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

Distributed event-based systems are fundamental to moderncomputing, powering applications like large-scale stream processing andreal-time collaboration. However, securing these systems is challengingdue to their distributed nature and the complexity of event flows. Thispaper examines the application of artificial intelligence (AI) and ma-chine learning (ML) models to enhance the security of such systems.Leveraging techniques like anomaly detection, predictive analytics, andautomated threat response, AI/ML models provide robust mechanismsto identify and mitigate potential vulnerabilities. I outline a taxonomy ofevent-based systems, emphasizing how AI/ML can address key securityconcerns in diverse contexts, including Apache Kafka ecosystems andcollaborative real-time applications. Additionally, I explore trade-offs insystem design, highlight practical deployments of AI-driven security solu-tions, and identify open research challenges to inspire further innovationin safeguarding distributed event-based architectures.