Federated Generative Intelligence for Privacy-Aware Cybersecurity in Sovereign and Hybrid Cloud Environments

Abstract

Federated Generative Intelligence (FGI) represents an emerging approach for strengthening cybersecurity while preserving data sovereignty across sovereign and hybrid cloud environments. Traditional centralized security analytics require organizations to transfer sensitive logs, identity information, network telemetry, and threat intelligence to common analytical repositories, creating privacy, compliance, and sovereignty risks. FGI combines federated learning, generative artificial intelligence, privacy-preserving computation, and distributed threat intelligence to enable collaborative cybersecurity intelligence without requiring raw organizational data to leave its originating environment. In sovereign and hybrid clouds, the approach can support localized model training while allowing participating environments to exchange model parameters, encrypted representations, or carefully controlled threat insights. Generative models can further enhance this architecture by producing synthetic attack traces, explaining detected anomalies, generating defensive recommendations, and supporting security analysts during incident investigation. The proposed framework integrates local security agents, federated orchestration, privacy protection, generative intelligence, policy enforcement, and adaptive response mechanisms. The methodology evaluates the framework through detection effectiveness, privacy preservation, communication overhead, model convergence, response latency, and operational resilience. The proposed architecture is intended to provide a scalable and privacy-aware cybersecurity foundation for organizations operating under strict regulatory, contractual, and national data-residency requirements.