Authors: Priya Nair, Vikram Chauhan, Anika Deshpande, Vasudev Sharma

Abstract: Real time customer relationship management intelligence continues to evolve as organizations rely on advanced analytics to drive sales planning, revenue optimization, and customer engagement decisions. This study addresses persistent challenges related to data fragmentation, inconsistent contextualization of CRM information, and the limited adaptability of conventional predictive models within Salesforce environments. The research introduces a generative AI and large language model driven data fabric architecture designed to unify distributed CRM assets, automate semantic enrichment, and enhance predictive sales forecasting accuracy. A mixed methodological approach was adopted, combining architectural modeling, data flow simulation, and empirical evaluation using historical opportunity data, customer interaction logs, and multichannel engagement records. Findings indicate that the proposed model improves context aware forecasting precision, reduces data preparation overhead, and increases interpretability for frontline sales teams by enabling narrative style insights generated through domain tuned language models. The framework demonstrates the potential to streamline CRM operations, enhance cross system interoperability, and support adaptive decision making by integrating knowledge graphs and LLM based reasoning into the Salesforce ecosystem. The study contributes an extensible reference architecture for enterprise CRM analytics and offers a pathway for organizations seeking to modernize sales intelligence processes. The results hold significance for both practitioners and researchers by proving that next generation AI enabled data fabrics can meaningfully strengthen forecasting reliability, reduce operational friction, and support scalable data governance strategies across complex CRM landscapes.

DOI: http://doi.org/10.5281/zenodo.17894776

Published by: vikaspatanker

Authors: Priya S. Bhatia

Abstract: The increasing sophistication and frequency of cybersecurity threats have made effective vulnerability management a critical priority for organizations of all sizes. Traditional approaches to vulnerability assessment often rely on manual evaluation or static scoring systems, which can be slow, resource-intensive, and unable to adapt to evolving threat landscapes. AI-driven risk scoring has emerged as a transformative solution, enabling automated, data-driven prioritization of vulnerabilities based on likelihood, potential impact, and exploitability. By integrating machine learning, predictive analytics, and real-time threat intelligence, AI systems can evaluate vulnerabilities across heterogeneous environments, dynamically assign risk scores, and guide security teams in allocating remediation resources efficiently. This approach not only reduces response times but also enhances accuracy by identifying high-risk vulnerabilities that might otherwise be overlooked. The review examines the conceptual foundations, architectural frameworks, and enabling technologies behind AI-driven risk scoring, alongside methodologies such as supervised and unsupervised learning, anomaly detection, and graph-based analysis. Additionally, it highlights practical applications across enterprise networks, cloud environments, and critical infrastructure, illustrating measurable improvements in threat prioritization and remediation effectiveness. Finally, the review discusses challenges related to data quality, model interpretability, and integration with existing security operations, while outlining future research directions in explainable AI, adaptive models, and autonomous vulnerability management. AI-driven risk scoring is positioned as a strategic enabler for proactive, scalable, and resilient cybersecurity operations.

DOI: http://doi.org/10.5281/zenodo.17883072

Published by: vikaspatanker

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