Intelligent Threat Prediction Through Federated Agentic Learning and Cloud-Native Observability for Enterprise Security

Abstract

Enterprise cloud environments are increasingly exposed to sophisticated cyber threats that exploit distributed infrastructure, dynamic workloads, interconnected services, and continuously changing attack surfaces. Conventional centralized threat detection approaches face limitations related to data privacy, scalability, latency, and the ability to coordinate security decisions across heterogeneous cloud environments. This research proposes an intelligent threat prediction framework that integrates federated learning, agentic artificial intelligence, and cloud-native observability to support proactive enterprise security. The proposed approach enables distributed enterprise nodes to collaboratively train machine learning models without directly exchanging sensitive security data, thereby improving privacy preservation while maintaining collective threat intelligence. Agentic learning components continuously analyze telemetry, behavioral patterns, system events, and security signals to identify emerging attack indicators and initiate context-aware predictive actions. Cloud-native observability integrates logs, metrics, traces, application events, network flows, and security telemetry to provide comprehensive visibility across containers, Kubernetes clusters, microservices, APIs, and hybrid cloud infrastructure. The methodology combines feature engineering, federated model training, agent-based reasoning, anomaly detection, threat scoring, and automated response orchestration. Evaluation focuses on prediction accuracy, detection latency, false-positive rate, communication efficiency, privacy preservation, and resource utilization. The proposed framework provides an adaptive foundation for proactive threat prediction, distributed intelligence, and resilient security operations across complex enterprise cloud ecosystems.