NETSCOUT SYSTEMS, 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 per cent and reduce costs by up to 60 per cent. 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 per cent reduction in AI token consumption compared with MELT-only data, and more than a 75 per cent 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.”