AI-Driven Cloud-Native Governance Frameworks for Secure Scalable Explainable Enterprise Systems
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Abstract
Artificial Intelligence (AI) and cloud-native technologies are transforming enterprise systems by enabling scalable, flexible, and automated digital infrastructures. However, the increasing complexity of distributed architectures, cybersecurity risks, data governance challenges, and regulatory requirements demand advanced governance frameworks capable of ensuring security, scalability, transparency, and explainability. This study explores AI-driven cloud-native governance frameworks designed to support secure and explainable enterprise ecosystems. The research examines how AI techniques such as machine learning, intelligent automation, predictive analytics, and anomaly detection enhance governance mechanisms across cloud-native environments including microservices, containers, Kubernetes orchestration, and multi-cloud infrastructures. The paper also investigates the integration of Explainable AI (XAI) principles to improve transparency, accountability, and trust in automated decision-making systems. Through a comprehensive literature review and methodological analysis, the study identifies key governance dimensions including policy management, compliance automation, risk assessment, access control, data privacy, and operational resilience. The proposed governance framework emphasizes adaptive security, intelligent monitoring, scalable orchestration, and explainability mechanisms that align with enterprise compliance standards and business objectives. The findings demonstrate that AI-driven governance models significantly improve operational efficiency, threat detection, resource optimization, and decision transparency while supporting sustainable digital transformation in modern enterprises.
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