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AI-Driven Intelligent Infrastructure Framework for Secure Cloud-Native Enterprise Systems and Digital Transformation

Abstract

Artificial Intelligence (AI) has become a transformative force in modern enterprise systems, particularly within cloud-native environments that support large-scale digital transformation. Organizations increasingly rely on distributed cloud architectures, containerized applications, and microservices to deliver scalable and resilient services. However, the rapid expansion of cloud infrastructure also introduces significant challenges related to security, infrastructure management, performance optimization, and threat detection. This research proposes an AI-driven intelligent infrastructure framework designed to enhance the security, automation, and operational efficiency of cloud-native enterprise systems.

 The proposed framework integrates AI techniques such as machine learning, predictive analytics, anomaly detection, and automated orchestration with cloud infrastructure management tools. It aims to create a self-adaptive environment capable of monitoring system behavior, predicting failures, detecting cyber threats, and optimizing resource allocation in real time. By combining AI-based analytics with cloud-native technologies such as containers, Kubernetes orchestration, and DevSecOps practices, enterprises can achieve improved system reliability and enhanced security.

The study analyzes existing AI-based infrastructure management models and develops a structured methodology for implementing intelligent infrastructure in enterprise environments. The results highlight that AI-driven infrastructure significantly improves system resilience, reduces operational costs, and strengthens cybersecurity defenses. This research contributes to the advancement of secure, scalable, and intelligent enterprise systems supporting long-term digital transformation initiatives.

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