Multimodal AI Enabled Intrusion Intelligence for Secure Cloud Edge Monitoring Across Distributed Enterprise Systems

Abstract

The rapid expansion of cloud-edge computing has transformed enterprise information systems into highly distributed environments comprising cloud platforms, edge devices, IoT endpoints, applications, containers, APIs, and interconnected network services. Although this architecture improves responsiveness, scalability, and resource efficiency, it also creates a heterogeneous and dynamic attack surface that challenges conventional intrusion detection systems. Security information is distributed across multiple modalities, including network traffic, system logs, application events, authentication records, endpoint telemetry, and operational metrics. Monitoring these sources independently can prevent security teams from recognizing relationships associated with sophisticated and multi-stage attacks. Multimodal artificial intelligence (AI) provides an emerging approach by integrating heterogeneous security signals into unified representations for more comprehensive intrusion intelligence. This study investigates the use of multimodal AI for secure cloud-edge monitoring across distributed enterprise systems. The proposed methodology integrates network, endpoint, application, identity, cloud, and edge telemetry and applies feature extraction, modality alignment, representation learning, multimodal fusion, anomaly detection, and threat classification. Multiple AI architectures are comparatively evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, scalability, and robustness. The methodology additionally examines missing modalities, concept drift, adversarial conditions, and distributed processing constraints. The study argues that multimodal AI can improve intrusion intelligence by correlating security evidence that would remain fragmented in single-source systems, enabling earlier detection, improved contextual awareness, and more resilient security decision-making across heterogeneous cloudedge enterprise environments.