Artificial Intelligence (AI) is rapidly transforming industries worldwide, and the realm of enterprise networking is no exception. In network management and monitoring, AI is beginning to play a pivotal role, offering a tantalizing glimpse of the future.
Understanding AI in network monitoring and management
AI harnesses the power of machine learning models to analyze data sets, using training data to derive insights. In the context of network management, AI takes in network traffic and telemetry data from networking hardware, creating baselines that reflect network traffic and health patterns. These baselines serve as the foundation for identifying, alerting, and optimizing network operations, including security enhancements.
Practical applications of AI in network management
Let’s explore a few examples that illustrate how AI can revolutionize network management and monitoring:
Anomaly detection for security
AI can be employed to establish baseline application traffic flows and promptly identify anomalies. These anomalies may indicate security threats, such as unauthorized intrusions or botnet activities. Furthermore, AI can not only detect these events but also automatically block and quarantine networked endpoints involved in suspicious behavior, significantly bolstering network security.
Automated configuration validation
Human-configured network devices can lead to outages and performance issues if not adhering to best practices. AI, when combined with intent-based networking (IBN), allows network administrators to communicate business intent to the system. The AI then translates this intent into precise switch command syntax, ensuring configurations are accurate and identifying potential conflicts. This approach minimizes human errors and enhances network reliability.
Dynamic traffic rerouting
AI can integrate seamlessly into network orchestration and monitoring platforms, continuously monitoring network traffic. When it detects rising congestion in network links, it can automatically implement traffic rerouting and load balancing techniques. This dynamic approach surpasses traditional static QoS and traffic management services, ensuring that critical traffic is preemptively rerouted around congestion points, optimizing network performance.
Preparing for AI in network operations
Before organizations can harness the potential of AI in network operations, they need to undertake certain preparations:
Standardize network configurations
To enable AI to function effectively, it is imperative to standardize network configurations across all switches and routers. Eliminating inconsistencies ensures that AI can collect uniform configuration training data for baselining and standardization. Thankfully, IBN orchestration platforms are available to help standardize network policies across all network components.
Consolidate network data
AI’s training relies on data, particularly traffic flow and network telemetry data. Rather than extracting this data separately for analysis, it’s advisable to send all flow and telemetry data to a centralized data lake—an NSoT (Network Source of Truth). This consolidation streamlines data management, accelerates AI training, and allows for specific data sharing with point-based network operations tools through a Northbound API.
Allocate resources for research
Finally, enterprises should allocate resources for researching and planning the integration of AI into their networks. AI is not just a buzzword but a transformative force in the world of IT. Getting a head start on understanding how AI can benefit network management and monitoring is crucial for staying competitive and secure.
The advent of AI in network management and monitoring is not a distant future but a rapidly approaching reality. AI’s ability to enhance security, automate configuration validation, and optimize traffic flows makes it a vital tool for modern enterprises. However, proper preparation in terms of standardization, data consolidation, and research is essential to fully harness AI’s potential in network operations. As the AI revolution continues to unfold, early adopters will undoubtedly gain a significant edge in the ever-evolving landscape of network management and security.
