(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2023)
Modern enterprise architectures are rapidly shifting toward cloud-native environments to achieve unprecedented scalability, flexibility, and resilience. However, this transition introduces complex security vulnerabilities and data management challenges across highly fragmented, distributed infrastructures. This paper proposes a novel framework for designing intelligent cloud-native enterprise systems by integrating AI-driven security mechanisms with distributed data intelligence models. By leveraging machine learning at the edge and utilizing localized data processing, the proposed architecture mitigates latency while establishing a proactive defense posture against sophisticated cyber threats. Real-time anomaly detection, zero-trust microsegmentation, and federated learning paradigms form the core of this intelligent ecosystem. The abstract model demonstrates how distributed data pipelines can optimize resource allocation and maintain continuous compliance without compromising system performance. Ultimately, this research provides a comprehensive blueprint for enterprises seeking to architect robust, self-healing, and data-intelligent cloud environments capable of defending against evolving vector attacks while unlocking the full potential of decentralized data assets