Challenges
More Data Isn’t the Answer. Better Data Is.
At its core, AIOps answers a simple question: what’s really happening?
Simple logs and rule-based monitoring aren’t enough. Cloud platforms, CI/CD pipelines, monitoring tools, and ticketing systems influence downstream business decisions in ways that aren’t always obvious. AI workflows require fast, reliable context to avoid noise, hallucinations, and false positives.
Curated, real-time datasets derived from packet-based network and service interactions create a pipeline observability and security teams can quickly operationalize. Observing system health through network transactions fuels automated responses and predictive operations, with root-cause analysis in seconds.
NETSCOUT can help you cut through the noise and see what’s actually happening across your AI ecosystem.
Network Observability in the Age of AI: How the Game is Changing
Join us for a discussion exploring how network observability is rapidly evolving—and what IT leaders need to do now to prepare for the next wave of operational transformation.
Outcomes that Matter
Why a Strong Data Foundation Matters for AIOps
Reduce Mean Time to Resolution (MTTR)
Automated correlation across network, application, and infrastructure domains helps pinpoint root causes faster. Surfacing gray failures and addressing issues like shadow IT earlier limits escalation and shortens resolution cycles for operations centers.
Improve Service Reliability and Resilience
High-quality telemetry establishes clear baselines that enable AI models to define normal operating conditions. This allows systems to instantly identify latency shifts and anomalous error states, addressing potential impact before it affects uptime.
Cut Through Alert Noise
Superior signal quality filters out false positives created by incomplete telemetry. This eliminates the black box nature of automated alerts by providing the specific context teams need to focus on genuine performance or security threats.
Deploy AIOps Faster and More Efficiently
Curated packet-level metadata provides the context needed to manage high-cardinality data at scale. This approach removes long tuning cycles and the massive cost of storing data for analysis, enabling AIOps to scale across the enterprise.
NETSCOUT’s Solution and How It Delivers Value
Turn Raw Network Traffic into Real-Time, Actionable Intelligence
NETSCOUT’s Omnis® AI Insights delivers intelligence directly from live network traffic. Using deep packet inspection (DPI) at scale, Omnis Sensors inspect traffic at critical network vantage points, while Adaptive Service Intelligence® (ASI) transforms packet activity into NETSCOUT Smart Data, which is protocol-aware, packet-derived metadata that reflects how services communicate.
The Omnis Streamer then makes Smart Data available through two delivery models. AI-ready datasets, called the Omnis AI Feed, can be tailored to specific use cases and streamed to ecosystem partners or stored in repositories like data lakes. The Streamer's built-in Model Context Protocol (MCP) server provides LLMs and AI agents with on-demand access to the same high-fidelity Smart Data, giving teams insight into observed service behavior and dependencies rather than inferred conditions.
By enriching MELT (metrics, events, logs, and traces) with packet-level context, organizations can reduce downstream data volume and storage costs while improving signal quality. The result is faster correlation, accelerated root cause analysis, improved automation, and stronger predictive insights.
NETSCOUT InfiniStreamNG®, CyberStream®, and vSTREAM® customers can use Omnis Sensor Adaptors with their existing deployments.
The Data You Need to Drive Your Business Forward
Accelerate project success, optimize the user experience, and keep operations running smoothly with NETSCOUT’s approach. Make relevant context for every click, every session, and every service available throughout your AI ecosystem.
Hover over each quadrant to see more.
DPI-Enriched Sources
+-Powerful insights from live network traffic in a dataset that supports all applications
Patented metadata generated from NETSCOUT’s deep packet analysis for scale and accuracy
Contextual Value
+-Connects disparate datasets with rich context
Maximize the value of individual datasets
Detect and fix new and existing problems faster and more efficiently
AI-Ready Feeds
+-Out-of-the-box integrations with third-party analytics platforms
Standard export formats
Curated, flexible export feed for quick time to value
Built for AI Workflows
+-Provides runtime access to Smart Data through the built-in MCP server.
Supports AI-driven investigations with protocol-aware network intelligence.
Related Products
Omnis Sensor
Generate real-time, enriched packet-level metadata at the source for real-time analytics and operational scale.
Omnis Streamer
Build Smart Data pipelines and enable on-demand access through the built-in MCP server to improve the efficiency, reliability, and scalability of AI workflows.
InfiniStreamNG Appliance
InfiniStreamNG (ISNG) is NETSCOUT's leading appliance technology, bringing borderless enterprise visibility necessary to manage business services.
vSTREAM Appliances
This virtual appliance extends ASI-based instrumentation to virtualized and cloud infrastructures, delivering the same Smart Data visibility as physical environments.
Omnis CyberStream Network Security Sensor
Providing Visibility Without Borders to Reduce Risk of Cyber Attacks
NETSCOUT Omnis Network Security Solution
A holistic cybersecurity platform for comprehensive network visibility, threat detection, investigation, and response.
Resources
AI data expectations are pushing IT teams beyond broad data collection toward intelligent enrichment.
With Omnis AI Insights in Splunk, organizations gain curated metadata from deep packet inspection to enhance...
Omnis AI Insights and ServiceNow provide real-time, traffic-derived metadata to improve CMDB accuracy, map live...
FAQs
Frequently Asked Questions
What role does Omnis AI Insights play in AIOps?
Omnis AI Insights delivers curated, high-fidelity metadata derived from packet flows, forming a critical data foundation for artificial intelligence for IT operations (AIOps). NETSCOUT Smart Data improves correlation and adds operational context through protocol-level, session-aware insight, supporting advanced analytics, automation, and faster understanding across observability and cybersecurity use cases.
Who owns the data, and is customer data used to train AI models?
Customer data always remains the customer’s data. Omnis AI Insights solutions generate curated datasets that customers can choose to store, index, and integrate within their technology ecosystems. When this data is used with AI or LLM-based systems, it is typically indexed and retrieved at runtime to provide context, not used to modify or retrain model parameters. This retrieval-based approach allows AI systems to be contextually aware while keeping models static and customer data private. NETSCOUT does not use customer data to train shared or public AI models.
What is “curated data?”
In Omnis AI Insights, curated data refers to packet-derived metadata that is filtered, structured, and enriched at the source before it is delivered downstream. Rather than exporting raw packets or unprocessed telemetry, the solution produces protocol-aware, session-level metadata designed for analytics, correlation, and automation. This improves data quality while reducing downstream data volume and complexity.
How does Omnis AI Insights fit into an existing AIOps strategy?
NETSCOUT's Omnis AI Insights solutions provide a high-fidelity data foundation by feeding curated operational metadata directly into an organization’s existing observability and security tools. Beyond acting as a standalone source, Smart Data enriches other telemetry like MELT with packet-level context. Omnis Sensors generate packet-derived telemetry, and the Omnis Streamer allows customers to organize this data into targeted feeds that can be streamed through pipelines or stored in data lakes.
The Streamer's built-in Model Context Protocol (MCP) server also provides LLMs and AI agents with on-demand access to the same Smart Data, supporting AI-driven investigations and automated workflows. This allows local and third-party platforms to correlate disparate data sources with higher precision, improving automation without replacing existing workflows.
How is Smart Data different from MELT?
Metrics, events, logs, and traces describe reported system states and events. Omnis AI Insights adds Smart Data derived from deep packet inspection (DPI). Raw packets are translated into protocol-aware, session-level metadata that shows how applications and services actually communicate across the network. This packet-derived context reveals service dependencies, blind spots, and real operational behavior that MELT data alone often cannot capture.
