A Reference Architecture for AI-Driven Network Assurance in Large-Scale Enterprise Networks

Abstract

Network assurance based on AI is becoming one of the most important capabilities for ensuring reliability, performance, and service-level goals in large enterprise network environments. However, automated assurance is a challenge owing to the heterogeneous nature of telemetry, dynamic network structure, time dependencies, and dependence among causes of failures. This is because the proposed AI-based network assurance engine is reliable and comprises streaming telemetry monitoring, semantic normalization, time-aware topological modeling, feature-controlled computation, multi-method feature analysis, evidence-based diagnosis, and control remediation. Observation, inference, action, and decision are well separated in the architecture so that the model prediction cannot be a free operation command. A combination of statistical detection, supervised learning, forecasting, rule-based analysis, and topology-based reasoning detects an anomaly, approximates its impact, categorizes likely root causes, and forecasts a risk underway formation. All assurance findings are linked with supporting observations, state of topology, and time frame, confidence, and data-quality indicators with an evidence layer, contributing to explain ability and audit ability. Even graduated responses are further supported with policy-managed action layer, among more diagnostics and incident creation, and the closed-loop managed and approved remediation with blast-radium limits, verification, and rollback. The architecture proposed has the potential to offer a systematic base of scalable, explainable and operationally trustworthy network assurance besides providing dynamic model testing, drifting and control in dynamic business environments.