real time data environment where AI can reason, decide, and act with the right context and guardrails. NETSCOUT produces Smart Data via a unique architectural approach, bringing together two complementary capabilities that improve context engineering:
•Early semantic extraction: NETSCOUT derives operational meaning from packets at the point of observation, preserving evidence that can disappear in conventional datasets.
•Context optimization at source: NETSCOUT delivers higher-density, relevant context so AI systems can spend less of their context window and compute budget.
Together, these capabilities deliver an AI-ready operational evidence layer that complements existing observability investments and can support human operators, analytics platforms, large language models, copilots, and AI agents. Smart Data is embedded across NETSCOUT solutions and can be integrated into enterprise data and AI workflows, enabling customers to use the operational intelligence within their chosen technology ecosystems.

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