A cloud-native intelligent decision system is a unique paradigm for organizations that need to secure processing of massive data, respond quickly to ever-changing environments, and conduct predictive analytics in real time. These systems combine cloud-native architectural patterns, including microservices, containerization, serverless computing and elastic orchestration with advanced artificial intelligence techniques such as deep learning, reinforcement learning and explainable models. This report explores how these systems are enabling secure, predictive and scalable analytics across multiple domains such as finance, healthcare, cybersecurity and telecommunications. It integrates state-of-the-art studies in trust management, decentralized data architecture, federated learning, automated incident response and adaptive cyber defense. It provides a systematic study of system layers, mechanisms and design principles. In a nutshell, the article claims that future analytics processes can no longer concentrate on individual hardware upgrades or better algorithms but need to take an integrated approach of security, scalability and smart decision-making. Such systems play a critical role in settings where high degrees of reliability, transparency and computational efficiency need to co-exist.
Adhya Katiyar (Wed,) studied this question.