NETSCOUT®, known for offering observability, AIOps, cybersecurity, and DDoS protection solutions, has expanded its data platform to build a robust foundation for enterprise AI.
This platform converts digital interactions into high-fidelity, contextualized evidence in real-time, which is crucial for observability, service assurance, cybersecurity, and artificial intelligence. The platform is designed to address a major challenge faced by enterprise AI: the need for complete, noise-free, and contextual data to enable reliable operational decisions.
Semantic Data Representations
As organizations increasingly rely on AI, Gartner predicts that by 2027 those prioritizing semantic data will enhance their AI accuracy by up to 80 percent while reducing costs by up to 60 percent.
Semantic data is essential for AI outcomes, especially as AI progresses from advisory roles to autonomous operations. NETSCOUT emphasizes the importance of context in AI processes, with Chief Operating Officer Sanjay Munshi stating, "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."
AI-Driven Analysis Benefits
IDC has projected that 80 percent of AI use cases will demand real-time, contextual data
NETSCOUT's method of converting digital interactions into accurate evidence has shown considerable benefits. According to their internal testing, they observed a more than 25 percent reduction in AI token consumption compared to traditional MELT data alone, and over a 75 percent decrease in Mean Time To Knowledge (MTTK). This efficient, context-rich intelligence aids clients in enhancing decision accuracy and reducing the cost of AI-driven analysis, facilitating safer progression towards autonomous operations.
IDC has projected that 80 percent of AI use cases will demand real-time, contextual data. NETSCOUT's unique Smart Data approach integrates early semantic extraction and context optimization at source to derive operational meaning from packets at the observation point. This approach ensures AI systems use their compute budgets efficiently without losing context, providing a significant edge in operational intelligence.
AI-Ready Operational Evidence
NETSCOUT's platform supports existing observability infrastructures by offering AI-ready operational evidence. This technology is deeply embedded across NETSCOUT solutions and can be seamlessly integrated into enterprise AI workflows, assisting teams across NetOps, SecOps, DevOps, and more. The platform allows organizations to reduce extraneous data processing, optimizing costs associated with telemetry, storage, and compute while maintaining necessary context for understanding service behavior.
Transforming Data Centers
NETSCOUT offers a path for customers to harness AI without forsaking current processes
This comprehensive intelligence layer supports AI-driven automation, offering operational context for governance, accountability, and compliance across cloud and data center transformations.
NETSCOUT enables teams to identify dependencies and discern the cause of failures in hybrid, multi-cloud, virtual, and physical environments, positioning itself as a vital provider of curated, network-derived intelligence that bolsters analytics and automation capabilities.
By extending its deep packet inspection capabilities into the realm of enterprise AI, NETSCOUT offers a path for customers to harness AI without forsaking current processes. This strategy benefits technology partners by delivering curated intelligence to enhance analytics and reinforce automation, showcasing NETSCOUT's ability to apply its data foundation across various domains including observability, cybersecurity, AIOps, and AgenticOps.
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.
