Cloud computing and big data analytics have transformed enterprise operations, yet traditional perimeter-based security models fail in distributed, multi-cloud environments. Zero Trust Architecture (ZTA) addresses these limitations by enforcing continuous verification and identity-centric controls. This study examines Zero Trust principles applied to cloud-based big data systems, focusing on micro-segmentation, policy-as-code enforcement, and continuous authentication mechanisms.

It has become more difficult to manage modern telecom infrastructure due to complex networks and unpredictable traffic patterns. We designed and tested a generative AI-based system that makes network optimization decisions automatically. To construct a multi-objective optimizer that can handle speed, response time, and running costs simultaneously, the system combines transformer-based large language models with reinforcement learning agents.