Federated Cyber Defense: A Privacy-Preserving AI Framework for Threat Intelligence Sharing Across Multinational Enterprises

Abstract

Multinational enterprises (MNEs) operate in a globally connected environment which poses complex and evolving cyber risks that require intelligence sharing, collaboration, and coordination in real-time. Unfortunately, privacy, legal compliance, and data sovereignty issues create barriers to informative sharing across sectors. This paper introduces a new framework of Federated Cyber Defense (FCD) systems that utilize AI techniques of privacy-preserving technologies, federated learning, and secure multiparty computation to allow private intelligence sharing across enterprises. With the FCD system, participants in a federation are allowed to train and process intrusion detection models on private data. Only model updates, not raw logs or sensitive indicators, are shared with a central coordinating system. Even though detection capabilities are augmented across the network, data confidentiality is preserved. Through a simulated network of multinational partners, high detection accuracy (above 95%) with stringent privacy requirements is maintained. This approach affirms the use of federated architectures for global cybersecurity alliances and proposes the integration of privacy-preserving technologies.