Smart Data dramatically improves MTTK and reduces AI token consumption helping organizations accelerate and control AI-driven operations as they advance toward autonomous IT

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.

Value Across the AI Maturity Curve

By meeting organizations wherever they are in their operational and AI transformation, NETSCOUT’s data platform helps customers:

• Operate more productively: Equip NetOps, SecOps, DevOps, SRE, and service teams with natural-language access to detailed operational evidence, accelerating investigation and helping resolve issues faster.
• Optimize token cost: Reduce the volume of low-value data AI must process by increasing signal density, helping organizations manage telemetry, storage, token, and compute costs without sacrificing the context required to understand service behavior.
• Automate with greater confidence: Provide AI systems with independently observed, explainable evidence to support recommendations, governance, auditability, and the controlled progression from assisted operations to agentic action.

This common foundation provides AI systems with evidence-based operational context suitable for governed automation and agentic workflows, and supports observability, cybersecurity, service assurance, cloud and data center transformation, and business-service resilience. It can help teams and agents expose hidden dependencies, distinguish infrastructure failures from application issues, identify protocol and security exposures, and understand the operational impact of an event across hybrid, multi-cloud, containerized, virtual, and physical environments.

Strategic Relevance for a Rapidly Evolving Market

As access to AI models broadens and model capabilities converge, the quality, completeness, and token efficiency of the context supplied to those models becomes a more lasting source of differentiation. NETSCOUT extends the value of its core deep packet inspection-at-scale technology into a new growth arena: providing the trusted operational intelligence layer for enterprise AI and automation.

For customers, this means a path to adopt AI without abandoning existing workflows or compromising visibility. For technology partners, it creates a source of curated, network-derived intelligence that can strengthen analytics and automation. For others, it demonstrates how NETSCOUT can apply its differentiated data foundation across observability, cybersecurity, AIOps, and AgenticOps.

Resources and Attributions:

• Learn more about NETSCOUT’s data platform at https://www.netscout.com/platform
• What Is AI-Ready Data? And How to Get Yours There, Rita Sallam. https://www.gartner.com/en/articles/ai-ready-data
• Gartner Press Release, “Gartner Says Lack of Semantics Causes Inaccurate AI Agents and Wasted Spending,” May 11, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-05-11-gartner-says-lack-of-semantics-causes-inaccurate-artificial-intelligence-agents-and-wasted-spending
• AI can’t run on stale data: Why enterprises are rethinking their architecture, March 24, 2026, Devin Pratt. https://www.idc.com/resource-center/blog/ai-cant-run-on-stale-data-why-enterprises-are-rethinking-their-architecture/

About NETSCOUT

NETSCOUT SYSTEMS, INC. (NASDAQ: NTCT) protects the connected world from cyberattacks and performance and availability disruptions through its unique visibility platform and solutions powered by its pioneering deep packet inspection at scale technology. As a leading provider of network observability, AIOps, carrier service assurance, cybersecurity, and Distributed Denial-of-Service (DDoS) attack protection solutions, NETSCOUT serves the world’s largest enterprises, service providers, and public sector organizations. Learn more at www.netscout.com or follow @NETSCOUT on LinkedIn, X, or Facebook.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Securities Act of 1933 and the Securities Exchange Act of 1934, which are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995 and other federal securities laws, including statements regarding expected benefits, capabilities, adoption, and market opportunities associated with NETSCOUT's products and technologies. Actual results may differ materially from those indicated in the forward-looking statements due to various important factors which are discussed in the section entitled “Risk Factors” in NETSCOUT’s most recent periodic report filed with the U.S. Securities and Exchange Commission (“SEC”) as well as discussions of potential risks, uncertainties, and other important factors in NETSCOUT’s subsequent filings with the SEC. All information in this press release is as of the date of this press release, and NETSCOUT undertakes no obligation to update such information unless required by law.

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