NetScout Systems. Inc. - Experts & Thought Leaders

NETSCOUT, a provider of network observability, AIOps, cybersecurity, and DDoS attack protection solutions, announces nGenius® Copilot, an AI-powered conversational extension to nGeniusONE® that provides intuitive access to NETSCOUT® Smart Data. It gives more people access to Smart Data for easier and faster ways to investigate service disruptions and assess business impact. nGenius Copilot is grounded in NETSCOUT Smart Data, the high-fidelity, contextual data derived from observed network, application, service, and user interactions. By transforming natural language questions into evidence-based answers, relevant visualisations, supporting evidence, and suggested follow-up inquiries, it helps experienced engineers accelerate complex investigations while enabling a broader range of professionals to benefit from advanced operational intelligence. Advanced operational intelligence As hybrid environments and digital dependencies become more complex, resolving service disruptions requires IT teams to interpret growing volumes of operational data and connect evidence across applications, infrastructure, networks, and external services. nGenius Copilot simplifies that process, transforming natural-language questions into clear, evidence-based answers grounded in NETSCOUT Smart Data, helping more members of an organization identify contributing conditions, assess business impact, and act faster. “Reliable AI starts with reliable data,” said Phil Gray, AVP, product management, NETSCOUT. “nGenius Copilot combines an intuitive conversational experience with the depth and context of NETSCOUT Smart Data. It helps teams quickly uncover relevant evidence, understand contributing conditions and business impact, and collaborate around a consistent view of what occurred. Ultimately, it enables customers to realize greater value from the NETSCOUT intelligence they already have.” Natural-language questions Simple direct questions can be asked, such as: which applications are responding slowly or experiencing increased latency? Which users, locations, or services are affected? What network, application, server, or external conditions may be contributing to the problem? nGenius Copilot interprets the question, applies the relevant Smart Data, and presents the resulting analysis in clear language with visual context and supporting evidence. nGenius Copilot is available for purchase now and helps organizations: Accelerate problem identification: Guide investigations from an initial symptom to the applications, services, network conditions, and dependencies contributing to the issue Prioritise business impact: Show which users, locations, services, and transactions are affected so teams can focus resources where they matter most Scale operational expertise: Make Smart Data accessible through natural-language questions, guided analysis, and clear explanations while helping senior engineers investigate complex problems more efficiently Improve cross-team collaboration: Translate detailed technical evidence into clear explanations and visualisations that network, application, infrastructure, service, and business teams can use together Enhance the value of NETSCOUT deployments: Bring Smart Data directly into everyday workflows and enable more professionals to benefit from the data their organizations already collect nGenius Copilot supports NETSCOUT’s commitment to make its packet-derived Smart Data more accessible and easier to use where operational decisions are made. It lets people ask questions in natural language, examine supporting evidence, and identify the conditions affecting services and users. For customers, that means a faster path from disruption to resolution. For NETSCOUT, it extends the value of its data into AI-assisted operations.

NETSCOUT SYSTEMS, INC., a provider of observability, AIOps, cybersecurity, and DDoS attack protection solutions, announces Model Context Protocol (MCP) connectivity for its Omnis™ AI Insights solution. The new capability gives AI assistants and agents on-demand access to AI-ready Smart Data, NETSCOUT’s real-time operational evidence, providing the trusted context they need to support more accurate and informed decisions. AI-ready Smart Data builds on NETSCOUT’s patented Adaptive Service Intelligence™ (ASI) technology, using granular data to provide a richer, more scalable source of contextual network intelligence. Enabling network infrastructure NETSCOUT performs early semantic extraction and context optimization at source, transforming ASI data into compact, AI-ready Smart Data before it enters downstream systems. Omnis Sensor and Omnis Streamer are key components of NETSCOUT’s Omnis AI Insights solution, moving intelligence closer to the source of the data and enabling network infrastructure to evolve from simply producing telemetry to delivering contextual, AI-ready network intelligence. Omnis Sensor performs early semantic extraction at critical network vantage points to represent application, service, transaction, and behavioral context within ASI in real time at the source. It generates essential metadata while preserving operational meaning at the point of observation. This compact, high-fidelity evidence gives AI models trusted operational context for more accurate, efficient, and explainable decisions Omnis Streamer collects and curates AI-Ready Smart Data for downstream use. Customisable playbooks shape the data for any domain, operational requirement or use case, with healthcare, financial services, and telecommunications service provider environments among the available templates. It delivers those datasets through platform integrations or on demand to AI assistants and agents through its new, built-in MCP server Increasingly autonomous operations Enriching network data before it reaches an AI model reduces the volume, cost, and complexity of processing raw telemetry while giving AIOps, observability, security, and analytics systems more meaningful evidence for faster, more reliable decisions and increasingly autonomous operations. Omnis AI Insights provides organizations with a ground truth, evidentiary view of operations that AI agents and assistants require for trusted autonomous action: On-demand access for AI: MCP connectivity gives AI assistants, AI models, and agents relevant AI-Ready Smart Data at run time. NETSCOUT-provided tools guide them to the evidence that matters and help them interpret it Direct platform integration: Ingestion of Smart Data via integration by Splunk (https://www.netscout.com/technology-partners/splunk), ELK Stack, Datadog, ServiceNow (https://www.netscout.com/technology-partners/servicenow), Dynatrace and others Investment protection: Omnis Sensor Adaptors help customers add these capabilities to their existing NETSCOUT infrastructure Application monitoring tools “Everyone knows there is no value to conclusions that cannot be trusted,” said Phil Gray, AVP, product management, NETSCOUT. “By adding MCP tools alongside our existing Kafka streaming capabilities, Omnis AI Insights gives IT professionals the flexibility to feed AI-ready Smart Data into analytics and AI platforms at scale and cost effectively, while also making that same context-rich intelligence directly accessible to Models and Agents. This helps organizations power AI with a compact, curated, trusted source of network truth rather than fragmented operational signals that suffer from hallucinations and high token spends.” In a live NETSCOUT deployment, conventional application monitoring tools indicated no application errors and nothing to investigate, while underlying network conditions degraded the user experience. NETSCOUT Smart Data preserved exactly what happened across the network, including the minimum window size, total retransmit count, and zero-window event count, allowing AI to verify facts rather than infer reality. Governed autonomous operations NETSCOUT AI-ready Smart Data helps customers reach accurate answers faster, reduce token and infrastructure costs, and advance toward governed autonomous operations with greater confidence. It also extends the value of existing NETSCOUT investments while providing a differentiated data foundation for future AI innovation. These new capabilities in the Omnis AI Insights solution put trusted operational context to work across AI, analytics, observability, service assurance, security, and data lake environments without re-platforming or building new data pipelines.

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, organizations 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 optimization 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 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 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.