Federated XAI for Cybersecurity Enhancing Threat Detection and Model Transparency

Abstract

Threats in cybersecurity are evolving rapidly, and artificial intelligence-based detection systems are essential for both security and transparency. Federated Learning (FL) provides the means for organizations to work together to detect threats, while also leaving user data private, and therefore less sensitive to the downside risks of centralized systems. Explainable AI (XAI) is fundamental to hardening federated security models by providing transparency on cybersecurity decisions, to enhance confidence and compliance. XAI provides a greater understanding of AI-powered security operations to stop cyber breaches and increases visibility into threat detection. This study discusses how XAI processes, the cybersecurity threats, by providing privacy in distributed networks, and then weighing down its benefits and challenges in order to achieve efficiency without compromising security. We also compare different AI techniques, considering their efficacy in real cyber scenarios with case studies, industry perspectives, and expert opinions. We evaluate the impacts that AI-enabled security capabilities will have, discuss changes on the horizon, and make recommendations on how to build AI-enabled, robust, and interpretable security systems to enable organizations to stay ahead of burgeoning threats. Finally, we present an overview of the performance of AI/ML-based security, analyze potential future directions in security powered by AI, and conclude with some suggestions on how to make security systems more trustworthy, effective, and interpretable. In this study, we also strive to gain a holistic knowledge of federated XAI in terms of empowering an organization to utilize what is needed to safeguard information assets against emerging cyber threats.