AI-Driven Cloud Computing and Intelligent Enterprise Information Management for Secure, Scalable Digital Transformation in Saudi Arabia under Vision 2030

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@article{1725149,
      author = {Wael Zouheir Jouni},
      title = {AI-Driven Cloud Computing and Intelligent Enterprise Information Management for Secure, Scalable Digital Transformation in Saudi Arabia under Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {2748-2761},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1725149.pdf},
      abstract = {Cloud platforms are fast becoming the foundation for new digital services in Saudi Arabia, yet moving workloads and enterprise information to the cloud raises three linked problems: how to run elastic infrastructure reliably, how to secure it against a growing volume of attacks, and how to keep data within the legal and sovereign boundaries that national regulation sets. Artificial intelligence (AI) is now applied to each problem, through anomaly detection and workload forecasting in cloud operations, learning-based intrusion detection and identity controls in security, and metadata-driven governance in cloud data platforms. This review asks how these capabilities can be combined into a coherent model for secure, scalable and compliant cloud-based information management in Saudi organisations. A systematic configurative review was conducted of journal articles published between 2020 and 2025, seeded by a Saudi study of deep-learning intrusion detection in cloud networks and a Saudi survey of barriers to cloud adoption, and closed at 30 sources. The synthesis shows that Saudi adoption is shaped above all by perceived security, regulatory compliance, technical capability and connectivity; that AI methods for forecasting, autoscaling and anomaly detection deliver measurable gains in controlled evaluations but remain dependent on metric selection, data preparation and evaluation settings that rarely resemble production; and that sovereignty must be engineered into data placement, identity and metadata rather than assumed from a provider's location. The paper proposes a sovereign intelligent cloud model built from four nested layers, a workload placement matrix that links data classification to deployment choices and controls, and an agenda for research and practice.},
      keywords = {Cloud computing, artificial intelligence, cloud security, data sovereignty, enterprise information management, Saudi Arabia, Vision 2030},
      month = {November},
      doi = {https://doi.org/10.64388/ire1725149}
  }