Management Strategies for Optimizing Security, Compliance, and Efficiency in Modern Computing Ecosystems

Abstract

The integration of cloud, on-premises, and edge environments has increased the complexity of managing diverse computing components. This paper examines management strategies essential for maintaining efficient and resilient computing infrastructures amid rapid advancements in artificial intelligence (AI), the Internet of Things (IoT), and distributed computing. The study focuses on key areas: infrastructure management, data governance, security protocols, user access management, and resource optimization. In infrastructure management, the paper discusses hybrid and multi-cloud orchestration, load balancing, and machine learning-driven auto-scaling techniques. For data governance, it covers data lineage and metadata management platforms, data anonymization methods, and compliance automation tools to meet regulations. Security management is addressed through AI-driven threat detection using anomaly detection models, the implementation of zero-trust security architectures with micro-perimeterization, and automated incident response using Security Orchestration, Automation, and Response (SOAR) platforms. User access management strategies include policy-based access control solutions, multi-factor authentication with biometrics, and behavioral analytics. Resource optimization focuses on serverless computing models for dynamic scaling, dynamic load balancing in containerized environments, predictive resource allocation using AI analytics, and green computing practices involving dynamic voltage scaling.