NETSCOUT обогатит платформу данными для повышения точности и эффективности корпоративного искусственного интеллекта

NETSCOUT®, a renowned entity in the fields of observability, AIOps, cybersecurity, and DDoS protection, is enhancing its data platform to offer essential operational context for enterprise AI development. This enriched platform is designed to interpret digital interactions, transforming packets into detailed, contextually rich evidence in real time, which is crucial for service assurance, cybersecurity, and AI functionalities. The expansion aims to tackle a significant hurdle in enterprise AI adoption—ineffective operational decisions stemming from incomplete or fragmented data. Traditional metrics like events, logs, and traces (MELT data) still hold value but often lack the context that AI requires to make accurate predictions. These shortcomings result in increased computational demands and the risk of misleading recommendations. Semantic data importance According to Gartner, by 2027, organisations that focus on semantic data for AI will enhance AI accuracy by up to 80% and reduce costs by up to 60%. This highlights the critical need for context as AI systems evolve from providing recommendations to executing autonomous actions. Sanjay Munshi, NETSCOUT's Chief Operating Officer, emphasises, "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." Munshi suggests that NETSCOUT's innovations lead to a more than 25% reduction in AI token consumption Munshi suggests that NETSCOUT's innovations lead to a more than 25% reduction in AI token consumption and over a 75% decrease in mean time to knowledge (MTTK) when compared to using only MELT data. This advancement gives customers improved decision-making confidence and reduces costs associated with AI-driven analysis. Integrating smart data NETSCOUT generates Smart Data through its unique architectural approach, which involves early semantic extraction and context optimisation at the source. This process offers high-density, relevant context for AI systems, ensuring efficient use of computational resources. The resulting AI-ready operational evidence can be integrated into existing workflows, enhancing human operators, analytics platforms, and AI agents. Enhancing operational efficiency The data platform facilitates multiple operational improvements, including: - Equipping NetOps, SecOps, DevOps, Service Reliability Engineering (SRE), and service teams with detailed operational evidence accessible via natural language, thereby accelerating issue resolution. - Optimising token costs by boosting signal density, which reduces unnecessary data processing, thus managing telemetry, storage, token, and computing costs efficiently. - Empowering AI systems with robust, explainable evidence that supports decision-making, governance, and reliable progression from assistive to autonomous operations. Support for cloud and data centre transformation This platform also supports a wide array of applications, including observability, cybersecurity, cloud and data centre transformation, and business-service resilience. It aids in recognising hidden dependencies, differentiating application issues from infrastructure failures, and understanding event impacts across diverse IT landscapes. NETSCOUT's strategy extends the utility of its extensive packet inspection technology by providing a reliable operational intelligence layer for enterprise AI, enhancing AI's ability to operate within existing systems. This advancement could significantly benefit customers by maintaining current workflows while improving visibility and simplifying AI adoption. For technology partners, NETSCOUT offers curated intelligence that strengthens analytics and automation capabilities. NETSCOUT®, a 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, contextualised 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. Semantic representations of data Gartner predicts that by 2027, organisations that prioritise semantics in AI-ready data will increase their agentic AI accuracy by up to 80 percent and reduce costs by up to 60 percent. 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. AI-driven analysis “NETSCOUT turns observed digital interactions into grounded-truth evidence. Through our own internal testing we experienced more than a 25 percent reduction in AI token consumption compared with MELT only data, and more than a 75 percent 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.” IDC expects 80 percent 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. Improving decision confidence 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 optimisation at source: NETSCOUT delivers higher-density, relevant context so AI systems can spend less of their context window and compute budget. AI-ready operational evidence 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. By meeting organisations 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. - Optimise token cost: Reduce the volume of low-value data AI must process by increasing signal density, helping organisations manage telemetry, storage, token, and compute costs without sacrificing the context required to understand service behaviour. - 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. Data centre transformation 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 centre 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, containerised, virtual, and physical environments. 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. Trusted operational intelligence layer 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.