What is AI network monitoring?

It uses AI systems to process network data streams in real time, learn what constitutes normal network behavior, and use established baselines to detect deviations in network activity. AI-driven monitoring strategies can help network operators overcome the limitations of traditional rule-based and manual methods, which are often insufficient for the scale, complexity and sophistication of today’s networks.

Traditional network monitoring tools rely on periodic polling, static rules and device-centric metrics, making it suitable for simpler, smaller networks. But modern computing networks are neither simple nor small. They span diverse, dynamic global environments and hybrid cloud infrastructures with thousands of interconnected devices. For example, the average multicloud environment spans 12 different services and platforms.

Advanced networks also produce tremendous amounts of data, compared to more traditional networks. The majority (86%) of tech leaders find that traditional monitoring methods simply cannot keep pace with the volume and speed with which modern networks generate data. Therefore, they require more sophisticated monitoring tools and practices.

AI network monitoring tools enable continuous analysis of massive telemetry datasets (including traffic flows, logs, tracing data and user interactions) from on-premises data centers and cloud environments, providing broader visibility into network activity. Using intelligent algorithms, AI tools can detect anomalies, predict component failures and provide remediation guidance, enabling network engineers and administrators to predict potential network issues before they cause operational disruptions (or affect the user experience).

As such, AI-powered network monitoring helps businesses implement more effective network management practices for smarter, faster, more resilient enterprise computing networks.

AI network monitoring relies on a range of processes and functions to automate network management tasks. These processes include:

AI network monitoring solutions gather telemetry data and other observability data from a variety of sources, including network devices (switches, routers), data queries and synthetic transactions. They can collect data actively (using test traffic) or passively (by observing live production traffic). And typically, AI systems enhance the data by integrating streaming telemetry, which enables real-time, granular insights that surpass traditional methods (such as Simple Network Management Protocol (SNMP) polling).

The raw data—including any headers, metadata and system-level performance metrics—is then cleansed, structured and aggregated for AI model training.

Using historical data and external data sources, ML models learn the network’s baseline behaviors, normal traffic volumes and application performance benchmarks. Then, AI models are configured to spot outlier patterns and to distinguish between benign performance fluctuations and actual security threats, inefficiencies or policy violations.

Advanced models can even employ deep neural networks and unsupervised learning to enable unsupervised anomaly detection (where the model can recognize new or unknown threats without predefined signatures).

Deep neural networks—such as autoencoders, convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are designed to learn complex patterns and representations from high-dimensional and unstructured data. These models can capture intricate dependencies and non-linearities in network data, so they excel at differentiating normal activity from anomalous instances.

AI models are used to monitor real-time data streams, analyzing every network flow, event or session for suspicious activity and impending failures. For example, an AI system might flag unusual bandwidth spikes that foreshadow an imminent distributed denial-of-service (DDoS) attack or recognize encrypted traffic flows that skirt traditional security filters.

Monitoring tools can deploy methods such as synthetic monitoring—wherein simulated user interactions validate network and application availability—and flow-based monitoring—which summarizes packet flows for traffic analysis and anomaly detection.

AI network monitoring tools also correlate data for more robust detection. If a set of disparate alerts are all linked to a common root cause (a misconfigured switch, for instance), the platform can aggregate them and escalate the anomaly to IT teams as a single actionable incident.

When the monitoring system detects an anomaly or threat, it triggers an alert (to IT staff or network administrators) and in some cases, initiates an adaptive response (by rerouting traffic, blocking a malicious IP, provisioning extra resources or adjusting network policies, for example).

AI monitoring tools use predictive analytics, which enable IT teams to anticipate future network issues based on trending data and to proactively repair components. If, for example, the system forecasts a router hardware failure, IT staff can schedule a hardware replacement before the router fails.

Monitoring tools also run optimization algorithms that can analyze network load distribution and latency, recommend configuration changes and automate network tuning to improve capacity planning.

AI-driven root cause analysis rapidly connects the dots across network layers and device logs to reduce the time to issue resolution.

AI-based network monitoring systems continuously learn from network data to update baselines and refine anomaly detection models, adapting to changes in network configurations and traffic patterns. The more context-rich data the AI model ingests, the more effectively it can self-optimize and prevent future outages.

Traditional network monitoring relies on manual setup and static rules or thresholds that generate alerts when specific conditions are met (CPU usage exceeding a certain percentage, as one example). In a traditional monitoring environment, network administrators deploy monitoring sensors across network devices (switches, routers, firewalls, servers and access points), and the sensors use protocols—such as SNMP, Internet Control Message Protocol (ICMP) and NetFlow—to collect data on device status, traffic flow and overall network performance.

Traditional monitoring approaches generally use polling methods to collect data at regular intervals, focusing mostly on device-level health metrics. While this method provides a straightforward, vendor-agnostic monitoring strategy, it has some significant limitations.

For example:

• It often treats events in isolation without correlating multiple data points or understanding cause-effect relationships, which can slow down root cause analysis and incident response.

• Rules-based monitoring relies on predefined thresholds and conditions, and static rules cannot easily adapt to dynamic network conditions. This issue can create false positives and alert fatigue, and in some cases, cause the monitoring system to miss critical events (especially in cloud-based and hybrid networks).

• The approach is largely reactive, so issues are detected only after they impact the network.

In contrast, AI-based network monitoring takes an adaptive, proactive approach. It can:

• Correlate data across multiple sources (traffic, logs, devices) for comprehensive insights and faster troubleshooting and root cause analysis.

• Adapt dynamically to evolving network conditions and learn over time.

• Scale to handle complex IT operations and network infrastructures efficiently.

• Reduce false alarms by refining alert accuracy over time, decreasing notification fatigue for network engineers and improving overall response efficiency.

• Detect subtle anomalies and predict potential issues proactively, before they impact the network.

• Recommend—or automatically apply—corrective actions to maintain network security and performance.

AI network monitoring enables IT teams to move away from reactive, manual network management strategies and toward the intelligent, predictive, automated approach that modern networks demand.  

According to the IBM Institute for Business Value (IBM IBV), “AI-enabled workflows—many driven by agentic AI—are poised to expand from 3% in 2024 to 25% by 2026,” representing an eightfold increase in AI deployments¹. Adopting an AI-based network monitoring approach offers businesses numerous benefits, including: