Designing AI-Driven and Machine Learning Enabled Cloud Systems for Secure, Compliant Analytics and Monitoring
DOI:
https://doi.org/10.21590/Keywords:
AI-driven cloud, machine learning, secure analytics, cloud monitoring, data compliance, cloud security, predictive analytics, anomaly detection, governance frameworks, data lifecycle managementAbstract
The integration of artificial intelligence (AI) and machine learning (ML) within cloud computing environments has significantly transformed the way organizations perform analytics and system monitoring. AI-driven cloud systems enable intelligent automation, predictive insights, and real-time decision-making, thereby enhancing operational efficiency and scalability. However, as data volumes increase and regulatory frameworks become more stringent, ensuring security and compliance has emerged as a critical challenge. This study explores the design and implementation of AI-driven and ML-enabled cloud systems that support secure and compliant analytics and monitoring. It examines architectural components such as data pipelines, distributed processing frameworks, and automated monitoring systems, along with security mechanisms including encryption, identity management, and anomaly detection. The research also emphasizes compliance with regulatory standards through governance models, audit mechanisms, and data lifecycle management. By leveraging advanced ML techniques such as predictive analytics and behavioral modeling, organizations can proactively detect threats and optimize performance. The study concludes that the integration of AI and ML with robust cloud architectures and governance frameworks is essential for building resilient, secure, and compliant systems capable of supporting modern data-driven enterprises.
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