Integrating Cloud Native Data Engineering with Reinforcement Learning for Autonomous Enterprise Cybersecurity

Abstract

Cloud-native data engineering and reinforcement learning (RL) are emerging as powerful technologies for developing autonomous enterprise cybersecurity systems capable of detecting, adapting to, and responding to complex cyber threats. Modern enterprises generate enormous volumes of security-related data from cloud applications, networks, endpoints, and digital services. Traditional cybersecurity approaches often struggle to process this information efficiently and respond to rapidly evolving attacks. Integrating cloud-native data engineering enables scalable data collection, processing, and management, while reinforcement learning provides adaptive decision-making capabilities through continuous interaction with dynamic environments. This research explores the integration of cloud-native data engineering architectures with reinforcement learning techniques to establish autonomous cybersecurity frameworks. The study examines how intelligent systems can improve threat detection, automated response, and security optimization by learning from real-time data environments. The research highlights technological opportunities, architectural challenges, and future directions for building resilient cybersecurity ecosystems capable of operating with minimal human intervention