Predictive Security Analytics for Enterprises through Federated Learning and Multi-Cloud Infrastructure for Risk Intelligence

Abstract

Predictive security analytics has emerged as an important approach for enterprises seeking to identify cybersecurity risks before they develop into significant incidents. Conventional security monitoring largely depends on centralized data collection, signature matching, and retrospective investigation, whereas predictive approaches seek to identify patterns, anomalies, and risk indicators from historical and real-time security data. However, enterprise security information is increasingly distributed across multiple cloud providers, private data centers, business applications, endpoints, and geographically separated organizational units. Centralizing such information can create privacy, regulatory, bandwidth, and data-governance challenges. Federated learning provides an alternative by enabling participating organizations or infrastructure environments to collaboratively train machine-learning models while retaining sensitive training data within their local environments. Multi-cloud infrastructure complements this approach by providing distributed computing, storage, resilience, and access to heterogeneous security telemetry. The combination can support a risk-intelligence framework capable of learning from diverse security environments without requiring unrestricted movement of sensitive data. This research proposes an architecture in which local security environments independently process telemetry, train predictive models, and transmit protected model updates to an aggregation layer. The aggregated model can then be redistributed to participating environments for further local adaptation. The proposed methodology evaluates predictive performance, privacy preservation, communication overhead, scalability, cloud-resource utilization, and resilience against adversarial or unreliable participants. The study focuses on developing a practical framework for enterprise risk intelligence that balances predictive capability with data sovereignty, security, and operational efficiency.