Secure Cloud-Native GNN Architecture for Multi-Channel Contact Center Flow Orchestration
Siva Venkatesh Arcot
Abstract
Modern contact center platforms face unprecedented challenges in optimizing customer interaction flows across heterogeneous communication channels while maintaining security, scalability, and personalization. Traditional contact center technologies rely on static rule-based routing systems that cannot adapt to dynamic customer behavior patterns, seasonal variations, or emerging security threats. This paper presents a novel cloud-native architecture leveraging Graph Neural Networks (GNNs) for intelligent orchestration of multi-channel contact center flows with integrated cybersecurity mechanisms. Our approach models customer interaction patterns as temporal heterogeneous graphs, employing advanced GNN architectures to predict optimal routing decisions in real-time contact center environments. The system incorporates zero-trust security principles, federated learning capabilities, and edge computing optimization for latency-critical contact center applications. Experimental validation on large-scale contact center datasets demonstrates 23% improvement in customer engagement metrics, 31% reduction in system latency, and 40% enhancement in security threat detection compared to traditional contact center routing systems. The architecture supports dynamic scaling across cloud environments while maintaining end-to-end encryption and privacy-preserving analytics essential for contact center operations.