Chuck Girt, Chief Technology Officer
As enterprise networks evolve into highly distributed, dynamic, and software-defined environments, the operational complexity has outpaced the capabilities of traditional network management tools. Artificial Intelligence (AI) and Machine Learning (ML) are emerging as foundational technologies for building autonomous, resilient, and scalable network infrastructures.
This five-part series will explore AI in networking and why we must invest in an AI infrastructure for the future. This series, including this introduction to AI in operations, will include AI in network operations, bandwidth demands in the age of AI, intent-based networks, and self-driving networks.
AI-Driven Anomaly Detection and Root Cause Analysis
Traditional threshold-based monitoring systems are ill-equipped to detect subtle or multi-domain anomalies. AI models, particularly those based on unsupervised learning (e.g., Isolation Forests, Autoencoders), can:
- Detect deviations from baseline behavior across metrics like latency, jitter, packet loss, and throughput.
- Correlate anomalies across layers (L2–L7) and domains (WAN, LAN, cloud, edge).
- Perform RCA using graph-based causal inference or Bayesian networks to identify the most probable root cause.
Telemetry Ingestion and Feature Engineering at Scale
Modern networks generate high-frequency telemetry from routers, switches, firewalls, SD-WAN appliances, and cloud-native services. AI pipelines ingest and process this data using:
- Stream processing frameworks (e.g., Apache Kafka, Flink) for real-time analytics.
- Feature extraction from NetFlow/IPFIX, SNMP, syslogs, and gNMI/gRPC telemetry.
- Dimensionality reduction (e.g., PCA, t-SNE) to visualize and cluster high-dimensional data.
Predictive Capacity Planning and SLA Assurance
AI models trained on historical utilization and traffic patterns can forecast:
- Bandwidth saturation and recommend link upgrades or traffic engineering.
- SLA violations before they occur, enabling proactive remediation.
- Application performance degradation due to network constraints.
Intent-Based Networking (IBN) and Closed-Loop Automation
AI is a key enabler of IBN, where operators define high-level business intents and the system translates them into low-level configurations. This involves:
- Policy abstraction and translation using NLP and rule-based engines.
- Real-time state validation against the desired intent using digital twins.
- Closed-loop feedback where AI agents continuously monitor, verify, and adjust configurations.
AI-Augmented Security Operations (SecOps)
AI enhances network security by:
- Detecting lateral movement and command-and-control traffic using graph neural networks (GNNs).
- Classifying encrypted traffic using TLS fingerprinting and flow metadata analysis.
- Automating threat response via SOAR platforms integrated with AI-driven playbooks.
Digital Experience Monitoring and AIOps Integration
AI enables correlation between network performance and user experience by:
- Mapping application paths using synthetic transactions and real-user monitoring (RUM).
- Correlating UX degradation with network KPIs using supervised learning.
- Feeding insights into AIOps platforms for cross-domain incident management.
Final Thoughts
AI is not just an enhancement to existing NetOps—it’s a paradigm shift. By embedding intelligence into every layer of the network stack, organizations can achieve:
- Autonomous operations with minimal human intervention.
- Predictive resilience against outages and performance degradation.
- Operational scalability in the face of growing complexity.
The future of networking is autonomous, and AI is the control plane.