Category Archives: Uncategorized

Authors: Nadiia Kudriashova, Alexander Mirza

Abstract: In recent years, generative artificial intelligence has gained traction, resulting in incredibly realistic synthetic multimedia content that can disseminate misinformation and mislead society. Deepfakes pose serious national security vulnerabilities since they enable sophisticated disinformation operations, foreign meddling, financial crime, and the erosion of faith in institutions. Deepfake detection and legal prosecution became an important agenda for contemporary nation-states. However, serious consequences of deepfakes for national security are still are not properly realized by legislative and regulatory establishment even in the countries of Five Eyes Alliance, known for its advanced cybersecurity awareness and policies. With this in mind, the article makes an attempt of integrating technological and legal domains of combating deepfake technology usage which causes serious consequences, within a single analytical model. Based on a combination of descriptive and exploratory research design, involving comprehensive literature review and semi-structured interviews with the experts across the fields of cybersecurity, machine learning, digital forensics, law, and ethics in the countries of Five Eyes Alliance (sample size 12 participants), the article outlines current landscape of deepfakes creation and detection technologies, as well as institutional awareness and legislative environment in the field of deepfakes law prosecution. The findings allowed making conclusion about scattered landscape of deepfakes identification and, at the same time, the evident lack of legal instruments to prevent deepfakes danger for national security even in the most developed countries, recently especially concerned with national security issues. The integration of findings allowed summarizing the essence of deepfakes serious consequences and developing integrative analytical model, based on Agile Security paradigm, implying predictive analysis of deepfake technology evolutive implications and options of appropriate criminal liability. The novelty of study lies in ‘organic’ combining of technological and legal planes of combating security danger of deepfakes, and the suggested integrative analytical model, based on Agile Security paradigm can become a starting point for further studies and developments in the field.

DOI: http://doi.org/10.5281/zenodo.17894064

 

Published by: vikaspatanker

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

Abstract: Understanding the drivers of workforce outcomes requires analytical methods capable of distinguishing correlation from true causal influence. Traditional predictive models commonly used in HR systems can forecast attrition, performance, or engagement shifts, yet they offer limited visibility into the underlying mechanisms that produce these changes. This paper introduces a causal AI approach that integrates SAP SuccessFactors operational data, SAP Analytics Cloud workforce metrics, and diverse multi source HR signals to estimate the effects of organizational interventions on measurable employee outcomes. The proposed framework combines structural causal modeling, treatment effect estimation, mediation analysis, and counterfactual reasoning to evaluate how learning pathways, compensation adjustments, managerial behaviors, mobility opportunities, and work environment conditions contribute to changes in performance, retention, and development trajectories. A unified data architecture harmonizes information from SuccessFactors modules with analytical layers in SAP Analytics Cloud to construct causal ready datasets that isolate confounders and quantify both direct and indirect effects. Empirical evaluation across representative HR scenarios demonstrates that causal models provide more actionable insight than conventional predictive methods by clarifying which interventions meaningfully influence workforce outcomes and under what conditions. The study argues that embedding causal AI within enterprise HR ecosystems supports evidence informed decision making, strengthens workforce planning accuracy, and enhances the strategic value of people analytics in complex organizational environments.

DOI: http://doi.org/10.5281/zenodo.17895700

Published by: vikaspatanker

Authors: Rohan Mehta, Arvind Sethi, Nisha Kulkarni, Vasudev Sharma

Abstract: Enterprises operating in complex digital ecosystems face accelerating growth in data volume, reporting demands, and governance obligations. Traditional rule-based automation remains insufficient for interpreting ambiguous business semantics, harmonizing heterogeneous information assets, or sustaining consistent reporting logic across distributed platforms. This study introduces a large language model powered semantic automation engine designed to unify enterprise reporting, knowledge extraction, and end-to-end data lifecycle governance. The research focuses on the central challenge of operationalizing generative models, retrieval-augmented reasoning, and dynamic semantic alignment to automate high-stakes analytical and compliance workflows while maintaining auditability, accuracy, and policy adherence. Using a mixed methodological approach that combines empirical prototyping, workflow instrumentation, and qualitative validation with enterprise architects, the study develops a layered architecture integrating semantic parsers, governance ontologies, vector-indexed knowledge repositories, and automated lineage reasoning. Findings show that LLM-driven inference strengthens metadata completeness, reduces manual reconciliation cycles, enhances cross-system reporting consistency, and improves lifecycle visibility from ingestion to archival. The study contributes a scalable framework for semantic automation, a reference ontology for enterprise reporting logic, and a set of design principles supporting trustworthy, context-aware automation across data-intensive environments.

DOI: http://doi.org/10.5281/zenodo.17895633

Published by: vikaspatanker

Authors: Buya Lekha, Pramani Kota, Nallireddy Anu, Vasudev Sharma

Abstract: Advances in sensor miniaturization, pervasive connectivity, and scalable cloud architectures have accelerated the adoption of Internet-of-Things solutions in healthcare, enabling continuous physiological monitoring, early disease detection, and remote clinical interventions. Yet, the complexity of heterogeneous sensor data, variable patient contexts, and unpredictable network conditions still limit reliability and predictive accuracy in real-world deployments. This study develops a hybrid deep-learning pipeline that integrates convolutional neural networks, bidirectional recurrent architectures, and attention-based temporal encoders with cloud-native event stream processing to enable real-time interpretation of multimodal physiological signals. The research examines how edge-assisted inference, micro-batch stream analytics, and distributed message brokers collectively enhance detection latency, anomaly classification, and model robustness. A mixed-method methodology combines simulation-driven performance evaluation with empirical analysis of IoT device logs and consumable EHR-derived datasets. Results demonstrate significant improvements in prediction accuracy, event-processing throughput, alert precision, and resilience against noisy sensor streams. The findings highlight the potential of hybrid AI pipelines to strengthen remote patient monitoring, chronic disease management, and population-health surveillance while addressing operational barriers tied to privacy, scalability, and interoperability.

DOI: https://doi.org/10.5281/zenodo.17920642

Published by: vikaspatanker

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