Category Archives: Uncategorized

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

Authors: Sonam D. Wangchuk

Abstract: Container orchestration systems have become the foundation of modern microservice architectures, enabling automated deployment, scaling, and management of containerized workloads. As organizations shift to cloud-native environments, the security of orchestration platforms such as Kubernetes, Docker Swarm, and OpenShift has emerged as a critical determinant of overall system reliability. Orchestration security ensures that applications maintain consistent performance, resilience, and integrity even in the face of evolving cyber threats. However, inadequate security controls within orchestration layers such as unprotected APIs, misconfigured network policies, and unencrypted communications can lead to severe vulnerabilities that disrupt service continuity. This review explores the intricate relationship between container orchestration security and microservices reliability. It analyzes the evolution of orchestration systems, common security challenges, and the impact of security mechanisms on microservices performance and stability. Additionally, it discusses best practices and emerging frameworks that integrate security and automation to ensure sustained reliability. The paper concludes by emphasizing the need for adaptive, intelligent orchestration mechanisms that integrate security and reliability by design, ensuring robust and scalable cloud-native infrastructures capable of withstanding future operational and security challenges.

DOI: http://doi.org/10.5281/zenodo.17880571

Published by: vikaspatanker

Authors: Keshav M. Rana

Abstract: Cloud-native observability platforms have revolutionized how organizations understand, measure, and improve service performance in distributed computing environments. Unlike traditional monitoring tools that focus on static metrics, observability provides a holistic, data-driven view of system behavior through the collection and correlation of metrics, logs, and traces. In dynamic environments powered by containers, microservices, and Kubernetes orchestration, such platforms enable real-time insights into performance bottlenecks, latency variations, and service dependencies. This comprehensive visibility helps teams identify root causes, optimize system efficiency, and enhance user experience. However, observability in cloud-native systems also introduces challenges, including data volume management, complex instrumentation, and high computational costs. Modern observability platforms address these issues through automation, AI-driven analytics, and scalable data architectures capable of handling multidimensional telemetry. This review explores the evolution, architecture, and influence of observability platforms on service performance visibility, highlighting their role in proactive fault detection, system resilience, and decision-making efficiency. It also examines the challenges and future directions shaping the next generation of observability frameworks that promise self-optimizing and predictive performance management in cloud-native ecosystems.

DOI: http://doi.org/10.5281/zenodo.17879382

Published by: vikaspatanker

Authors: Sandhya R. Bista

Abstract: Generative Artificial Intelligence (AI) has rapidly become a transformative yet paradoxical force in the domain of cybersecurity. Its dual-edged nature capable of both fortifying defenses and amplifying cyber threats has redefined the way organizations approach malware detection, prevention, and response. Traditional cybersecurity models, which rely heavily on signature-based detection and heuristic methods, are increasingly inadequate against polymorphic, evasive, and zero-day malware variants. These conventional systems lack the adaptive capacity to counter attackers who continuously modify malicious code to escape static defense algorithms. In contrast, generative AI introduces a new paradigm in which defense systems evolve dynamically, learning from both real and simulated threats to anticipate and neutralize future attacks before they occur. Generative AI models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based architectures have been instrumental in driving this evolution. GANs, for instance, can simulate sophisticated attack patterns, enabling security systems to train against artificially generated malware samples that replicate real-world adversarial behavior. Similarly, transformer-based models enhance contextual awareness and anomaly detection by processing vast streams of network, behavioral, and endpoint data in real time. This fusion of generative modeling and adaptive learning fosters proactive defense strategies capable of identifying subtle deviations indicative of malicious intent long before damage is inflicted.

DOI: http://doi.org/10.5281/zenodo.17879335

Published by: vikaspatanker

Authors: Rohan C. Shrestha

Abstract: Phishing attacks have become a critical cybersecurity threat in the digital era, targeting individuals, businesses, and organizations to obtain sensitive information such as login credentials, financial data, and personal identification details. The sophistication of modern phishing attacks has evolved beyond simple spam emails, encompassing spear phishing, clone phishing, smishing, and vishing, making detection increasingly difficult. Traditional detection mechanisms, including rule-based systems, blacklists, and heuristic approaches, often fail to detect new or obfuscated attacks and are prone to high false-positive rates, which can compromise security operations. Deep learning (DL), a subset of artificial intelligence, offers promising solutions to these challenges through its ability to automatically extract complex features, learn non-linear relationships, and detect patterns that are imperceptible to human analysts or conventional machine learning algorithms. This review examines the application of deep learning in enhancing phishing detection mechanisms, focusing on architectures such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and autoencoders. The discussion highlights how these models improve detection accuracy, adaptability, and resilience against evolving phishing strategies. Furthermore, the review explores the utilization of diverse datasets, challenges in computational requirements and adversarial robustness, and the role of hybrid and ensemble models in optimizing performance. Finally, future directions, including explainable AI, multi-modal detection systems, and adaptive reinforcement learning frameworks, are addressed. Overall, deep learning provides a transformative approach to phishing detection, offering enhanced efficiency, robustness, and proactive threat mitigation, while opening avenues for continued research into intelligent, adaptive cybersecurity solutions.

DOI: http://doi.org/10.5281/zenodo.17879281

Published by: vikaspatanker