Aug 2026· American Journal of Innovation in Science and Engineering· 0 citations· 23 references
TL;DR
The authors introduce the AI-powered Integrated Cyber-Economic Resilience (AICER) Framework, which is a conceptual framework that integrates cloud-native infrastructure, cybersecurity intelligence, AI-assisted operational analytics, secure software delivery, economic impact assessment, and governance into a comprehensive decision-support architecture.
Abstract
The growing dependence of governments, financial institutions, healthcare organizations, energy providers, transportation networks, and communication systems on “cloud-native” platforms has made it more important than ever that these digital services be delivered continuously and securely. Cloud-native architectures offer scalability, flexibility, and quick deployment using microservices, containers, orchestration, and DevSecOps practices, but they also present complex cybersecurity, operational, and supply chain threats to the country’s critical infrastructure and economic well-being. The existing research focuses on cloud resilience, artificial intelligence for IT operations (AIOps), cybersecurity or governance individually, causing approaches to be disjointed and suboptimal for critical service continuity during large-scale cyber incidents. In this research, the authors introduce the AI-powered Integrated Cyber-Economic Resilience (AICER) Framework, which is a conceptual framework that integrates cloud-native infrastructure, cybersecurity intelligence, AI-assisted operational analytics, secure software delivery, economic impact assessment, and governance into a comprehensive decision-support architecture. The framework is formulated on the basis of Design Science Research Methodology (DSRM) and supported by an integrative literature review encompassing the most recent literature, international cybersecurity standards, and cloud computing best practices. The proposed framework does not introduce new performance metrics but rather builds on existing metrics, such as Service Level Objectives (SLOs), availability and reliability metrics, Mean Time Between Failures (MTBF), Mean Time to Recovery (MTTR), DORA software delivery metrics, Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), Software Levels for Supply Chain Security (SLSA), and NIST Cybersecurity Framework (CSF 2.0) and NIST AI Risk Management Framework (AI RMF). The framework also takes economic impact into account to inform decisions on recovery for nationally significant services. The proposed architecture provides a vision for a policy-aware and AI-driven approach to enhancing cyber resilience, bolstering critical infrastructure, and fortifying continuity of essential digital services. The study provides a strong foundation for further prototype implementation, experimental validation, and deployment in public and private critical sectors.
Public services, financial systems, healthcare records, industrial operations, logistics networks and artificial intelligence workloads are hosted on cloud platforms and constitute a larger part of Saudi Arabia’s digital economy. This transition enhances scalability and service agility, but also increases operational risk in conditions where outages, ransomware, misconfiguration, data exposure and third-party dependency threaten national continuity. This review investigates cloud computing and cyber resilience strategies for critical digital infrastructure in Saudi Arabia. It employs a structured narrative review methodology, integrating peer-reviewed literature, international standards, and Saudi policy documents published between 2020 and 2025. The review constructs a resilience framework connecting cloud governance, zero trust access, data classification, encryption, shared-responsibility management, security monitoring, DevSecOps, backup immutability, disaster recovery, and crisis coordination. It argues that cyber resilience is more than just cybersecurity compliance, as critical entities need to not only prevent attacks but also absorb disruption, maintain minimum viable services, recover trusted operations and learn from incidents. The Saudi context is unique because the simultaneous increase in cloud demand and governance expectations is driven by Vision 2030, cloud-first adoption, national computing infrastructure expansion, data localisation requirements, and cybersecurity controls. The study concludes that resilient cloud adoption requires a tiered workload classification, sovereign and approved cloud architectures, continuous control validation, cyber-recovery engineering and executive-level resilience metrics. The proposed roadmap supports governments, utilities, healthcare providers, financial institutions and industrial operators in implementing secure digital transformation, while protecting national trust and service continuity.
The Adaptive Risk-Driven DevSecOps Framework (ARDDSF) is proposed, a layered framework for securing multi-cloud enterprise systems in the era of agentic artificial intelligence that bridges DevSecOps automation, AI-assisted security analysis, Zero Trust policy enforcement, and multi-cloud governance.
Nitin Bodade· International Journal of Inn...· 0 citations
A Saudi Enterprise Cloud Cyber-Resilience Framework which integrates compliance alignment, architectural controls, operational preparedness, and continuous learning through a six-stage lifecycle is proposed, delivering a practical control-to-outcome model and a staged implementation roadmap for enterprises pursuing secure cloud transformation while assuring compliance, service availability, and organizational confidence.
Munir Ahmed Mohammed· Global academic journal of e...· 0 citations
AI-native IT architecture is fundamentally reshaping the modern enterprise and accelerating the shift toward decentralized autonomous networks. While this transition unlocks unprecedented capabilities, it also introduces a wave of complex security vulnerabilities. Today, enterprises face a tripartite challenge which are securing hyper-connected cloud environments, mitigating sophisticated cyber threats and ensuring algorithmic accountability. To address these interconnected issues, this study synthesizes recent literature (2022–2024) from Scopus and Web of Science bridging three disciplines typically studied in isolation of cloud governance, cybersecurity resilience and digital trust engineering. Technological adoption alone is insufficient for sustainable digital transformation. Organizations must pivot from reactive, post-deployment compliance to embedding proactive governance directly into their system architecture from inception. By examining dual-loop governance models and policy-as-code frameworks, this paper translates high-level theories into actionable deployment pathways and measurable performance indicators. Furthermore, as stringent regulations like the EU AI Act take effect, cyber defenses must evolve in tandem. Enterprises urgently require predictive resilience frameworks powered by Explainable AI (XAI) to pierce algorithmic opacity which is an effort that will rely heavily on quantitative trust assessments and decentralized identity infrastructures. Finally, to validate these concepts beyond theoretical constructs, we ground our proposed model in real-world application scenarios across the healthcare, finance and critical infrastructure sectors. Ultimately, for AI-native enterprises to achieve long-term viability, they must successfully balance rapid, autonomous innovation with adaptive security and verifiable stakeholder trust.
Uthayashankari A/P Shumugam, Nurshahida Binti Muhamad Razali, Syed Mohsen Bin Syed Abu Bakar Alkaff et al.· International journal of res...· 0 citations
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.
The analysis indicates that effective edge-cloud AI systems require adaptive workload placement, privacy-preserving distributed learning, security-aware inference, explainability, fault tolerance, and continuous resource optimization rather than simple physical distribution of computation.
Amir Hosseini, L. Karimi· International Journal of Adv...· 0 citations
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