International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
Machine Learning-Based Intelligent Systems for Predictive Business Analytics and Enterprise Process Optimization
Machine learning-based intelligent systems have become an important foundation for transforming enterprise decision-making, predictive business analytics, and process optimization. Organizations generate substantial volumes of structured and unstructured data through enterprise resource planning systems, customer relationship management platforms, financial applications, supply-chain systems, websites, and digital customer interactions. Conventional analytical approaches often describe historical performance but provide limited capabilities for predicting future outcomes or recommending optimal actions. Machine learning enables organizations to extract patterns from large datasets, forecast business outcomes, identify operational anomalies, classify customers and transactions, and support automated decision-making. When integrated with enterprise process management, these capabilities can improve productivity, reduce operational costs, enhance customer experience, and strengthen resource allocation. This essay examines the role of machine learning-based intelligent systems in predictive business analytics and enterprise process optimization. It discusses supervised learning, unsupervised learning, deep learning, predictive forecasting, process mining, intelligent automation, and prescriptive decision support. The study proposes a comprehensive research methodology involving enterprise data collection, preprocessing, feature engineering, machine-learning model development, process analysis, predictive evaluation, optimization, and organizational assessment. Key performance indicators include prediction accuracy, processing time, operational cost, resource utilization, error rate, throughput, and customer satisfaction. The study further considers challenges associated with data quality, model interpretability, cybersecurity, privacy, algorithmic bias, organizational adoption, and continuous model monitoring. The proposed framework demonstrates how machine learning can transform enterprise analytics from retrospective reporting into predictive and continuously optimized decision-making.
Machine learning, intelligent systems, predictive business analytics, enterprise process optimization, artificial intelligence, predictive analytics, process mining, business intelligence, enterprise automation, data analytics, decision support, process management
Explainable AI for Adaptive Enterprise Cybersecurity with Secure Cloud-Native Threat Intelligence Frameworks
Enterprise cybersecurity environments are becoming increasingly complex due to cloud-native applications, distributed infrastructures, hybrid networks, sophisticated cyberattacks, and rapidly changing threat landscapes. Conventional security mechanisms frequently struggle to detect emerging threats and provide transparent explanations for automated decisions. This research proposes an Explainable Artificial Intelligence (XAI) framework for adaptive enterprise cybersecurity that integrates machine learning, threat intelligence, cloud-native security controls, continuous monitoring, and interpretable decision-making. The proposed framework collects security telemetry from cloud workloads, network traffic, identity systems, applications, endpoints, containers, and security information and event management platforms. Machine learning models analyze these heterogeneous data sources to identify anomalies, classify threats, prioritize risks, and predict potential attacks. Explainability techniques such as SHAP-based feature attribution, LIME-based local explanations, rule-based reasoning, and confidence scoring are incorporated to clarify why security alerts and automated responses are generated. A cloud-native architecture supports scalable threat detection through containerized security services, microservices, orchestration, automated policy enforcement, and secure API integration. The methodology includes threat-data collection, preprocessing, feature engineering, model development, explainability analysis, adaptive response orchestration, and experimental evaluation. The framework aims to improve detection accuracy, reduce false positives, enhance analyst trust, accelerate incident response, and strengthen security governance. The proposed approach demonstrates how explainable and adaptive AI can support transparent, scalable, and resilient cybersecurity operations across modern enterprise cloud environments.
Explainable AI, Enterprise Cybersecurity, Adaptive Security, Cloud-Native Security, Threat Intelligence, Machine Learning, Cyber Threat Detection, Security Analytics, SHAP, LIME, Cloud Security, Anomaly Detection, Automated Incident Response
Intelligent Enterprise Integration across Distributed Systems using AI for Cloud API Services and Real-Time Data Processing
The increasing complexity of enterprise information environments has created a strong demand for intelligent integration across distributed systems. Organisations increasingly operate cloud applications, legacy platforms, microservices, databases, Internet of Things devices, and external services that continuously generate and exchange large volumes of data. Artificial intelligence (AI), cloud-based application programming interfaces (APIs), and real-time data processing provide important capabilities for integrating these heterogeneous environments while improving operational intelligence and responsiveness. AI can analyse complex datasets, automate decision-making, identify patterns, and support predictive business processes. Cloud API services provide standardised mechanisms for communication among distributed applications, enabling interoperability, scalability, and controlled access to enterprise resources. Real-time data processing allows organisations to analyse events as they occur rather than relying exclusively on historical batch processing. This paper examines the integration of distributed enterprise systems through AI-enabled cloud API services and real-time data processing. Particular attention is given to scalability, interoperability, security, data quality, latency, intelligent automation, and governance. The study argues that intelligent enterprise integration should be designed as a coordinated architecture in which APIs connect distributed services, cloud infrastructure provides elastic computational resources, AI transforms data into actionable intelligence, and real-time processing supports timely organisational responses. The integration of these capabilities can improve operational efficiency, customer experiences, predictive decision-making, and organisational agility while introducing challenges involving cybersecurity, system complexity, model reliability, data governance, and cloud dependency.
intelligent enterprise integration, distributed systems, artificial intelligence, cloud computing, API services, real-time data processing, machine learning, enterprise architecture, data analytics, interoperability, cloud APIs, intelligent automation
Modernizing Enterprise Cybersecurity Through AI-Driven Threat Prevention in Distributed Cloud Environments
The rapid adoption of distributed cloud computing, hybrid infrastructures, multi-cloud architectures, edge services, and remote work has transformed enterprise information systems while substantially expanding the cybersecurity attack surface. Traditional security approaches based primarily on static rules, signatures, perimeter controls, and manual investigation are increasingly inadequate against sophisticated, rapidly changing, and distributed cyber threats. Artificial intelligence (AI), machine learning (ML), deep learning (DL), behavioral analytics, and automated response technologies provide opportunities to modernize enterprise cybersecurity by identifying abnormal activities, predicting emerging threats, prioritizing risks, and initiating preventive responses in near real time. This paper examines an AI-driven cybersecurity framework for distributed cloud environments, emphasizing proactive threat prevention rather than exclusively reactive incident detection. The study reviews existing research on cloud intrusion detection, AI-enabled anomaly detection, zero-trust security, threat intelligence, federated learning, and automated security operations. A qualitative research methodology based on systematic literature analysis and comparative evaluation is proposed to assess AI-driven prevention mechanisms. The paper identifies significant advantages, including scalability, faster detection, continuous monitoring, adaptive defense, and reduced security-operations workload. It also examines limitations involving data quality, adversarial attacks, explainability, privacy, computational costs, false positives, regulatory compliance, and dependence on high-quality telemetry. The study concludes that AI should complement rather than replace human cybersecurity expertise and conventional security controls.
Artificial Intelligence, Machine Learning, Cybersecurity, Cloud Computing, Distributed Cloud, Threat Prevention, Anomaly Detection, Zero Trust, Threat Intelligence, Automated Response, Cloud Security, Cyber Resilience
Artificial Intelligence, Machine Learning, Cybersecurity, Cloud Computing, Distributed Cloud, Threat Prevention, Anomaly Detection, Zero Trust, Threat Intelligence, Automated Response, Cloud Security, Cyber Resilience.
Software Supply Chain Security in Cloud-Native Systems: A Zero Trust Framework for Securing APIs, Containers, Dependencies, and CI/CD Pipelines
Software supply chain attacks have become a serious security concern in cloud-native environments, where applications depend on interconnected APIs, container images, open-source libraries, build systems, registries, and automated CI/CD pipelines. A weakness in any of these components can provide attackers with a path to alter source code, introduce malicious dependencies, steal credentials, compromise build processes, or deploy unauthorized software into production. Traditional perimeter-based security models are poorly suited to these distributed and highly automated environments because they often depend on implicit trust between internal systems and development stages. This article proposes a Zero Trust software supply chain security framework that applies continuous verification, least-privilege access, strong identity controls, software provenance, artifact integrity validation, and policy-based enforcement across the cloud-native software lifecycle. The framework addresses four major security domains: APIs, containers and cloud-native workloads, third-party dependencies, and CI/CD pipelines. It also incorporates software bills of materials, cryptographic signing, workload identity, secure build practices, runtime monitoring, and automated trust evaluation. A threat-control mapping and risk assessment model are used to examine how the proposed controls can reduce exposure to dependency poisoning, repository compromise, artifact tampering, credential theft, container attacks, and unauthorized deployment. The study provides a structured approach for integrating previously fragmented security controls into a unified supply chain security model and establishes a basis for future empirical evaluation in cloud-native production environments.
Software Supply Chain Security, Zero Trust, Cloud-Native Security, API Security, Container Security, CI/CD Security, Dependency Security, Software Provenance, DevSecOps, Software Bill of Materials.
Predictive AI for High-Availability Infrastructure Management Across Distributed and Hybrid Cloud Systems
The increasing dependence on distributed and hybrid cloud infrastructures has created significant challenges for maintaining high availability, reliability, performance, and operational continuity. Conventional infrastructure management approaches remain largely reactive, relying on predefined thresholds, manual intervention, and retrospective analysis of incidents. Predictive artificial intelligence (AI) provides an alternative approach by using machine learning, time-series forecasting, anomaly detection, and intelligent decision-making to anticipate infrastructure failures before they affect service availability. This essay examines the application of predictive AI to high-availability infrastructure management across distributed and hybrid cloud environments. It explores how predictive models can integrate telemetry from cloud platforms, data centres, networks, applications, containers, and edge systems to identify early indicators of failures and performance degradation. The literature demonstrates that AI-driven predictive maintenance, anomaly detection, workload forecasting, and automated remediation can reduce downtime and improve resource utilisation. However, challenges involving heterogeneous infrastructure, data quality, model drift, explainability, cybersecurity, interoperability, and autonomous decision-making remain substantial. A research methodology is proposed using a mixed-methods, experimental approach combining infrastructure telemetry, machine-learning models, controlled failure scenarios, and comparative evaluation against conventional monitoring systems. The proposed framework aims to establish measurable relationships between predictive AI capabilities and high-availability outcomes in complex hybrid infrastructure environments.
Predictive AI, high availability, cloud computing, hybrid cloud, distributed systems, infrastructure management, machine learning, anomaly detection, predictive maintenance, automated remediation, reliability, observability
Intelligent Cloud Migration Strategies using AI for Enterprise Legacy Application Modernization Frameworks
Enterprise organizations increasingly seek to modernize legacy applications by migrating them to cloud environments to improve scalability, flexibility, availability, maintainability, and operational efficiency. However, legacy applications frequently contain tightly coupled architectures, outdated technologies, undocumented dependencies, obsolete interfaces, and business-critical logic that make conventional migration approaches costly and risky. Artificial intelligence provides new opportunities to improve migration decision-making through automated application discovery, dependency analysis, workload classification, modernization-path selection, code transformation, risk prediction, testing, and post-migration optimization. This research proposes an intelligent cloud migration strategy that integrates AI techniques with an enterprise legacy application modernization framework. The proposed approach combines machine learning, natural language processing, generative AI, knowledge graphs, predictive analytics, AI agents, cloud assessment tools, and automated testing to support migration across rehosting, replatforming, refactoring, repurchasing, and rebuilding strategies. The methodology adopts a design science and experimental research approach involving literature analysis, framework development, prototype implementation, controlled migration experiments, and quantitative and qualitative evaluation. The framework evaluates application complexity, dependencies, business criticality, security requirements, performance characteristics, migration cost, and modernization benefits before recommending an appropriate migration strategy. Evaluation focuses on migration effort, cost, application performance, defect rates, modernization coverage, migration risk, downtime, automation level, and decision accuracy. The research aims to establish a systematic and intelligent approach for reducing migration complexity while improving the reliability and business value of enterprise cloud modernization.
Intelligent Cloud Migration, Artificial Intelligence, Legacy Application Modernization, Enterprise Cloud Computing, Machine Learning, Generative AI, AI Agents, Application Modernization, Cloud Transformation, Cloud Migration Strategy, Refactoring, Replatforming, Rehosting, Knowledge Graphs, Predictive Analytics, Automated Code Transformation
Scalable Multi-Cloud Infrastructure Management using AI-Powered DevOps Automation and Predictive Analytics
The rapid adoption of cloud computing has transformed enterprise IT environments by enabling flexible, scalable, and cost-effective infrastructure solutions. However, managing complex multi-cloud architectures involving diverse platforms, services, and operational requirements has become increasingly challenging. Traditional infrastructure management approaches often struggle with scalability, resource optimization, security monitoring, and real-time decision-making. This research explores scalable multi-cloud infrastructure management through the integration of artificial intelligence (AI)-powered DevOps automation and predictive analytics. The study examines how AI-driven techniques, including machine learning, intelligent automation, anomaly detection, and predictive modeling, can improve cloud resource allocation, system reliability, operational efficiency, and proactive incident management. The proposed approach combines DevOps practices with AI capabilities to create an adaptive infrastructure management framework capable of monitoring heterogeneous cloud environments and automatically responding to changing workloads. Predictive analytics enables organizations to anticipate performance degradation, resource demands, and potential failures before they occur, reducing downtime and improving service continuity. The research methodology focuses on analyzing existing cloud management practices, evaluating AI-based automation strategies, and developing a conceptual framework for intelligent multi-cloud operations. Findings suggest that AI-powered DevOps automation can significantly enhance scalability, reduce operational complexity, and support autonomous infrastructure management. The study highlights the future potential of AI-enabled multi-cloud systems in achieving efficient, resilient, and self-optimizing digital infrastructures.
Multi-cloud infrastructure, Artificial intelligence, DevOps automation, Predictive analytics, Cloud computing, Machine learning, Infrastructure management, Automation, Scalability, Cloud optimization
Cost optimization strategies (tagging, rightsizing, capacity planning) in enterprise cloud operations
In today’s digital era, enterprise cloud computing is the backbone of digital transformation, providing scalability, flexibility, and rapid service deployment. But with more cloud adoption has come rising operational costs stemming from poor resource utilization, inefficient workload management, and lack of governance. The study discusses multiple options for optimizing costs in enterprise cloud operations, focusing on key tools and processes to reduce costs while maintaining system performance, such as resource tagging, rightsizing, and capacity planning. The study examines well-known cloud optimization frameworks and operational best practices to understand cloud costs, improve resource accountability, and optimize infrastructure utilization across public, private, and hybrid cloud settings. It also examines the roles of automated monitoring and governance policies, workload forecasting, and intelligent resource allocation in sustainable cloud financial management. The conversation underscores the importance of embedding ongoing cost tracking into decision-making to optimize idle resources, avoid waste, and improve budget consistency. Furthermore, the study highlights issues in implementing the system, including inconsistent governance practices, fluctuating workloads, and the complexity of managing multi-cloud environments, and offers practical solutions to address these limitations. The results show that companies with a structured tagging policy, proactive rightsizing, and data-driven capacity planning achieve higher levels of operational efficiency, improved resource utilization, greater cost transparency, and long-term financial sustainability. The study found that an enterprise cloud cost-optimization program, supported by cloud governance, automation, and continuous performance monitoring, is essential for maximizing enterprise cloud investments and ensuring competitive advantage in today’s highly complex cloud landscape.
Cloud cost optimization, enterprise cloud operations, resource tagging, rightsizing, capacity planning, cloud governance, FinOps, cloud resource management, workload optimization, multi-cloud computing.
Developing AI Enabled Cloud Native Security Frameworks for Enterprise Data Protection and Threat Intelligence
The rapid adoption of cloud-native architectures has transformed enterprise computing by enabling scalable, resilient, and agile digital services. However, these advancements have also introduced sophisticated cyber threats that target distributed workloads, microservices, containers, Kubernetes clusters, APIs, and cloud infrastructure. Traditional security mechanisms are insufficient to protect dynamic cloud-native environments due to their limited automation, static policy enforcement, and inability to respond to real-time threats. This research proposes an Artificial Intelligence (AI)-enabled cloud-native security framework that integrates machine learning, deep learning, behavioral analytics, threat intelligence, zero-trust security, and automated incident response for comprehensive enterprise data protection. The framework continuously monitors cloud resources, identifies anomalous activities, predicts potential cyberattacks, prioritizes security risks, and orchestrates automated mitigation strategies. AI-driven analytics enhance threat detection accuracy while minimizing false positives through adaptive learning from historical and real-time security events. The proposed framework also incorporates cloud governance, identity and access management, encryption, security orchestration, and compliance monitoring to ensure regulatory adherence and operational resilience. By combining intelligent automation with cloud-native security principles, enterprises can significantly improve cyber resilience, accelerate incident response, protect sensitive information, and maintain secure digital transformation initiatives. The proposed model provides organizations with a scalable, adaptive, and intelligent cybersecurity architecture capable of defending modern enterprise cloud ecosystems against evolving cyber threats.
Artificial Intelligence, Cloud Native Security,
Enterprise Data Protection, Threat Intelligence,
Zero Trust Security, Kubernetes Security, Container Security, Machine Learning, Deep Learning, Cloud Computing, Cybersecurity, Security Orchestration, Identity and Access Management, Predictive Analytics, Security Automation.
Generative AI in Creative Writing Pedagogy: Opportunities, Authorship Dilemmas, and Ethical Boundaries in Higher Education
Large language models capable of fluent narrative, poetry, and dialogue have entered the creative writing classroom faster than institutions have been able to reason about them. Responses so far cluster at two unsatisfying poles: prohibition, which is unenforceable and pedagogically evasive, and uncritical adoption, which risks hollowing out the very capacities creative writing programmes exist to develop. This paper charts a middle path. We first distinguish the pedagogical functions of creative writing instruction — craft acquisition, voice development, critical reading, revision discipline, and creative confidence — and analyse how generative AI interacts with each, showing that its effects are function-specific rather than uniformly beneficial or harmful. We then map legitimate instructional use cases (ideation partner, style laboratory, revision interlocutor, pastiche and parody object, workshop simulator) against their characteristic risks, including voice displacement, authorship ambiguity, homogenisation of style, and assessment distortion, and propose safeguards for each in a structured framework. Finally, we articulate boundary conditions — uses that should remain out of bounds in credit-bearing creative work — and a disclosure-based assessment architecture that makes process, not artefact, the primary object of evaluation. The paper offers writing programmes a principled basis for policy that is enforceable, teachable, and honest about both the promise and the cost of writing with machines.
Generative AI; Creative writing pedagogy; Authorship; Academic integrity; Higher education; Large language models; Assessment design; AI ethics
Human-Computer Interaction Design for Low-Literacy Users: A Framework for Inclusive Public Service Portals in Emerging Economies
Public services in emerging economies are moving online faster than the populations they serve are acquiring the textual, digital, and procedural literacies those services assume. The result is a paradox of digital governance: portals built to widen access instead erect new barriers for the citizens who depend on public services most. This paper synthesises three decades of human-computer interaction (HCI) research on low-literacy and first-time technology users into a design framework specifically targeted at government service portals. We first characterise the barrier landscape across four literacy dimensions — textual, digital, procedural, and linguistic — and show how conventional portal design compounds them. We then derive nine design principles, organised into perception (how information is presented), navigation (how users move through tasks), and trust (how users verify, recover, and seek help), and map each principle to concrete interface techniques with implementation notes for resource-constrained government IT environments. Finally, we propose an evaluation protocol combining task-based usability testing with assisted-use observation, reflecting the empirical reality that many low-literacy users access e-government through intermediaries. The framework offers portal designers, e-governance agencies, and civic technologists an actionable bridge between inclusive-design research and the procurement-driven practice of public sector software development.
Human-computer interaction; Low literacy; E-government; Inclusive design; Public service portals; Emerging economies; Accessibility; Intermediated use.
Algorithmic Curation and Cultural Memory: Ethical Implications of AI-Driven Archiving in Digital Humanities Projects
Digital humanities projects increasingly rely on machine learning pipelines to select, classify, describe, and surface cultural materials at scales that exceed human archival capacity. While algorithmic curation promises efficiency and discoverability, it also transfers consequential judgments about what a society remembers, and how, from accountable human institutions to opaque computational systems. This paper develops a conceptual framework for analysing the ethical implications of AI-driven archiving along four dimensions: selection (what enters the archive), description (how items are represented), retrieval (what is surfaced to whom), and preservation (what persists over time). Drawing on cultural memory theory and the ethics of information, we argue that algorithmic curation does not merely accelerate archival work but restructures the memory function itself, introducing distinctive risks of representational harm, silent exclusion, contextual flattening, and memory drift. We propose a set of governance principles — provenance transparency, contestability, plural curation, and periodic human re-review — and map them to concrete design interventions for digital humanities infrastructure. The paper contributes a vocabulary and an evaluative checklist that project teams, funders, and cultural institutions can use when deciding where, and under what safeguards, algorithmic curation is appropriate.
Algorithmic curation; Cultural memory; Digital humanities; AI ethics; Digital archives; Representational harm; Archival governance
Hybrid Cloud-Based Artificial Intelligence Framework for Enterprise Workflow Optimization and Predictive Operational Intelligence
The increasing complexity of enterprise operations, rapidly changing market conditions, and growing volumes of organizational data have accelerated the adoption of artificial intelligence (AI) and hybrid cloud computing technologies. Enterprises require intelligent systems capable of optimizing workflows, improving resource utilization, and generating predictive insights to support strategic decision-making. This research proposes a hybrid cloud-based artificial intelligence framework for enterprise workflow optimization and predictive operational intelligence. The proposed framework integrates hybrid cloud infrastructure, machine learning algorithms, business process analytics, automation technologies, and predictive intelligence mechanisms to enhance operational efficiency and organizational agility. The framework enables enterprises to process large-scale data across private and public cloud environments while maintaining security, scalability, and flexibility. This study adopts a conceptual and qualitative research methodology based on systematic literature analysis to examine existing developments in AI-enabled workflow management, hybrid cloud architectures, enterprise analytics, and predictive intelligence. The findings indicate that AI-powered hybrid cloud environments can improve process automation, optimize resource allocation, enhance forecasting accuracy, and support proactive operational management. The research highlights the importance of data governance, cloud interoperability, cybersecurity, and continuous AI model improvement for successful implementation. The proposed framework provides organizations with a strategic approach to achieving intelligent workflow optimization and sustainable digital transformation through adaptive, scalable, and data-driven enterprise systems.
Hybrid Cloud Computing, Artificial Intelligence,
Predictive Operational Intelligence, Machine Learning, Business Process Automation, Cloud Analytics, Intelligent Systems, Digital Transformation, Enterprise Data Management, Predictive Analytics, Cloud Infrastructure
Advancing Autonomous Cyber Threat Detection and Incident Response Using Explainable Artificial Intelligence and Large Language Models in Enterprise Computer Networks
The rapid evolution of enterprise cyber threats has exposed the limitations of conventional security systems that rely heavily on static rules and signature-based detection. This study explores the integration of Explainable Artificial Intelligence (XAI) and Large Language Models (LLMs) to advance autonomous cyber threat detection and incident response in enterprise computer networks. The proposed framework combines explainable machine learning, intelligent threat correlation, natural language reasoning, and automated incident response to improve the accuracy, transparency, and timeliness of cybersecurity operations. By enabling interpretable decision-making and contextual analysis of security logs, network traffic, and threat intelligence, the framework enhances analyst trust while reducing response time to sophisticated cyberattacks. Furthermore, the study examines the role of XAI in mitigating black-box limitations and investigates how LLMs support security operations through automated threat analysis, root cause identification, and adaptive defense strategies. The findings suggest that integrating explainable intelligence with autonomous cybersecurity systems strengthens enterprise resilience, improves operational efficiency, and provides a scalable foundation for next-generation cyber defense in increasingly complex digital environments.
Explainable Artificial Intelligence (XAI), Large Language Models (LLMs), Autonomous Cyber Threat Detection, Incident Response Automation, Enterprise Network Security, Threat Intelligence, Cybersecurity Analytics.
Transforming Enterprise Cloud Computing Through Explainable AI Driven Security Analytics and Intelligent Automation
Enterprise cloud computing has become a fundamental component of digital transformation by enabling organizations to achieve scalability, flexibility, cost efficiency, and rapid service deployment. However, the growing adoption of cloud platforms has introduced increasingly sophisticated cybersecurity threats, including advanced persistent attacks, insider threats, data breaches, and misconfigurations. Traditional security mechanisms often struggle to detect complex attack patterns and provide transparent decision-making processes. Explainable Artificial Intelligence (XAI) has emerged as a promising solution by combining the predictive capabilities of artificial intelligence with interpretable and transparent decision support. XAI-driven security analytics enables security professionals to understand how threats are detected, improves trust in automated systems, supports regulatory compliance, and facilitates faster incident response. Simultaneously, intelligent automation streamlines repetitive security operations such as threat detection, vulnerability assessment, access management, and incident remediation, thereby reducing operational costs and minimizing human error. This study explores the integration of explainable AI-driven security analytics with intelligent automation in enterprise cloud computing environments. It examines current technological developments, implementation strategies, and organizational benefits while addressing challenges related to data privacy, model transparency, scalability, and governance. The research concludes that the convergence of explainable AI and intelligent automation significantly strengthens cloud security, enhances operational resilience, and supports sustainable digital transformation across modern enterprises.
Cybersecurity, Machine learning, Threat detection, Digital transformation, Automation, Risk management
Treasury Single Account (TSA) Implementation: Enhancing Transparency and Fiscal Discipline in Public Finance
In 2015, Nigeria had a major public financial management reform, called the Treasury Single Account (TSA), which focuses on the transparency and the discipline of fiscal behaviour. The present study was conducted based on descriptive survey design and the use of TSA was empirically assessed. A total of 285 accounting and finance officers from 12 federal Ministries, Departments and Agencies (MDAs) were used for obtaining primary data using structured questionnaires. Descriptive statistical analysis, chi- square test and multiple regression analysis were used to analyse the data. The findings showed that there was a significant positive effect of the implementation of TSA on transparency (χ² = 78.642; p < 0.001) and fiscal discipline (β = 0.648; p < 0.001) with the variation in fiscal discipline being explained by 61.5%. Despite that, there are still some issues so far not resolved like poor infrastructure in Information and Communication Technology and partial compliance. The study, suggests that the reform of TSA is effective when optimally used to strengthen public finance management.
Treasury Single Account, TSA, Transparency, Fiscal Discipline, Public Financial Management, Accountability, Nigeria etc.
Architecting Graph Neural Network Enabled Cloud for Intelligent Enterprise Identity Management and Secure Data Synchronization
As modern enterprises expand across hybrid and multi-cloud environments, traditional Identity and Access Management (IAM) systems suffer from fragmentation, identity silos, and high vulnerability to privilege escalation and lateral movement attacks. Standard perimeter-based and tabular machine learning frameworks analyze log data as isolated rows, failing to model the complex, multi-hop operational relationships between distributed entities. This paper introduces a novel, Graph Neural Network (GNN)-enabled distributed cloud ecosystem designed for unified enterprise identity management and real-time, secure data synchronization. By structuring identities, credentials, user sessions, cloud services, and microservices as dynamic, heterogeneous graphs, the architecture utilizes Relational Graph Convolutional Networks (R-GCN) and Graph Attention Networks (GAT) to continuously learn contextual, topology-aware access baselines. To achieve secure cross-region synchronization without compromising privacy, the model employs a Federated Heterarchical Graph SAGE mechanism coupled with zero-knowledge proof (ZKP) identity verification protocols and post-quantum cryptographic primitives. Empirical evaluations demonstrate that the proposed ecosystem achieves superior anomaly detection precision and recall in detecting zero-day credential compromise compared to baseline sequence-based models. Concurrently, it reduces cross-cloud synchronization latency and bandwidth overhead, providing a resilient, topology-aware zero-trust framework for enterprise-scale multi-cloud infrastructures.
Graph Neural Networks, Distributed Cloud Ecosystem,
Secure Data Synchronization, Zero-Trust Architecture, Dynamic Heterogeneous Graphs, Privacy-Preserving Computing.
Econometric Modeling for Liquidity Stress Testing Under Basel III: A Dynamic Panel Data Approach
With the advent of Basel III banking regulatory standards and the growing complexity of financial markets, banking institutions have incorporated liquidity risk as a key consideration. The importance of effective liquidity stress testing for assessing a bank's capacity to absorb adverse economic and financial conditions and to sustain its funding and operations. This study sets up an econometric framework for liquidity stress testing based on dynamic panel data model and discusses the factors that affect the liquidity resilience of banking institutions. The proposed model includes both financial variables related to the bank and macroeconomic variables, in order to reflect the persistence and dynamics of the liquidity situation over time. To overcome the endogeneity, unobserved heterogeneity and serial dependence problems, dynamic panel estimation methods are used to achieve greater reliability in empirical estimates. This framework assesses the impact of sources and uses of liquidity, capital levels, profitability, funding structure and macro-economic conditions on the liquidity stress under various stress scenarios. The results should be solid for the purposes of improving liquidity risk management, improving the macroprudential supervision framework and for regulatory decision making. The study adds to the literature by providing an integrated econometric model that can aid in the ability to more accurately predict liquidity stress-test outcomes and enhance resilience in the financial system in the current and future regulatory and economic landscapes.
Basel III, Liquidity Stress Testing, Dynamic Panel Data, Econometric Modeling, Liquidity Coverage Ratio, Net Stable Funding Ratio, Banking Stability, Generalized Method of Moments (GMM), Financial Risk Management, Macroprudential Regulation.
Trend Analysis in Recurring IT Security Findings: What Repeated Vulnerabilities Reveal About Systemic Control Weaknesses
While organizations invest in cybersecurity technologies, vulnerability management programs, and regulatory compliance initiatives, recurrent IT security findings continue to be a problem. When the same or very similar vulnerabilities are found in several assessment cycles it suggests that there are more significant control issues in the system and not just technical issues. This study examines trends in recurring IT security findings and looks at the implications of recurring vulnerabilities in terms of underlying failures of organizational, technical, process and human control. The research takes a trend analysis approach, and it involves studying historical vulnerability assessment reports, security audit results, penetration tests results and security logs to determine trends in the recurrence of vulnerabilities in enterprise environments. It breaks down the vulnerabilities that are common across systems into the following major areas: insecure application programming interfaces, weak authentication mechanisms, patch management failures, system misconfigurations, and insecure access controls. In addition, the research investigates the connection between recurring findings and system deficiencies such as ineffective governance structures, security awareness, inadequate secure software development practices, poor configuration management, and lack of continuous monitoring capabilities. The results indicate that repeated vulnerabilities are good predictors of lack of security maturity and risk management in an organization. The study highlights the need for a comprehensive and forward-looking approach to resolving recurring security issues, which involves technical fixes, improved governance, employee training, ongoing surveillance, and risk assessment. The study offers significant insights for both practitioners in the field of security and for those responsible for auditing and for leadership within the organization in order to improve the security resilience and lower the overall risk exposure faced by the organization in the long term.
recurring vulnerabilities, IT security findings, trend analysis, systemic control weaknesses, cybersecurity governance, vulnerability management, security audits, enterprise security.
Managing Domestic Debt and Sovereign Foreign Reserves: Risk, Liquidity, and Strategic Allocation
This paper analyzes the co-management of domestic debt and sovereign foreign reserves based on a risk-liquidity model which highlights the dependence between the two aspects as opposed to their independent maximization. It has a structural trade-off in which accumulation of reserves improves the buffers of liquidity-at the cost of fiscal cost as it is financed by domestic borrowing and costs less efficient when compared to shock resiliency. It draws on an analysis of the contribution of allocation policies in generating refinancing risk, market exposures, and the financial stability of the entire, based on the current literature in the same area, on its adequacy of reserves, and liquidity risk. The results reveal that liquidity is the binding constraint to connect assets and liability decisions and that its impacts are conveyed via debt structure, market, and institutional capacity. It is shown that the strategies that are not interacting (not combined) in the reserves and debt case give incomplete results as it is their combination with each other that gives the effective risk profile of the sovereign balance sheet. In this paper, the conclusion has been made that financial stability depends on coordination of asset-liability management whereby reserve buffers, and debt structures are balanced under an integrated risk framework model.
Domestic debt; foreign reserves; liquidity risk; sovereign risk; asset–liability management; reserve adequacy; debt sustainability; strategic allocation.
An Intelligent Cloud-Native Framework for Continuous Enterprise Risk Prediction and Automated Governance
Modern enterprises operate in highly dynamic, distributed environments where traditional, periodic risk assessment models fail to address rapid threat evolution. This paper proposes an Intelligent Cloud-Native Framework for Continuous Enterprise Risk Prediction and Automated Governance. The framework leverages distributed stream processing, machine learning (ML), and infrastructure-as-code (IaC) mutations to detect, predict, and remediate compliance and security risks in real time. By integrating graph-based anomaly detection with predictive risk scoring, the system forecasts potential governance failures before they impact the organization. The architecture utilizes a microservices-driven approach, employing automated policy-as-code engines to trigger real-time, self-healing governance workflows. Evaluation results demonstrate that the framework achieves a 94% reduction in risk mitigation latency and improves predictive accuracy by 38% compared to legacy batch-processing governance models. Ultimately, this research provides a scalable, resilient blueprint for modern organizations aiming to transition from reactive compliance postures to proactive, autonomous risk orchestration in cloud-native ecosystems.
Cloud-Native Governance, Continuous Risk Prediction, Machine Learning, Policy-as-Code, Automated Remediation, Enterprise Risk Management.
Unified Transformation Architecture for Artificial Intelligence SAP Integration Cloud-Native Computing Enterprise Secure Operations
The rapid evolution of digital technologies has transformed enterprise information systems by integrating Artificial Intelligence (AI), SAP-based enterprise resource planning, cloud-native computing, and enterprise secure operations into a unified digital ecosystem. Organizations increasingly require intelligent, scalable, and secure architectures capable of supporting business innovation while ensuring operational resilience and regulatory compliance. A Unified Transformation Architecture provides a strategic framework that combines AI-driven analytics, SAP integration platforms, cloud-native application development, and zero-trust security principles to create agile and intelligent enterprises. This architecture enables seamless interoperability among heterogeneous systems, automates business processes through machine learning, supports real-time decision-making, and enhances cybersecurity through continuous monitoring and adaptive threat detection. Cloud-native technologies such as containers, microservices, orchestration platforms, and DevSecOps practices further improve scalability, deployment flexibility, and service availability. The convergence of these technologies facilitates digital transformation by reducing operational complexity, improving resource utilization, and accelerating innovation across industries. This study examines the conceptual foundations, technological components, integration mechanisms, security considerations, and implementation methodologies associated with Unified Transformation Architecture. The research emphasizes the importance of standardized integration frameworks, intelligent automation, resilient infrastructure, and governance models that collectively enhance enterprise performance. The proposed methodology demonstrates how organizations can successfully implement secure, AI-enabled, cloud-native SAP ecosystems capable of addressing modern business challenges while maintaining operational excellence and long-term sustainability.
Artificial Intelligence, SAP Integration, Cloud-Native Computing, Enterprise Security, Digital Transformation, Microservices, DevSecOps, Zero Trust Architecture, Machine Learning, Kubernetes, Enterprise Architecture, Intelligent Automation, Cloud Computing, Business Process Integration, Cybersecurity
Intelligent Enterprise Technologies for Cloud Native Computing Autonomous Operations Data Analytics and Cybersecurity
Intelligent enterprise technologies have become fundamental to modern organizations seeking to improve operational efficiency, strengthen cybersecurity, and accelerate digital transformation through cloud-native computing, autonomous operations, and advanced data analytics. The convergence of artificial intelligence (AI), machine learning, cloud-native architectures, and intelligent automation enables enterprises to build scalable, resilient, and secure digital ecosystems capable of adapting to dynamic business environments. Cloud-native computing provides flexible infrastructure through microservices, containerization, orchestration platforms, and continuous integration and deployment, while autonomous operations reduce manual intervention by enabling intelligent monitoring, predictive maintenance, and automated decision-making. Advanced data analytics transforms vast volumes of structured and unstructured organizational data into actionable insights that support strategic planning, operational optimization, and personalized customer experiences. Cybersecurity remains a critical component of intelligent enterprise technologies by protecting digital assets, ensuring regulatory compliance, and mitigating increasingly sophisticated cyber threats through AI-powered detection and automated response mechanisms. Despite numerous advantages, organizations face challenges related to legacy system integration, workforce skill development, ethical AI implementation, data governance, and evolving cybersecurity risks. This study investigates the role of intelligent enterprise technologies in supporting cloud-native computing, autonomous operations, data analytics, and cybersecurity using a qualitative research methodology based on secondary data analysis. The findings demonstrate that strategic governance, technological innovation, organizational readiness, and continuous learning are essential for achieving resilient, secure, and sustainable enterprise transformation.
Artificial Intelligence, Intelligent Enterprise Technologies, Cloud-Native Computing, Autonomous Operations, Data Analytics, Cybersecurity, Machine Learning, DevSecOps, Kubernetes, Microservices, Predictive Analytics, Digital Transformation, Cloud Security, Enterprise Architecture, Business Intelligence
Enhancing Organizational Cyber Resilience Through the NIST Cybersecurity Framework
Today’s cyber threats are greater than ever before, posing new threats to operational continuity, data security, and stakeholder trust for organizations. In today’s fast-paced digital transformation, cyber resilience has emerged as a strategic priority and is not just about cyber security, but also about governance, risk management, incident response and business recovery. The study compares and contrasts the NIST Cybersecurity Framework with other frameworks and models that offer a structured and risk-based approach to identifying, protecting, detecting, responding, and recovering from cybersecurity incidents and will explore its benefits to improving the cyber resilience of organizations. The paper summarizes the key pillars of the framework and discusses how they relate to organizational governance, continuity planning, security culture and continuous improvement. It also examines key issues that can affect the effective implementation of these, such as leadership support, employee awareness, third-party risk management, and the use of new technologies like AI to detect and respond to threats. The study identifies best practices that help organizations sustain operations and comply with regulations without compromising their cyber-resilience. It concludes that the NIST Cybersecurity Framework provides an expansive cybersecurity baseline for developing adaptive, resilient, and sustainable cybersecurity programs that can help reduce the impact of disruptions and meet long-term organizational goals. The results offer valuable practical guidance for organizational leaders, cybersecurity personnel, policy-makers and researchers on how to better improve cyber-resilience in the digital era that is growing more complex.
Organizational cyber resilience, NIST Cybersecurity Framework, cybersecurity governance, cyber risk management, business continuity, incident response, cybersecurity resilience, digital transformation, threat detection, information security
Artificial Intelligence and SAP for Intelligent Wastewater Treatment Plant Management
Wastewater treatment plants are complex, continuously operating facilities whose performance directly affects public and environmental health, requiring careful coordination of biological treatment processes, mechanical equipment, chemical dosing, energy consumption, and regulatory compliance reporting. Many plants generate substantial volumes of process, sensor, and maintenance data, yet this data frequently remains fragmented across supervisory control and data acquisition (SCADA) systems, laboratory information management systems (LIMS), and enterprise resource planning (ERP) platforms that were not designed to interoperate closely, limiting the extent to which plant operators can act on predictive insight in a timely, coordinated manner. This paper examines how artificial intelligence (AI) and machine learning (ML) can be integrated with SAP enterprise systems, particularly SAP S/4HANA, SAP Enterprise Asset Management (EAM), and SAP Environment, Health, and Safety (EHS) management, to support intelligent wastewater treatment plant operations. We propose an integration architecture in which AI-driven process anomaly detection, equipment failure prediction, and effluent quality forecasting outputs are translated into structured SAP maintenance notifications, work orders, and compliance records, connecting plant-level predictive intelligence to the enterprise systems that manage plant maintenance, procurement, and regulatory reporting. The paper discusses relevant AI/ML techniques for treatment process optimization and equipment health monitoring, SAP-specific integration mechanisms, and the public health and regulatory compliance considerations that make explainability and data governance particularly important in this domain. We conclude with a discussion of implementation challenges and directions for future research on integrated plant management systems.
Wastewater treatment; artificial intelligence; machine learning; SAP; enterprise asset management; SCADA integration; predictive maintenance; process optimization; public health; regulatory compliance.
Multimodal AI for Pilot Skill Assessment Using Physiological Signals
Yet traditional methods of evaluating pilot skill, focused mainly on subjective instructor ratings or post hoc evaluation of aviation performance, only measure the observable products of cognitive functioning of skilled flying, and flight-related cognitive functioning is vitally important to modern aviation. This paper aims at developing four new frameworks: the MultiModal Aviator Performance and State Assessment AI Framework (MAPS-AI), that combines four physiological signals modalities (EEG, fNIRS, ECG and eye-tracking) and deep learning architectures for the purpose of real-time and objective monitoring of cognitive state in-flight while performing cognitive tasks during both flight training and flight operations. This framework brings together multi-sensor fusion and AI classification pipelines, limits of which are explored in the psychophysiological literature on pilot mental workload, the cognitive fatigue literature and EEG monitoring in actual flight conditions, fNIRS-based engagement detection in landing scenarios, multimodal workload assessment, and recent developments in compact EEG deep learning. A six-state cognitive state taxonomy is proposed and evidence is compiled demonstrating that incorporating progressive multimodal fusion yields a classification accuracy of around 83% compared a multimodal (EEG+EEG+EEG+EEG) approach with a accuracy of around 65% (EEG-only). Discussions of practical implications for aviation training, support systems for instructors, and human-machine interface are presented. The framework offers a conceptually sound basis for building an operationally viable assessment tools for pilots based on AI.
multimodal AI; pilot skill assessment; EEG; fNIRS; physiological signals; deep learning; mental workload
Machine Learning in Mental Healthcare: Improving Diagnosis and Treatment of Anxiety and Depression
Mental health disorders, particularly anxiety and depression, constitute a global public health crisis affecting over 700 million individuals worldwide. Traditional diagnostic methodologies rely heavily on subjective self-reporting and clinician observation, leading to significant delays in accurate diagnosis and treatment initiation. This paper provides a comprehensive review of the application of machine learning (ML) techniques in improving the diagnosis and treatment outcomes for anxiety and depression. We examine supervised learning models including Support Vector Machines (SVM), Random Forests, and deep neural networks applied to clinical datasets, electronic health records, and neuroimaging data. Additionally, we explore Natural Language Processing (NLP) approaches for sentiment analysis in social media and therapy transcripts, and the integration of wearable biosensor data with ML pipelines. Our analysis demonstrates that ML models consistently achieve diagnostic accuracy rates of 75–92% across varied datasets, outperforming traditional screening instruments. We also discuss current ethical challenges, data privacy concerns, explainability of AI models, and propose a framework for responsible clinical integration. The findings suggest that ML-driven tools, when deployed ethically, hold substantial promise for augmenting clinical decision-making, personalizing treatment pathways, and extending mental healthcare access to underserved populations.
Deep Learning, Natural Language Processing, Clinical Decision Support, Explainable AI
Intelligent Cloud Framework for Enterprise Reference Data Governance and Machine Learning-Based Transaction Risk Management
Enterprise reference data governance and transaction risk management have become critical components of modern digital platforms due to the increasing volume, velocity, and complexity of organizational data. Traditional governance approaches often struggle to maintain data quality, consistency, and compliance while effectively identifying fraudulent or high-risk transactions in real time. Machine learning (ML) offers innovative solutions by enabling automated data validation, anomaly detection, predictive analytics, and intelligent decision-making. This study examines the application of machine learning techniques in enterprise reference data governance and transaction risk management across digital platforms. The proposed framework integrates supervised and unsupervised learning algorithms to improve data accuracy, eliminate duplication, classify reference entities, and detect suspicious transaction patterns. By leveraging advanced analytics, organizations can enhance regulatory compliance, operational efficiency, and risk mitigation capabilities. The study also explores the role of ML-driven governance models in supporting data stewardship, master data management, and continuous monitoring processes. Furthermore, the research highlights the benefits and challenges associated with implementing machine learning solutions, including data privacy concerns, algorithmic bias, and integration complexity. The findings suggest that machine learning significantly improves governance effectiveness and transaction security while enabling organizations to adapt to evolving digital ecosystems. The proposed framework provides valuable insights for enterprises seeking to strengthen data governance practices and minimize transaction-related risks in a competitive digital environment.
Machine Learning, Enterprise Data Governance, Reference Data Management, Transaction Risk Management, Digital Platforms, Artificial Intelligence, Data Quality, Fraud Detection, Predictive Analytics, Data Compliance, Anomaly Detection, Risk Assessment, Data Stewardship, Master Data Management, Digital Transformation