Design and Implementation of a Data-Flowing Oriented Zero-Trust Security Situational Awareness Framework

Amid growing concern for safeguarding data assets, conventional security architectures, such as static authentication and privilege control mechanisms, fall short in ensuring data security. This paper aims to reduce data loss utilizing zero-trust architecture while minimising disruption to the original system. A zero-trust security situation awareness framework is developed, focusing on an abstract data-flowing process. The system is operated on a generalized distributed big data stream processing engine, and its performance was evaluated using a test dataset that included scenarios involving malicious insiders and account burglaries. The results indicate that the system performs exceptionally well in identifying and blocking malicious initiators, preventing risky access, and minimising data loss. The system guarantees a latency overhead of 46 ms on average, demonstrating its real-time processing capabilities.