Evolving Network Monitoring Beyond Basic Tools for AI Insights

Addressing Network Performance Blind Spots with Advanced Observability

Modern enterprise networks are complex ecosystems, and despite sophisticated monitoring tools, performance issues frequently arise without prior alerts. End users often report slow application loading or lagging video conferences, leaving network teams scrambling to diagnose problems across various domains like circuits, Wi-Fi, or DNS resolution. This common scenario highlights a significant gap in traditional network oversight, where engineers spend valuable time manually toggling between different screens and command-line interfaces, a process that is both time-consuming and inefficient in today’s fast-paced digital environment.

The conventional troubleshooting sequence, starting with ping, traceroute, and physical link checks, often yields normal results, pushing engineers toward arduous device-by-device command-line investigations. This reactive approach not only delays resolution but also generates frustration among users and executive leadership when service disruptions persist. The current technological landscape, increasingly driven by artificial intelligence, suggests a path toward more proactive solutions. Leveraging AI could automate repetitive diagnostic tasks and even predict potential issues, transforming how network problems are identified and resolved.

However, integrating AI into network operations presents its own set of challenges, primarily centered on data acquisition. While modern network devices offer capabilities like open telemetry or comprehensive event logging, many existing infrastructures still rely on older vendor solutions that might not fully support these advanced data streams. This necessitates a strategic shift from basic polling mechanisms like Ping and SNMP, which provide only snapshots, to continuous, high-fidelity data collection from event-driven logs. Capturing granular data is crucial for AI models to establish baselines, detect anomalies, and forecast future network states with accuracy.

Building a Robust Data Foundation for AI-Driven Insights

The journey toward an AI-ready observability framework begins with a fundamental change in how network data is collected and managed. Traditional methods, such as Ping and SNMP, offer limited insights, providing data at infrequent intervals that obscure real-time network dynamics and emerging trends. To enable AI to effectively predict and diagnose issues, a continuous stream of high-quality, reliable data is indispensable. This data is best sourced from comprehensive logs generated whenever an event occurs across the network infrastructure, moving beyond simple availability checks to capture the nuances of network behavior.

Initial efforts focused on identifying the appropriate level of logging. Collecting “informational” level logs from approximately 2,500 global devices introduced immediate scalability considerations, necessitating robust server capacity within the organization. This comprehensive logging extended to SD-WAN routers, capturing critical metrics such as SLA violations, CPU utilization spikes, bandwidth threshold breaches, configuration changes, and NetFlow data. The rationale behind this broad collection was the understanding that many network brownouts originate from complex interactions between users and applications, rather than isolated device failures.

SD-WAN routers proved particularly valuable due to their built-in SLA monitors for services like DNS, HTTPS, and various SaaS applications. These monitors act as synthetic emulators, generating logs whenever a Layer 7 service experiences an SLA breach or an application becomes slow, thus providing critical insights into application performance from a router’s perspective. Concurrently, logs from RADIUS and TACACS servers offered visibility into Layer 2 port security violations and occasional MAC flooding incidents. Wireless infrastructure data, including signal strength, SSID details, channel bandwidth, and client counts, was also integrated, leveraging vendor APIs for efficient data extraction. Similarly, data from switches encompassed everything from Layer 2 VLAN changes and OSPF convergence events to RADIUS server health and detailed interface statistics.

Overcoming Data Ingestion and Normalization Hurdles

Aggregating such a vast volume of diverse data initially led to a “data swamp” rather than a usable data lake. A significant challenge was the inconsistency in timestamps across different devices and the lack of proper labeling, rendering the data largely unusable for AI analysis. Without clear labels, differentiating logs originating from a router versus a switch was nearly impossible, hindering effective correlation. To address this, network configurations were revised to send logs to unique UDP ports for each device type, facilitating granular filtering and parsing into distinct data buckets. Furthermore, standardizing all device configurations to a Coordinated Universal Time (UTC) timezone was implemented to ensure consistent temporal correlation across the entire infrastructure.

Defining clear schemas, establishing data ownership, and implementing retention tiers were crucial steps in transforming the data swamp into a structured data lake. “Hot” data, comprising the most recent seven days, resided on fast storage for real-time streaming analytics. “Warm” data, covering 30 to 90 days, was migrated to columnar stores for trend analysis, while “cold” data, extending beyond 90 days, was archived to object storage with automated lifecycle rules. A meticulously maintained data catalog documented each table, outlining joining methods and providing sample queries. While seemingly bureaucratic, this meticulous approach prevented fragile data joins and mitigated the creation of enigmatic dashboards, ensuring data integrity and usability for subsequent AI applications.

Realizing the Benefits of AI-Enhanced Network Observability

With a clean, labeled dataset now available, the next phase involved developing dashboards for data correlation and implementing anomaly detection features. One of the earliest successes materialized from an anomaly detection system designed for CPU spikes on a specific router. This router was known to experience intermittent high CPU utilization, leading to packet drops affecting other sites, though the root cause remained elusive. The new anomaly detection feature provided proactive alerts when CPU usage began to climb. Further investigation, drilling down into NetFlow data, revealed that large file transfers were overloading the router, directly causing the CPU spikes and subsequent packet loss—an insight difficult to obtain with traditional monitoring tools.

Another significant victory came in resolving persistent “Wi-Fi is bad” complaints. Prior to AI integration, Wi-Fi controller alerts were basic “up/down” statuses, and every user complaint seemed identical. Engineers would spend hours, sometimes days, manually sifting through logs in an effort to identify patterns. The AI observability system revolutionized this process by mapping the entire client journey, from connection and authentication to IP address assignment and application access. It quickly pinpointed failures occurring at the authentication stage, correlating these with logs from RADIUS servers that indicated “expired certificate” issues.

Instead of generic “Wi-Fi failing” notifications, the system generated precise alerts such as “Authentication failures increased 300% after August 20, 2025. Possible cause: Certificate.” This specific, data-driven insight allowed the team to directly address the expired certificate, resolving the issue in minutes and eliminating the time wasted chasing phantom problems within access points or misattributing issues to the wireless network itself. Such targeted problem identification significantly reduced troubleshooting time and improved service quality.

Charting the Future of Network Management

This strategic shift from relying on vendor promises to taking ownership of data collection and labeling proved instrumental. By combining clean, contextually rich data with open-source observability tools and leveraging practical engineering experience, organizations can gain the deep visibility they consistently seek. While significant progress has been made, the journey toward a fully autonomous, AI-driven network is ongoing. The effectiveness of AI models is directly proportional to the quality and context of the data they consume. By building an observability framework that captures user journeys and critical changes, the models are equipped with the necessary language to learn and evolve.

Today, when queried about network status, network engineers can offer detailed, data-backed explanations rather than simple yes or no answers. For instance, explaining that “most users can access the payroll application without issues, but a specific site is experiencing delays due to a connection problem, with traffic already rerouted for a five-minute recovery” demonstrates a profound level of insight. This shift moves beyond mere green lights on dashboards, empowering leaders to demand clear service-level objectives (SLOs) and enabling engineers to focus less on defending network performance and more on continuous improvement.

For network professionals seeking to move beyond the limitations of Ping and SNMP, the advice is clear: design for questions, not just graphs; for complete network paths, not isolated nodes; for streaming data, not static snapshots; and for actionable insights, not just admiration of complex visualizations. The path forward involves starting small, meticulously labeling all data, maintaining data hygiene, and providing AI models with the contextual information they need. The reward is an observability framework that is truly AI-ready, capable of ensuring the network can consistently meet the demanding requirements of modern business operations.