Next-Generation AI-Powered Cloud Network Governance: An Intelligent Framework for Autonomous Operations, Data Excellence, and Adaptive Cyber Defense in Saudi Arabia

Walid Abdulfattah Fararjeh
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Walid Abdulfattah Fararjeh: Independent Researcher – Telecom Cloud Operations and Digital Transformation, Riyadh, Saudi Arabia

Technium, 2026, vol. 31, 574-614

Abstract: Abstract The rapid advancement of artificial intelligence (AI), cloud-native computing, software-defined networking, and intelligent automation is fundamentally transforming enterprise cloud ecosystems and redefining the principles of cloud network governance. Modern organizations increasingly operate complex hybrid and multi-cloud environments that integrate distributed infrastructures, cloud-native applications, enterprise data platforms, and adaptive cybersecurity mechanisms. Although existing governance frameworks provide valuable guidance for cloud management, cybersecurity, compliance, and enterprise architecture, they generally address these domains independently and offer limited support for integrated AI-driven governance, autonomous operations, enterprise data excellence, and adaptive cyber defense. This fragmentation creates significant challenges in achieving operational resilience, governance transparency, regulatory compliance, and intelligent decision-making, particularly within rapidly evolving digital ecosystems such as those envisioned under Saudi Arabia's Vision 2030.This study proposes a Next-Generation AI-Powered Cloud Network Governance Framework that integrates autonomous cloud operations, enterprise data excellence, adaptive cyber defense, AI governance, regulatory compliance, and intelligent decision support into a unified governance architecture. The research adopts a Design Science Research (DSR) methodology supported by a structured review of international standards, peer-reviewed literature, cloud governance frameworks, cybersecurity best practices, AI governance models, and Saudi digital transformation initiatives. The proposed framework is developed through the synthesis of governance principles derived from ISO/IEC standards, the NIST Artificial Intelligence Risk Management Framework, the NIST Cybersecurity Framework, Cloud Security Alliance guidance, cloud-native computing practices, and Saudi national strategies for data, artificial intelligence, and cybersecurity.The proposed architecture comprises six integrated governance dimensions: Strategic AI Governance, Autonomous Cloud Operations, Enterprise Data Excellence, Adaptive Cyber Defense, Intelligent Decision Support, and Continuous Governance Improvement. Together, these dimensions establish a holistic governance model that enables continuous operational monitoring, AI-assisted policy enforcement, trusted enterprise data management, predictive risk assessment, explainable decision-making, and adaptive cybersecurity across hybrid and multi-cloud environments.As a conceptual contribution, the proposed framework provides a comprehensive governance model for organizations seeking to modernize cloud governance while aligning intelligent operations with business objectives, regulatory obligations, and national digital transformation priorities. The framework offers practical guidance for government agencies, telecommunications operators, cloud service providers, financial institutions, healthcare organizations, and smart-city initiatives pursuing secure, resilient, scalable, and intelligent cloud ecosystems. Furthermore, it establishes a foundation for future empirical validation, industrial implementation, and quantitative assessment of AI-enabled cloud governance in next-generation digital infrastructures. Corresponding Author: Walid Abdulfattah FararjehORCID: 0009-0006-3150-6225 Keywords: Artificial Intelligence; Cloud Network Governance; Autonomous Cloud Operations; Enterprise Data Excellence; AI Governance; Adaptive Cyber Defense; Cloud Security; Multi-Cloud Governance; Intelligent Decision Support; Digital Transformation; Saudi Vision 2030.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:ojs:13784

DOI: 10.47577/technium.v31i.13784

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