Enhancing Security in Distributed Event-Based Systems Using AI/ML Models

Distributed event-based systems are fundamental to modern computing, powering applications like large-scale stream processing and real-time collaboration. However, securing these systems is challenging due to their distributed nature and the complexity of event flows. This paper examines the application of artificial intelligence (AI) and machine learning (ML) models to enhance the security of such systems. Leveraging techniques like anomaly detection, predictive analytics, and automated threat response, AI/ML models provide robust mechanisms to identify and mitigate potential vulnerabilities. We outline a taxonomy of event-based systems, emphasizing how AI/ML can address key security concerns in diverse contexts, including Apache Kafka ecosystems and collaborative real-time applications. Additionally, we explore trade-offs in system design, highlight practical deployments of AI-driven security solutions, and identify open research challenges to inspire further innovation in safeguarding distributed event-based architectures.