Authors: Tharushi Jayasuriya

Abstract: System downtime poses significant challenges for modern IT and industrial operations, often resulting in financial losses, productivity reductions, and compromised service quality. Traditional configuration management approaches, reliant on manual processes and static documentation, are prone to human error and delays, which can exacerbate system failures and prolong downtime. Intelligent configuration management systems (ICMS) have emerged as a transformative solution, leveraging artificial intelligence, machine learning, and predictive analytics to monitor, validate, and optimize system configurations in real time. These systems enable automated change tracking, anomaly detection, and proactive remediation, reducing the likelihood of misconfigurations and preventing system disruptions. By analyzing historical data and system dependencies, ICMS can predict potential failures and recommend corrective actions before incidents occur. This review examines the conceptual foundations, architectural frameworks, enabling technologies, and operational strategies that underpin intelligent configuration management. Additionally, it explores practical applications across IT infrastructure, cloud environments, manufacturing systems, and critical industrial operations, highlighting measurable reductions in downtime, improved reliability, and enhanced resource efficiency. The review also addresses challenges related to integration, data quality, security, and human oversight, while identifying future research directions such as autonomous self-healing systems, edge-enabled monitoring, and AI-enhanced root cause analysis. Intelligent configuration management is positioned as a strategic enabler for resilient, high-availability systems in complex operational environments.

DOI: http://doi.org/10.5281/zenodo.17883050

Published by: vikaspatanker

Authors: Hasina Chowdhury

Abstract: Insider threats, caused by malicious or negligent actions of employees, contractors, or trusted users, pose a significant challenge to organizational cybersecurity. Traditional security measures, including access control and periodic audits, often fail to detect subtle deviations in user behavior that indicate potential insider risks. AI-based behavioral monitoring has emerged as a transformative solution, leveraging machine learning, anomaly detection, and predictive analytics to identify unusual patterns, deviations, and risky activities in real time. By analyzing user interactions, access patterns, and contextual data, AI systems can generate dynamic risk scores, prioritize alerts, and guide security teams in taking proactive measures. This review examines the conceptual foundations, architectural frameworks, enabling technologies, and operational methodologies that underpin AI-driven behavioral monitoring. It highlights the techniques used to detect insider threats, including supervised and unsupervised learning, clustering, sequence analysis, and predictive modeling. The paper also discusses real-world applications across industries such as finance, healthcare, and critical infrastructure, demonstrating measurable improvements in threat detection, incident response, and compliance. Additionally, challenges such as data privacy, model interpretability, and false positives are analyzed. Finally, the review explores future directions, including explainable AI, adaptive learning, and privacy-preserving monitoring, positioning AI-based behavioral monitoring as a strategic enabler for proactive, resilient, and context-aware insider threat management.

DOI: http://doi.org/10.5281/zenodo.17882976

Published by: vikaspatanker

Authors: Ananya Paul

Abstract: The growing adoption of distributed enterprise networks, cloud services, and remote work has created a critical need for secure, reliable, and scalable connectivity across multiple sites. Traditional VPN solutions, while effective in point-to-point scenarios, often face limitations in flexibility, scalability, and performance when applied to complex multi-site networks. Hybrid VPN frameworks, which integrate site-to-site VPNs, remote access VPNs, and cloud-based VPN services, offer a comprehensive approach to addressing these challenges. By combining the strengths of conventional and cloud-native VPN technologies, hybrid frameworks enable dynamic routing, optimized bandwidth usage, and enhanced security across distributed environments. This review examines the concept, architecture, and operational impact of hybrid VPN frameworks on multi-site connectivity. It explores enabling technologies such as software-defined networking (SDN), software-defined WAN (SD-WAN), cloud VPN gateways, and centralized management platforms. The paper also analyzes techniques for secure and efficient connectivity, including encryption strategies, traffic prioritization, failover mechanisms, and automated policy enforcement. Challenges such as integration complexity, interoperability, latency, and security vulnerabilities are discussed, along with mitigation strategies. Finally, the review highlights industry applications and future research directions, emphasizing how hybrid VPN frameworks are critical for achieving secure, resilient, and high-performance multi-site connectivity in modern enterprise networks.

DOI: http://doi.org/10.5281/zenodo.17882844

Published by: vikaspatanker

Authors: Rohit K. Basnet

Abstract: The rapid adoption of artificial intelligence has increased the demand for scalable, efficient, and cost-effective computational infrastructures. Traditional server-based architectures often result in underutilized resources, idle compute time, and increased operational overhead, which can limit the performance and scalability of AI workloads. Serverless AI models provide a transformative solution by leveraging event-driven, cloud-native architectures that dynamically allocate resources based on demand, abstracting infrastructure management from developers and organizations. These models enable functions to execute on-demand, scale automatically, and terminate once tasks are completed, ensuring optimized utilization of computational resources. This review examines the concept, architecture, and methodologies underlying serverless AI, highlighting how it improves computational efficiency while reducing costs. Key enabling technologies such as function-as-a-service (FaaS), microservices, containerization, orchestration frameworks, and cloud-native pipelines are explored. Additionally, the paper evaluates techniques for optimizing serverless AI performance, including dynamic scaling, resource-aware scheduling, asynchronous execution, and caching mechanisms. Challenges such as cold start latency, state management, integration complexities, and vendor lock-in are also addressed. Finally, the review explores emerging trends in hybrid and edge serverless AI, predictive resource allocation, and energy-efficient model execution, positioning serverless AI as a strategic enabler for agile, cost-effective, and high-performance AI computing in modern cloud ecosystems.

DOI: http://doi.org/10.5281/zenodo.17882776

Published by: vikaspatanker

Authors: Manoj K. Lama

Abstract: The growing reliance on cloud infrastructure has introduced unprecedented complexity in managing regulatory compliance, as organizations operate across multiple environments, jurisdictions, and service models. Traditional manual compliance methods struggle to keep pace with continuous integration and deployment cycles, leaving enterprises vulnerable to misconfigurations and regulatory breaches. Autonomous Policy Engines (APEs) have emerged as intelligent automation frameworks that enforce compliance dynamically by interpreting, monitoring, and executing policies in real time. These systems leverage rule-based logic, artificial intelligence (AI), and policy-as-code paradigms to ensure that every cloud resource adheres to internal and external standards without manual oversight. This review article explores the architectural foundation, functional mechanisms, and practical implications of APEs in achieving continuous compliance across hybrid and multi-cloud ecosystems. It examines their integration with DevOps pipelines, orchestration tools, and Infrastructure-as-Code frameworks, and evaluates their ability to reduce compliance risk, improve audit readiness, and streamline governance. Additionally, the paper discusses the limitations and challenges of deploying autonomous engines, including issues of policy complexity, explainability, and integration with legacy infrastructure. Finally, it identifies future research directions such as AI-driven predictive compliance, intent-based policy models, and blockchain-enhanced auditing systems. Through this comprehensive review, the article highlights how autonomous policy engines transform cloud compliance from a reactive audit-driven process into a proactive, intelligent, and self-sustaining governance model.

DOI: http://doi.org/10.5281/zenodo.17882727

Published by: vikaspatanker

Authors: Asha L. Gurung

Abstract: Zero-Touch Provisioning (ZTP) represents a pivotal advancement in cloud infrastructure automation, designed to streamline deployment, reduce manual configuration errors, and accelerate scalability. In enterprise environments where cloud workloads fluctuate dynamically, provisioning speed and consistency are vital to maintaining performance and reliability. ZTP enables automated device and service configuration immediately upon connection to the network, eliminating the need for manual intervention. This review article explores the architectural principles, integration frameworks, and operational benefits of ZTP, with particular attention to its impact on enterprise cloud scalability. It evaluates how ZTP works synergistically with orchestration and automation platforms to enable elastic scaling and continuous availability in hybrid and multi-cloud environments. Furthermore, the paper identifies challenges such as configuration drift, security vulnerabilities, and legacy system constraints that can impede adoption. Finally, it highlights future innovations including AI-driven provisioning, intent-based networking, and predictive resource allocation as emerging directions for ZTP evolution. Through this comprehensive review, the study underscores the critical role of ZTP as a cornerstone of modern cloud management strategies, emphasizing its transformative potential in enhancing operational efficiency, agility, and scalability within large-scale enterprise cloud infrastructures.

DOI: http://doi.org/10.5281/zenodo.17882604

Published by: vikaspatanker

Authors: Nirosha K. Fernando

Abstract: The increasing digitization of critical infrastructure systems has magnified the urgency of implementing robust access control mechanisms to prevent cyber intrusions, data breaches, and operational disruptions. Critical infrastructures spanning energy grids, transportation systems, healthcare networks, financial institutions, and defense installations are foundational to national security and economic stability. As cyber threats evolve in sophistication, traditional access control mechanisms such as Role-Based Access Control (RBAC) and Discretionary Access Control (DAC) are proving inadequate in addressing the dynamic and complex threat landscape. This review explores how advanced access control models such as Attribute-Based Access Control (ABAC), Risk-Adaptive Access Control (RAdAC), Policy-Based Access Control (PBAC), and Zero Trust Architecture (ZTA) enhance the security posture of critical infrastructures. These models employ fine-grained, context-aware, and adaptive mechanisms that respond to real-time risk assessments and user behavior analytics. The paper synthesizes existing literature, government frameworks, and industry case studies to evaluate the effectiveness of these models in mitigating unauthorized access, insider threats, and lateral movement within critical systems. It also examines the integration of artificial intelligence and behavioral analytics in access control for predictive risk mitigation. Finally, the review identifies ongoing challenges, including interoperability, policy complexity, and compliance barriers, while suggesting future directions such as AI-driven automation, blockchain-based identity systems, and quantum-resistant access frameworks. The study concludes that advanced access control models represent an essential evolution toward proactive, adaptive, and resilient cybersecurity architectures for safeguarding critical infrastructures.

DOI: http://doi.org/10.5281/zenodo.17882494

Published by: vikaspatanker

Authors: Chathura S. Jayasinghe

Abstract: The rapid expansion of artificial intelligence (AI) applications has significantly increased global data center workloads, leading to rising energy demands and associated carbon emissions. Sustainable AI strategies are emerging as critical solutions to counteract these environmental challenges. This review examines the integration of AI-driven methodologies that promote sustainability within data center operations. It explores how AI can optimize energy use, reduce carbon footprint, and enable green computing practices through intelligent workload management, predictive cooling, and hardware efficiency improvements. The paper presents a synthesis of literature on carbon-aware computing, highlighting approaches such as AI-based energy forecasting, model optimization, and renewable energy integration. Furthermore, it evaluates case studies from industry leaders like Google and Microsoft, demonstrating quantifiable reductions in power usage effectiveness (PUE) and carbon usage effectiveness (CUE). Analytical frameworks and sustainability metrics are discussed to assess environmental performance, along with limitations such as data transparency and scalability challenges. Finally, the paper identifies future research opportunities in low-energy AI model development, federated learning for energy optimization, and policy-driven sustainability governance. The findings suggest that sustainable AI strategies can substantially mitigate the ecological footprint of modern computing infrastructures while ensuring computational resilience and efficiency. Through an interdisciplinary perspective, this review underscores the necessity of embedding sustainability principles into AI system design, operation, and lifecycle management. The collective insights affirm that the synergy between AI innovation and environmental responsibility is pivotal to achieving a carbon-neutral data center ecosystem.

DOI: http://doi.org/10.5281/zenodo.17882399

Published by: vikaspatanker

Authors: Malini R. De Silva

Abstract: The increasing complexity of regulatory requirements and the growing volume of enterprise data have made IT compliance auditing a challenging process for organizations. Traditional audit methods, reliant on static rule sets and manual inspection, often struggle to maintain accuracy and timeliness in dynamic digital environments. Cognitive analytics, integrating artificial intelligence, machine learning, and natural language processing, has emerged as a transformative approach to enhance audit precision and adaptability. By simulating human reasoning and contextual understanding, cognitive systems can analyze structured and unstructured data, interpret compliance documentation, and detect anomalies with greater accuracy. These systems not only automate evidence collection but also prioritize high-risk areas using predictive insights, thereby improving overall compliance efficiency. Furthermore, cognitive analytics enables continuous learning from historical audit outcomes, refining its models to adapt to evolving regulations. As a result, IT audit processes become more proactive, data-driven, and transparent. This paper reviews how cognitive analytics reshapes compliance auditing, improving reliability, speed, and accuracy while reducing operational overhead. The integration of cognitive intelligence represents a paradigm shift toward intelligent, adaptive, and autonomous compliance ecosystems capable of ensuring governance integrity in modern IT infrastructures.

DOI: http://doi.org/10.5281/zenodo.17880764

Published by: vikaspatanker

Authors: Anura J. Perera

Abstract: Artificial intelligence (AI) has revolutionized post-incident investigations by introducing automation, precision, and predictive intelligence into digital forensic processes. Traditional forensic techniques, dependent on manual log examination and human expertise, often struggle to handle the enormous data volumes and complex digital trails generated in modern cyber incidents. AI-based forensic systems leverage machine learning, natural language processing, and data mining to extract, analyze, and correlate evidence at unprecedented speed and accuracy. These systems can automatically detect anomalies, reconstruct attack timelines, and identify threat actors by learning from historical data patterns. The review explores the evolution of digital forensics, the integration of AI-driven tools, and the measurable improvements in investigation efficiency. It also highlights challenges such as model transparency, bias, explainability, and legal admissibility of AI-generated evidence. Moreover, it discusses emerging research opportunities, including explainable AI, blockchain-enabled evidence validation, and collaborative forensics through federated learning. By synthesizing academic research and industrial applications, this paper emphasizes that AI-based forensic systems are not merely tools but strategic enablers of resilience in cyber incident response. They represent a paradigm shift toward intelligent, adaptive, and self-learning investigation frameworks, crucial for addressing the growing sophistication of digital threats in enterprise and law enforcement environments.

DOI: http://doi.org/10.5281/zenodo.17880644

Published by: vikaspatanker