Adaptive AI-Driven Digital Infrastructure for Secure Cloud Platforms Intelligent Automation and Enterprise Resilience

Abstract

Adaptive digital infrastructure has become essential for enterprises operating complex cloud platforms, distributed applications, automated services, and data-intensive business systems. Traditional infrastructure management approaches often depend on static configurations, manual monitoring, and reactive incident response, making them insufficient for rapidly changing cloud environments. This paper proposes an Adaptive AI-Driven Digital Infrastructure framework for secure cloud platforms, intelligent automation, and enterprise resilience. The proposed framework integrates artificial intelligence, machine learning, predictive analytics, intelligent automation, cloud security, observability, and self-healing mechanisms into a unified infrastructure management architecture. The system continuously collects telemetry from cloud resources, applications, networks, identities, databases, containers, and business services. AI models analyze these data streams to identify anomalies, predict failures, optimize resources, detect cyber threats, and recommend or execute corrective actions. Intelligent automation enables dynamic workload scaling, configuration remediation, security enforcement, incident containment, and service recovery. Enterprise resilience is strengthened through predictive risk assessment, fault isolation, automated recovery, policy-driven governance, and continuous learning. The methodology evaluates the framework using operational metrics including availability, detection accuracy, response latency, recovery time, resource efficiency, security effectiveness, and automation reliability. The proposed approach aims to establish an intelligent and adaptive digital infrastructure capable of continuously responding to operational and cybersecurity changes while improving cloud performance, security, efficiency, and long-term enterprise resilience.