International Journal of Science, Research and Technology
The International Journal of Science, Research and Technology (IJSRAT) is a double-blind, peer-reviewed, open-access scholarly journal dedicated to publishing high-quality research in computer science and emerging technologies. The journal provides a rigorous academic platform for innovative theoretical and applied research that advances knowledge and drives technological development in rapidly evolving fields. IJSRAT is overseen by an international editorial board of recognized subject-matter experts who uphold strict academic and ethical standards. Every manuscript undergoes a transparent double-blind peer-review process, ensuring that articles are assessed solely on their originality, methodological rigor, scholarly contribution, and overall research integrity. Publishing with IJSRAT offers authors a reliable and efficient process for disseminating research to a global academic and professional audience. The journal is available in both online and print formats, ensuring wide accessibility, broad visibility, and lasting impact within the scientific and technological communities worldwide. Journal Focus and Scope The International Journal of Science, Research and Technology (IJSRAT) is a leading double-blind, peer-reviewed, open-access scholarly journal dedicated to publishing original research in computer science, information technology, and emerging technological fields. The journal provides a rigorous academic platform for researchers, practitioners, and scholars to disseminate innovative work that advances both theoretical understanding and practical applications in rapidly evolving technological domains. IJSRAT is guided by an international editorial and advisory board comprising subject-matter experts recognized for their contributions to their respective fields. Each submission undergoes a stringent double-blind peer-review process that evaluates manuscripts for originality, methodological rigor, technical soundness, and relevance. This process ensures that only high-quality research that meets global academic standards is published. IJSRAT publishes research across a wide spectrum of topics in science, engineering, and technology, including, but not limited to, computer science, information technology, artificial intelligence, data science, cybersecurity, cloud computing, software engineering, robotics, electronics and communication, mechanical engineering, electrical engineering, biotechnology, nanotechnology, materials science, environmental engineering, and other emerging technological fields. IJSRAT provides unrestricted open-access to its content, enabling global dissemination and accessibility to academic and professional audiences. The journal emphasizes research that drives practical applications, fosters interdisciplinary collaboration, and informs the development of next-generation technologies. Through its commitment to scholarly excellence and innovation, IJSRAT serves as a respected international platform for advancing knowledge, shaping industry practices, and influencing the broader scientific and technological landscape. Publication Frequency The International Journal of Science, Research and Technology (IJSRAT) is a bimonthly, peer-reviewed, open-access journal. It publishes high-quality research in computer science and emerging technologies, providing a consistent platform for timely dissemination of innovative theoretical and applied studies. Its monthly publication schedule ensures rapid visibility and on-going engagement with the global academic and professional community. Types of Contributions The International Journal of Science, Research and Technology (IJSRAT) accepts a variety of scholarly contributions, including: 1. Original Research Articles 2. Review Articles 3. Book Reviews 4. Discussion and Commentary Papers 5. Translated Articles (subject to editorial approval) Open Access Policy The International Journal of Science, Research and Technology (IJSRAT) is an open-access journal, ensuring that all published articles are immediately and permanently free to read, download, copy, and share. This model promotes unrestricted dissemination of high-quality research to a global academic and professional audience. IJSRAT is published under the Creative Commons CC BY 4.0 license, which clearly defines how third parties may reuse its content, allowing lawful redistribution, adaptation, and citation, provided proper attribution is given to the original authors. This approach maximizes the visibility, accessibility, and impact of the research, ensuring that scholarly work reaches the widest possible audience without any subscription or access barriers. Conflict of Interest Conflicts of interest may arise when professional judgments regarding a primary research objective could be influenced by secondary interests, such as financial or personal gain. Even the perception of a conflict is considered significant and is treated with the same level of scrutiny as actual conflicts. Authors are expected to avoid any agreements with study sponsors—whether for-profit or non-profit—that could restrict their access to research data, limit their ability to independently analyze or interpret results, or compromise their authority to publish findings according to their own professional judgment. A formal declaration of interest from all authors is required prior to the review and acceptance of any manuscript, ensuring transparency, ethical integrity, and the credibility of the published research. Archiving The International Journal of Science, Research and Technology (IJSRAT) employs the LOCKSS (Lots of Copies Keep Stuff Safe) system to establish a distributed archiving network among participating libraries, enabling the creation of permanent archives for preservation and restoration purposes. The journal fully supports and encourages author self-archiving of all content, also known as “green” open access. All journals are registered with SHERPA/RoMEO, ensuring that license terms and self-archiving policies are clearly defined and transparent. IJSRAT is indexed by Google Scholar and is available for harvesting via OAI-PMH, facilitating broad discoverability and accessibility of published research. Authors and readers can request additional indexing by contacting the editorial office at editor@ijsrat.com or by submitting an indexing request directly to the desired service. Plagiarism Screening The International Journal of Science, Research and Technology (IJSRAT) upholds the highest standards of academic integrity and originality. All submitted manuscripts undergo a comprehensive plagiarism screening using advanced detection tools to identify similarities with existing literature. Manuscripts exhibiting significant overlap are further evaluated by the editorial board to ensure the integrity of the work. Authors are required to submit original research and provide proper attribution for all sources. Any manuscript found to contain plagiarism, duplicate publication, or improper citation is subject to rejection or returned for revision. This rigorous process ensures that all published research meets international ethical standards, maintains scholarly credibility, and preserves the journal’s reputation as a trusted platform for high-impact, cutting-edge research. Advertisement Policy IJSRAT does not host advertisements on its website. This ensures an uncluttered, professional environment focused solely on scholarly content. Readers and authors can access the journal without commercial distractions, maintaining its integrity and academic credibility. Publisher International Academy of Digital Innovation, Education & Research (IADIER) 1602, Lensfield Road, Cambridge CB2 1EG, United Kingdom Email: editor@ijsrat.com Mobile No: +44 7440 417167
AI Enabled Enterprise Infrastructure Modernization through Data Center Migration Cloud Integration and Operational Resilience
Enterprise infrastructure modernization has become a strategic priority for organizations seeking to improve agility, scalability, operational efficiency, and resilience in an increasingly digital economy. The integration of Artificial Intelligence (AI) into modernization initiatives has significantly transformed traditional approaches to data center migration, cloud adoption, and operational management. AI-enabled systems facilitate intelligent workload assessment, predictive analytics, automated resource allocation, and proactive risk management, enabling organizations to execute complex infrastructure transformations with greater accuracy and reduced downtime. Data center migration serves as a critical component of modernization, allowing enterprises to transition from legacy environments to flexible cloud-based architectures. Cloud integration further enhances organizational capabilities by providing scalable computing resources, advanced analytics, and improved collaboration across distributed environments. Operational resilience has emerged as an essential outcome of modernization efforts, ensuring business continuity, cybersecurity preparedness, disaster recovery effectiveness, and adaptive response to disruptions. This study explores the role of AI in enabling enterprise infrastructure modernization through data center migration, cloud integration, and operational resilience. The discussion synthesizes existing academic and industry perspectives, examines implementation methodologies, and evaluates strategic considerations for successful transformation. The findings indicate that AI-driven modernization frameworks improve infrastructure performance, optimize resource utilization, strengthen security postures, and support sustainable digital transformation, thereby creating long-term competitive advantages for enterprises operating in dynamic technological environments
Next-Generation Enterprise Intelligence Using Generative AI Agentic Systems Cloud Computing and Autonomous Cybersecurity
The rapid evolution of digital technologies has transformed the way organizations generate, process, and utilize information for strategic decision-making. Next-generation enterprise intelligence represents a comprehensive framework that integrates generative artificial intelligence, agentic systems, cloud computing, and autonomous cybersecurity to create adaptive, intelligent, and resilient business environments. Generative AI enhances organizational capabilities by producing insights, automating content creation, supporting decision-making, and facilitating human-machine collaboration. Agentic systems extend these capabilities by enabling autonomous reasoning, planning, and execution of complex tasks with minimal human intervention. Cloud computing provides scalable infrastructure, computational power, and data accessibility necessary for deploying intelligent enterprise solutions across distributed environments. Autonomous cybersecurity strengthens organizational resilience by leveraging artificial intelligence to detect, predict, and respond to cyber threats in real time. Together, these technologies form an interconnected ecosystem capable of transforming enterprise intelligence from a reactive information management function into a proactive strategic capability. This study examines the conceptual foundations, technological integration, benefits, challenges, and implementation considerations associated with next-generation enterprise intelligence. The research highlights how organizations can leverage these emerging technologies to improve operational efficiency, innovation, security, and competitive advantage. The findings suggest that enterprises adopting integrated intelligent ecosystems are better positioned to navigate increasing complexity, uncertainty, and digital transformation demands in contemporary business environments.
Intelligent Enterprise Transformation through AI Driven Cloud Native Systems and Predictive Analytics
The rapid evolution of digital technologies has fundamentally transformed the way enterprises operate, compete, and create value. Among the most influential innovations, artificial intelligence (AI), cloud-native systems, and predictive analytics have emerged as critical enablers of intelligent enterprise transformation. Organizations across industries are increasingly adopting these technologies to improve operational efficiency, enhance decision-making, optimize resource utilization, and deliver personalized customer experiences. AI-driven cloud-native architectures provide scalable, flexible, and resilient platforms that support real-time data processing, automation, and continuous innovation. Simultaneously, predictive analytics leverages historical and real-time data to forecast trends, identify risks, and generate actionable insights that strengthen strategic planning and business agility. The integration of these technologies enables enterprises to transition from traditional reactive models to proactive and data-driven operating environments. This transformation facilitates improved collaboration, accelerated product development, enhanced cybersecurity, and sustainable competitive advantages. However, organizations also face challenges related to data governance, security, regulatory compliance, workforce readiness, and technology integration. This study examines the role of AI-driven cloud-native systems and predictive analytics in enterprise transformation, reviews existing literature, and proposes a comprehensive research methodology for evaluating their impact on organizational performance. The findings contribute to understanding how intelligent technologies reshape modern enterprises and support long-term digital transformation objectives
Autonomous Decision Intelligence for Cloud-Native Systems through Predictive Analytics and Cyber Risk Orchestration
The rapid adoption of cloud-native technologies has transformed enterprise computing by enabling scalability, resilience, agility, and continuous innovation. Modern organizations increasingly rely on distributed architectures, microservices, containers, serverless computing, and multi-cloud environments to support digital transformation initiatives. However, the complexity and dynamic nature of cloud-native ecosystems have introduced significant challenges in decision-making, cybersecurity management, operational governance, and risk mitigation. Traditional approaches to monitoring and risk management are often inadequate for handling the volume, velocity, and variety of data generated within cloud-native environments. Autonomous Decision Intelligence (ADI) has emerged as a transformative paradigm that combines artificial intelligence, machine learning, predictive analytics, and automation to support intelligent, real-time decision-making. Simultaneously, cyber risk orchestration provides coordinated mechanisms for identifying, assessing, prioritizing, and mitigating cybersecurity threats across interconnected systems. This study explores the integration of Autonomous Decision Intelligence, Predictive Analytics, and Cyber Risk Orchestration as a unified framework for next-generation cloud-native systems. The proposed approach leverages continuous data analysis, automated risk evaluation, adaptive decision-making, and orchestrated security responses to enhance operational resilience and governance effectiveness. By integrating intelligent analytics with cyber risk management processes, organizations can improve situational awareness, optimize resource allocation, strengthen security postures, and support proactive decision-making. The study contributes to digital enterprise governance literature by presenting a comprehensive conceptual framework for achieving intelligent autonomy, cybersecurity resilience, and sustainable cloud-native transformation in increasingly complex technological environments.
Intelligent Enterprise Retail Infrastructure using AI Driven Cybersecurity and AWS CloudWatch Alert Automation
Modern enterprise retail systems are increasingly dependent on cloud-native infrastructures, distributed microservices, and real-time data pipelines. This transformation has expanded the attack surface, making cybersecurity a critical concern. At the same time, operational efficiency requires continuous monitoring, automated alerting, and intelligent incident response mechanisms. This paper explores an intelligent enterprise retail infrastructure that integrates AI-driven cybersecurity with AWS CloudWatch-based alert automation to enhance resilience, scalability, and threat detection accuracy . The proposed system leverages machine learning models for anomaly detection, behavioral analytics for fraud prevention, and predictive security intelligence to identify potential threats before they escalate. AWS CloudWatch serves as the centralized observability layer, collecting logs, metrics, and events from distributed retail services such as payment gateways, inventory systems, and customer applications. Automated alerting workflows trigger AWS Lambda functions for incident response, reducing mean time to detection (MTTD) and mean time to recovery (MTTR). The integration of AI with cloud monitoring enables proactive defense mechanisms, adaptive thresholding, and intelligent noise reduction in alerts. This hybrid approach ensures that enterprise retail infrastructures remain secure, highly available, and operationally efficient while minimizing human intervention in cybersecurity operations.
Transformative SAP-Based Intelligent Systems for Cloud Analytics Enterprise Security and AI Applications
The rapid evolution of enterprise digital ecosystems has positioned SAP-based intelligent systems as a foundational element for cloud analytics, enterprise security, and artificial intelligence (AI) integration. Organizations are increasingly adopting SAP platforms enhanced with AI capabilities to achieve real-time decision-making, predictive analytics, automated workflows, and robust cybersecurity frameworks. This study explores the transformative role of SAP-based intelligent systems in enabling secure, scalable, and data-driven enterprise environments. It focuses on how SAP technologies such as SAP S/4HANA, SAP Business Technology Platform (SAP BTP), and SAP Analytics Cloud integrate with AI techniques including machine learning, natural language processing, and deep learning to enhance enterprise performance. The research also examines the role of cloud computing in supporting distributed data processing, intelligent automation, and enterprise-wide analytics capabilities. Furthermore, the study highlights how AI-driven SAP systems strengthen enterprise security through anomaly detection, risk prediction, and automated incident response. A qualitative research methodology based on secondary data analysis and thematic interpretation is adopted. Findings indicate that SAP-based intelligent systems significantly improve operational efficiency, cybersecurity resilience, and data intelligence. However, challenges such as integration complexity, skill shortages, and data governance issues remain critical barriers. The study concludes that SAP-AI convergence represents a key driver of next-generation intelligent enterprise transformation
AI-Driven CI/CD Pipeline Automation for Secure .NET Applications in Azure Kubernetes Services
The increasing pace of the evolution of cloud-native systems and microservices in business propositions has increased the intricacy of deployment of secure to both business and IT space.NET applications. The proposed pipeline in the experiment suggests the Automation system needed to enhance speed in deployments, security and reliability with several unique focus behaviors by deploying the Azure Kubernetes Services (AKS) using AI. The proposed architecture with the aid of containers offers machine learning adaptable deployment schemes, automatic verification of the vulnerability scanners and code verification code analysis. With the help of AI, the pipeline will be self-driving and know where the particular failures are likely to occur within the pipeline, optimize resource usage, and follow the best practices in the security development lifecycle. The architecture is made up of the modular steps of monitor code commit, automated test, work on container images, security check and coordinated launch in AKS clusters. Each step requires an AI-infused decision-making procedure to a point that it does not require human interventions, errors in requirements and automatically identifies security risks. A POC implementation will demonstrate tremendous time saving during implementation, error reduction, security threat, contrasting to traditional CI/CD. Through performance measures, performance has been achieved by 35-percent deployment efficiency and post-deployment security incidents have been achieved by 40-percent. The experiment proves the applicability of AI to CI/CD pipelines as the way to streamline the delivery of software and improve the security posture of cloud-based.NET applications. The research findings do hold some valuable findings to organizations when it comes to the endeavor of implementing intelligent automation with an intention of provision of cloud-native, secure, scalable and resilient applications.
IT/OT Convergence: A Zero Trust Reference Architecture for the Energy Sector
The barrier separating IT from OT has narrowed over the past 10+ years. The rate at which it is happened recently has increased . It is no longer enough for utilities and pipelines / refiners to digitally transmit log data, and they should expect cloud backed analytics, to let engineers adjust parameters using remote control rooms and virtual digital twins of physical assets, which allow them to manage facilities across vast geographies. All that information, the ability to use digital technologies, provides tangible business value to these operations, which results in correspondingly wider exposure of the systems running them. Today's paper examines IT/OT convergence as a business requirement that represents an ongoing security challenge. It discusses and proposes an adaptable zero trust reference architecture comprising five layers-identity, device, network, application and data-that energy sector environments may deploy within existing frameworks to better mitigate cybersecurity threats. We rely on case examples drawn from actual cyberattacks and disclosures over 2020 through mid 2022 to inform this architecture, which also reflects recommendations made by organizations such as NIST, CISA and IEC. Than offering a vendor neutral checklist or prescriptive list of recommendations, the goal of this discussion is to provide a strategic framework within which architects can make decisions about OT/IT convergence as it relates to operational security
Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration
The rapid proliferation of artificial intelligence capabilities within Integration Platform as a Service (iPaaS) solutions has fundamentally altered the enterprise integration landscape, enabling intelligent data routing, anomaly detection, predictive transformation, and automated pipeline optimization. However, this convergence of AI and integration introduces significant governance and observability challenges that existing MLOps practices and iPaaS operational models address only in isolation, leaving a critical gap in unified oversight. This paper presents the MLOps-iPaaS Governance Framework (MIPGF), a unified framework bridging MLOps model lifecycle management with iPaaS operational governance through four pillars: model governance and versioning, real-time observability of AI-driven decision points, compliance and auditability, and drift detection. We analyze governance gaps in three leading iPaaS platforms — MuleSoft Anypoint, Azure Integration Services, and Boomi — and evaluate the framework against enterprise integration scenarios in healthcare, financial services, and retail. Results demonstrate measurable improvements in anomaly detection, compliance posture, and incident resolution time, providing a practical governance blueprint for enterprises adopting AI-augmented integration
AI Driven Cloud Native Enterprise Reliability Framework for Predictive Analytics and Intelligent DevOps Automation
Cloud-native enterprise systems have transformed modern digital infrastructures by enabling scalability, flexibility, and rapid deployment across distributed environments. However, the increasing complexity of microservices, container orchestration, and hybrid cloud ecosystems has introduced significant reliability and operational challenges. This research proposes an AI-driven cloud-native enterprise reliability framework that integrates predictive analytics with intelligent DevOps automation to improve system resilience, operational efficiency, and proactive incident management. The framework leverages machine learning algorithms, anomaly detection models, and real-time telemetry data to predict failures before they occur and automate corrective actions through intelligent orchestration mechanisms. By combining artificial intelligence with DevOps practices such as continuous integration, continuous delivery, infrastructure as code, and automated monitoring, organizations can reduce downtime, enhance service availability, and optimize resource utilization. The proposed framework also incorporates Kubernetes-based orchestration, observability platforms, and reinforcement learning techniques to support adaptive decision-making in dynamic cloud environments. Furthermore, the study evaluates the effectiveness of predictive maintenance and automated remediation in minimizing operational risks and improving enterprise reliability. The framework demonstrates how AI-powered DevOps ecosystems can support self-healing infrastructure, faster incident response, and continuous service optimization. This research contributes to the advancement of autonomous cloud operations and intelligent enterprise infrastructure management in modern digital transformation initiatives.
Hybrid AI, Cloud, and Cybersecurity Frameworks for Autonomous Enterprise Data Intelligence and Scalable Governance
The rapid advancement of digital transformation has significantly increased the volume, complexity, and strategic importance of enterprise data. Organizations across industries are increasingly adopting Hybrid Artificial Intelligence (AI), cloud computing, and cybersecurity frameworks to support intelligent automation, scalable governance, and secure data operations. Hybrid AI combines machine learning, deep learning, and rule-based systems to enhance enterprise decision-making, predictive analytics, and operational efficiency. Cloud computing offers scalable infrastructure, real-time data accessibility, and cost-effective resource management, enabling enterprises to process and manage large-scale data environments. Simultaneously, cybersecurity frameworks provide mechanisms for protecting sensitive information, ensuring regulatory compliance, and mitigating evolving cyber threats. This study examines the integration of Hybrid AI, cloud technologies, and cybersecurity frameworks in developing autonomous enterprise data intelligence systems. The research highlights the importance of secure cloud ecosystems, intelligent governance models, and adaptive cybersecurity strategies for sustainable digital transformation. Furthermore, the study explores implementation challenges, governance complexities, and ethical considerations associated with AI-driven enterprise systems. The proposed framework supports scalable governance, automated decision-making, and cyber resilience, contributing to the development of secure and intelligent enterprise ecosystems capable of supporting future digital economies and data-centric organizational environments
Optimizing SAP Workloads on Cloud-Native Platforms: A Framework for Intelligent Resource Allocation and Performance Scaling
For the better part of two decades, enterprise capacity management has relied on massive, static over-provisioning to guarantee database stability a legacy mindset that fundamentally clashes with the modern industry infatuation with ephemeral, cloud-native platforms. Contemporary orchestration methodologies attempt to resolve this friction through generic machine learning models that trigger hypervisor-level metrics minutes after a bottleneck has materialized; does wrapping a monolithic database in a container genuinely constitute an architectural advancement? To bridge this foundational disconnect, this research introduces a predictive framework that directly subordinates Kubernetes-driven infrastructure scaling to SAP HANA’s internal memory governance via a Long Short-Term Memory (LSTM) network. By ingesting proprietary internal telemetry to pre-emptively warm memory pools ahead of cyclical analytical swells within an OpenStack-based service cloud, the model effectively neutralizes high-dependency transactional latency and nearly doubles CPU utilization efficiency from 22.36% to 43.02%. While the predictive horizon predictably collapses during unprecedented anomalies exposing the limitations of idealized orchestration algorithms that treat hardware physics as academic toys the framework successfully maintains strict transactional continuity under genuine enterprise duress. Ultimately, this synthesis proves that true elasticity requires abandoning localized infrastructure optimization, establishing a necessary trajectory for future multi-cloud federations to synchronize dynamic provisioning APIs directly with proprietary database constraints.
Secure Generative AI and Deep Learning Models for Enterprise Automation Cloud Reliability Cybersecurity and Intelligent Operational Analytics
Generative Artificial Intelligence (AI) and deep learning technologies are revolutionizing enterprise automation, cloud reliability management, cybersecurity defense, and intelligent operational analytics. Modern organizations increasingly depend on AI-driven systems to automate workflows, improve operational efficiency, and strengthen security infrastructures. However, the adoption of generative AI introduces significant concerns related to data privacy, adversarial attacks, model manipulation, ethical issues, and operational reliability. This research explores secure generative AI and deep learning models designed to enhance enterprise automation while maintaining cloud reliability, cybersecurity resilience, and intelligent analytics capabilities. The study examines secure AI architectures, federated learning techniques, explainable AI frameworks, and zero-trust security mechanisms integrated within enterprise ecosystems. Additionally, the research investigates the role of AI-powered predictive analytics, anomaly detection systems, and automated cyber defense mechanisms in modern cloud environments. A mixed-methodology approach combining qualitative and quantitative techniques is used to evaluate model performance, scalability, security robustness, and operational efficiency. The findings indicate that secure generative AI systems significantly improve decision-making, automate enterprise processes, enhance cloud service reliability, and strengthen cybersecurity operations. The research contributes toward building secure, scalable, and trustworthy AI-driven enterprise infrastructures capable of supporting intelligent operational analytics and sustainable digital transformation initiatives
Cloud-Native Shift Left Accessibility Engineering for Scalable Agile Product Development Ecosystems
In software development circles, the “cloud-native” end-to-end product development or agile software development lifecycle approaches have come into prominence, with making sure products are accessible and available from the very beginning of the SDLC process. This research paper proposes a Shift-Left Accessibility Engineering Framework for agile product development eco-systems to bring accessibility requirements, accessibility automated testing, inclusive design validation, developer enablement and accessibility monitoring all under one roof in the cloud-native DevOps pipelines to support the scalability of agile product development eco-systems. The table below shows that the framework correlates the accessibility checkpoints with backlog refinement, user story writing, design prototyping, coding, continuous integration/continuous deployment, quality assurance and post-release analytics. The study illustrates a combination of standards based accessibility criteria (WCAG), automated accessibility scanning, containerized test environment, design system governance, and cross functional working in an agile manner to reduce remediation costs, increase user inclusivity, and boost product quality at scale. The overall emphasis of the paper is accessibility engineering is also another responsibility which should be shared by the product owners, designers, the developers, the testers, DevOps and compliance stakeholders. It also focuses on the use of cloud-native architectural principles, microservices, reusable components and observability tools to ensure ‘continuous accessibility assurance’ for any product in an ecosystem of distributed products. With the proposed practice, it provides an effective model in which accessibility is also an intrinsic part of the agile process for development and deployment, but does not affect delivery velocity. To conclude, the study highlights the importance of accessibility accessibility left in transitioning away from a reactive approach to digital product development, solely focused on compliance, towards a proactive and inclusive approach; and to promote more sustainable practices.
Reinforcement Learning for Dynamic Cloud Resource BI Optimization
Dynamic cloud environments: These environments have a varying volume of data, query workload, number of users and/or business continuity objectives (SLOs) for Business Intelligence (BI) workloads. Due to these uncertainties, it will be impossible to balance the three important factors of performance, energy efficiency and cost with fixed rule and threshold based autoscaling approaches. In this research article we want to propose a framework for dynamically optimizing cloud resource BI based on Reinforcement Learning (RL) model who will continually interact with the cloud environment and learn a set of optimal policies for cloud resource allocation. This framework consists of five layers such as BI workload monitoring, state space construction, RL decision engine, resource orchestration and evaluation of performance feedback. Every system metric (such as CPU usage, memory usage, query response time, queue length, storage I/O, workload priority and cost constraints) is important. Those can be the scaling of virtual machines, moving containers around and changing memory sizes, prioritizing BI queries, moving workloads from node to node, etc., and are decided by the RL agent. The rewards can be specified as a multi-objective function, which include reduction of response time, reduction of cloud cost, resource utilization gain and SLA and energy consumption satisfaction. The proposed model is adaptive – it adapts itself based on the trends of the historical data or real-time data of BI workload. Algorithms like Q-learning, Deep Q-Networks and Proximal Policy Optimization could be potentially applied for an intelligent decision making in cloud based BI platforms, the study mentions. This framework can enhance the analytical responsiveness, minimise resources wastage and enable cost-aware business decision systems. It also helps businesses in planning for capacity with a pro-active assessment and hand over their real-time dashboards and predictive analytics services. Lastly, reinforcement learning can be leveraged for scaling and adapting for optimal BI workloads in a complex, dynamic and data rich cloud environment.
Intelligent Cloud Native AI Architectures for Secure Enterprise Data Management and Scalable Financial Systems
The rapid advancement of cloud computing, artificial intelligence, big data engineering, and intelligent financial technologies has transformed enterprise data management and digital financial ecosystems across global industries. Modern enterprises and financial institutions continuously generate massive volumes of structured and unstructured data from cloud platforms, transactional systems, IoT devices, digital banking services, enterprise applications, cybersecurity infrastructures, and customer interaction environments. Traditional centralized architectures often struggle to support scalability, intelligent analytics, operational resilience, cybersecurity protection, and real-time financial processing requirements in highly dynamic digital ecosystems. Intelligent cloud-native AI architectures have emerged as transformative solutions for secure enterprise data management and scalable financial systems by integrating distributed cloud infrastructure, machine learning optimization, cybersecurity intelligence, intelligent automation, and predictive analytics. This research presents a comprehensive framework for intelligent cloud-native AI architectures supporting secure enterprise operations and scalable financial infrastructures. The proposed framework incorporates microservices orchestration, distributed data engineering, AI-driven analytical models, blockchain-supported governance, intelligent cybersecurity mechanisms, and adaptive automation systems to improve scalability, operational intelligence, financial reliability, and data security. Experimental evaluation demonstrates improvements in predictive financial analytics, intelligent fraud detection, distributed scalability, operational efficiency, cybersecurity resilience, and cloud resource optimization. The findings indicate that cloud-native AI architectures provide secure, adaptive, intelligent, and scalable solutions for future enterprise and financial computing ecosystems.
Smart Cyber Intelligence and Machine Learning Models for Secure Cloud Native Enterprise Data Platforms
Smart cyber intelligence and machine learning models have become essential components of secure cloud-native enterprise data platforms in the era of digital transformation and intelligent computing. Modern enterprises increasingly depend on cloud-native infrastructures to support scalable analytics, automated operations, cybersecurity resilience, and real-time decision-making. This study explores the development and implementation of smart cyber intelligence frameworks integrated with machine learning models for securing cloud-native enterprise data platforms across financial, healthcare, industrial, and business environments. The research focuses on how artificial intelligence, cloud computing, predictive analytics, cybersecurity mechanisms, and automation technologies collectively improve enterprise security, operational efficiency, and intelligent governance. The study also examines the role of zero-trust security architectures, cloud-native microservices, real-time threat intelligence, anomaly detection systems, and intelligent orchestration frameworks in protecting enterprise digital ecosystems. A comprehensive literature review highlights recent advancements in cloud-native cybersecurity, AI-driven threat intelligence, machine learning analytics, and secure enterprise data engineering. The proposed methodology introduces a multi-layered intelligent cloud-native architecture integrating cyber intelligence, machine learning analytics, governance, automation, and adaptive security services within a unified enterprise framework. The findings indicate that smart cyber intelligence systems significantly improve threat detection, predictive security analytics, operational transparency, scalability, and business continuity. However, challenges related to data privacy, infrastructure complexity, ethical AI concerns, interoperability, and regulatory compliance continue to influence enterprise adoption and management strategies.
Enterprise Grade AI Driven DevOps Platforms for Resilient Cloud Infrastructure Automation
Enterprise-grade AI-driven DevOps platforms are transforming cloud infrastructure automation by integrating Artificial Intelligence, machine learning, predictive analytics, and intelligent orchestration into modern software development and operational workflows. As enterprises increasingly adopt cloud-native technologies, microservices, containerized applications, and distributed infrastructures, the demand for scalable, reliable, and automated DevOps solutions has significantly increased. Traditional DevOps practices often rely on manual monitoring, static automation scripts, and reactive operational management, which may not efficiently address the complexity and dynamic nature of modern cloud environments. AI-driven DevOps platforms overcome these limitations by enabling autonomous infrastructure management, predictive failure detection, intelligent resource optimization, and automated incident response. This study explores enterprise-grade AI-driven DevOps platforms designed for resilient cloud infrastructure automation. The research examines the integration of AI with continuous integration and continuous deployment (CI/CD) pipelines, cloud orchestration systems, observability platforms, infrastructure-as-code frameworks, and automated testing environments. It also analyzes how machine learning algorithms enhance system reliability, deployment efficiency, security monitoring, and operational scalability. Furthermore, the study investigates implementation challenges including infrastructure complexity, cybersecurity risks, computational overhead, and integration difficulties with legacy systems. The findings indicate that AI-driven DevOps platforms significantly improve operational efficiency, reduce downtime, optimize resource utilization, and strengthen cloud infrastructure resilience, making them essential for future intelligent enterprise software engineering and digital transformation initiatives.
Cybersecurity Integration Framework for Mergers and Acquisitions
The practice of merger and acquisition transactions predictably produces a unique period of cybersecurity exposure lasting the 45-90 days immediately following the transaction's close. Throughout this interval, the acquired business unit must operate on the basis of the cyber deficiencies inherited, inconsistencies within the privilege controls assigned to its users and insufficient access to or understanding of the cybersecurity processes employed by the acquirer. Despite considerable literature about cybersecurity in mergers, most treat this as either a due diligence activity solved during the pre-deal period or an effort to create full and consistent similarity over 12-24 months, which leaves little published research covering effective strategies for those 45-90 days The cybersecurity integration framework proposed here aims directly at that 45-90 day period. The framework includes parallel efforts along four lines of activity; directory and policy, endpoint management, privilege controls and visibility and compliance; bound by a reusable runbook template. The framework utilizes a four-phase gated decision model with well-documented criteria for both entering and exiting each phase, plus a hybrid pattern for merging user and resource identity management that addresses situations involving both on premises Active Directory and cloud identities, common today in acquired entities. The pattern proposed is a general-purpose and vendor-neutral concept intended to serve across multiple industries and the paper is intended to bridge between high-level concepts about merger integration and the step-by-step vendor guides to specific products that often are the sole reference documentation available to practitioners
Designing Self-Healing Cloud Architectures for Mission-Critical Distributed Systems
Distributed systems which are mission critical require high availability, resilience and minimum downtimes during failure, cyber threats, and dynamic workloads. Conventional fault-tolerant systems, though useful to a certain degree, can be based on manual intervention and recovery plans and are therefore less adaptable in the complicated cloud environment. This paper discusses how self-healing cloud architecture can be designed to use automation, smart monitoring and adaptive remediation methods to maintain the reliability of the system.The suggested architecture will include real-time anomaly detection, predictive analytics and automated recovery processes to detect, diagnose, and fix system failures automatically. Using technologies like microservices, container orchestration, and AI- driven observability, the system dynamically reacts to failures by taking measures such as auto-scaling, rerouting of services, and isolating faults. Proactive healing is also a focus area in the framework to anticipate possible disruptions and prevent them before they affect system performance. Experimental analysis indicates that there is better system uptime, shorter mean time to recovery (MTTR), and greater efficiency in operation as opposed to traditional methods. The study will help to create intelligent, scalable, and robust cloud architectures that can support mission-critical applications in areas like healthcare, finance, and smart cities.
Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence
Governance-aligned Generative AI (GenAI) systems enable enterprise-grade GenAI capabilities by embedding governance safeguards directly into GenAI pipelines. Such pipelines instantiating enterprise workflows mitigate GenAI risks in GenAI model choices, foundation datasets, user prompts, and output consumption. Decisions regarding these GenAI pipeline elements should be made with the same formal structures and rigor applied to enterprise decision-making. A wealth of governance-aligned GenAI pipelines applied to enterprise processes would deliver comprehensive enterprise workflow intelligence at a fraction of the cost of traditional GenAI implementations. For enterprises to harness Generative AI (GenAI) safely, enterprise governance objectives for GenAI must be realised. Candidates address the five enterprise corporate governance objectives—compliance, risk management, strategic alignment, performance and accountability—iteratively for all GenAI pathways; in computer science parlance, the task is to devise a breadth-first search through the enterprise GenAI pipeline graph. A foundational support system is a community-shared catalog of representative governance-aligned pipeline instances that address specific enterprise workflows or aspects thereof, deployed enterprise, domain or solution-wide with appropriate safeguards or controls. Candidate pipelines should instantiate enterprise workflows; the assets used in the pipeline or any GenAI input or output that may pose risk or compliance issues require particular attention. Enterprise workflows are risk scenarios against which GenAI adoption should be justified; pipeline adoption a risk-control measure.
Explainable and Trustworthy Artificial Intelligence Architectures for Enterprise Cybersecurity and Decision Intelligence
Artificial Intelligence (AI) has become a transformative technology in enterprise cybersecurity and decision intelligence by enabling automated threat detection, predictive analytics, intelligent decision-making, and adaptive security management. However, the increasing reliance on AI systems has raised significant concerns regarding transparency, accountability, trustworthiness, and explainability in enterprise environments. Traditional AI models often operate as black-box systems, making it difficult for organizations to understand, validate, and trust automated decisions. Explainable and trustworthy AI architectures aim to address these challenges by integrating transparency, interpretability, fairness, robustness, and ethical governance into intelligent enterprise systems. This study explores the role of explainable AI (XAI) architectures in enhancing cybersecurity operations and enterprise decision intelligence frameworks. The research examines how AI-driven systems support threat detection, anomaly analysis, access control, incident response, and strategic decision-making while maintaining accountability and regulatory compliance. Furthermore, the study investigates architectural components such as interpretable machine learning models, trust management systems, human-AI collaboration mechanisms, and governance frameworks designed to improve stakeholder confidence and operational reliability. The findings demonstrate that explainable and trustworthy AI architectures significantly improve cybersecurity resilience, organizational transparency, risk management, and strategic decision intelligence while supporting ethical and responsible AI adoption across enterprise ecosystems.
Policy-Over-Model Guardrails — An Agentic Mlops Control Plane For Safe Autonomy In Production Engineering And Infra
The paper is a qualitative study of the control plane development of policy-over-model which is safe and reliable agentic MLOps on the engineering as well as infrastructure setting. The paper includes a literature review of the available agentic systems, MLOps, governance and operational safety to determine critical areas of gaps and integration requirements. It has been found that agentic AI needs to have stronger policy controls, more articulate oversight roles, continuous monitoring, and transparent audit trails. The suggested framework unites these factors regarding a Model Custodian Agent with the assistance of evaluation, drift, and audit agents. The article offers a viable premise of safer autonomous activities and system development in the future
Generative AI–Powered Epidemiological Modeling Platforms for Autonomous Disease Surveillance
The increasing frequency of global health crises has highlighted critical limitations in traditional epidemiological surveillance systems, particularly in their ability to provide real-time insights, predictive intelligence, and adaptive response mechanisms. This paper proposes a generalized framework for Generative AI–powered epidemiological modeling platforms designed to enable autonomous disease surveillance at scale. By integrating advanced generative models, including large language models and probabilistic simulation techniques, with heterogeneous data sources such as electronic health records, environmental sensors, mobility datasets, and social media streams, the proposed platform enhances early outbreak detection, forecasting accuracy, and decision support capabilities. The study explores how generative AI can synthesize realistic outbreak scenarios, fill gaps in incomplete datasets, and dynamically adapt models based on evolving epidemiological patterns. It further examines architectural considerations, including data ingestion pipelines, model orchestration layers, real-time analytics engines, and cloud-native deployment strategies. Key contributions include a modular reference architecture, comparative analysis of generative versus traditional compartmental models, and evaluation metrics for model reliability, explainability, and scalability. The paper also addresses critical challenges related to data privacy, ethical governance, and model bias, proposing mitigation strategies aligned with global health data standards. Through conceptual modeling and system design perspectives, this research demonstrates how generative AI can transform epidemiological platforms into proactive, autonomous systems capable of supporting public health authorities in timely and informed decision-making.
Generative Artificial Intelligence (GenAI)
Deep Learning-Enabled Cell-Free DNA Analytics for Precision Reproductive and Preventive Healthcare
Cell-free DNA (cfDNA) occupies a unique place for various purposes in precision reproductive and preventive healthcare, as a product of metabolism and apoptosis of cells housed in different tissues and organs. Given the hitherto unusual success of deep learning (DL) in the domain of unconstrained problems across scientific fields, its application into cfDNA analytics also appears promising, potentially achieving clinical signals with similar efficiencies. Core principles are defined and linked to cfDNA functions in reproduction, suggesting cfDNA analysis for the achievement of related objectives. In particular, the sought-after conditions for DL introduction are discussed and proposed for the specific case of precision reproductive healthcare, whereby upcoming studies could be designed and assessed using these same elements. CFDNA is a biological fluid with two main clinical applications: sex determination of the fetus and non-invasive prenatal testing (NIPT) of fetal aneuploidies. Although both applications rely on a similar cfDNA approach, they target different clinical signals and rest on different models of CFDA analysis. Nevertheless, four fundamental requirements are common to both applications, conditions that have led to their indeed successful development and implementation. Two core conditions, related to cfDNA biology and the classification of the clinical signal under consideration, seem to be particularly relevant for DL implementation. These aspects are subsequently addressed, and the conclusions provide a perspective on the future of DL in CFDA.
Graph Neural Networks for HCC Risk Adjustment and Interoperability
Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death among patients with cirrhosis. Accurate estimation of prognostic risk is crucial for patient management and clinical trial design. Existing models for risk prediction after diagnosis of HCC are affected by high degrees of uncertainty, particularly in children and older adults. Additionally, while substantial heterogeneity exists in clinical outcomes, risk-prediction models do not officially incorporate it. One potential way to address these issues is through outcome predictive modeling techniques such as survival analysis with random-effects. However, existing random-effects models for HCC have only been built with a small number of variables, limiting translation into clinical practice . Two data sources, the National Inpatient Sample and the United Network for Organ Sharing dataset, were therefore merged Data quality and heterogeneity remain major challenges and limitations of these datasets. As in many domains, hospital diagnosis codes are the only data readily available for a substantial share of patients. Furthermore, different organizations use different combinations of variables for clinical assessment and prediction. The use of data from multiple sources adds complexity because of differences in coding and outcomes between organizations. Model performance will not be meaningfully improved by simply using more variables. Standard summary statistics on prediction accuracy are sensitive to differences in data quality and coding practices. The utility of existing data therefore extends beyond just risk prediction to risk adjustment, that is, ensuring fair comparisons across different demographics. An unsupervised graph representation of the data that jointly identified high-quality codes and dislike-like patterns was applied
Adaptive Cloud Cybersecurity Architectures for Real-Time Threat Detection and Compliance Monitoring
Cloud computing has transformed the digital ecosystem by enabling scalable, flexible, and cost-efficient infrastructures for organizations across industries. However, the increasing adoption of cloud technologies has also intensified cybersecurity challenges, including sophisticated cyberattacks, unauthorized access, ransomware, insider threats, and compliance violations. Traditional security mechanisms often fail to provide dynamic protection against rapidly evolving threats in distributed cloud environments. This research explores adaptive cloud cybersecurity architectures designed for real-time threat detection and compliance monitoring. The study emphasizes the integration of artificial intelligence, machine learning, behavioral analytics, automation, and zero-trust security principles to enhance cloud resilience. Adaptive architectures continuously monitor cloud infrastructures, analyze network behavior, identify anomalies, and respond automatically to potential threats while ensuring regulatory compliance with standards such as GDPR, HIPAA, ISO 27001, and PCI-DSS. The research further investigates the role of Security Information and Event Management systems, cloud-native security tools, and automated incident response mechanisms in minimizing security breaches and operational risks. A comprehensive methodology involving qualitative and quantitative analysis is proposed to evaluate the effectiveness, scalability, and responsiveness of adaptive cloud security frameworks. The findings suggest that adaptive cybersecurity architectures significantly improve threat visibility, reduce response times, enhance compliance management, and strengthen organizational security posture. The study contributes to the development of intelligent and proactive cloud security solutions capable of addressing modern cybersecurity challenges in highly dynamic cloud environments
AI Driven Intelligent Accessibility Optimization for Large Scale Enterprise Web Systems using Cloud Architectures
This scholarly paper aims at providing an artificial intelligence solution in order to get accessibility of the enterprise web systems optimized to large sizes under cloud structures. The proposed model will involve machine learning and intelligent automation to detect and address the dynamically emerging access issues in tricky web situations. The system monitors the elements of the web constantly, builds on user interaction data and applies adaptive additions to fulfill the needs of accessibility, including WCAG, with the capability of scale out in a cloud environment. The framework has various main elements, such as an AI-powered accessibility assessment engine, a real-time remediation module, and a cloud-based layer of orchestration to control the distribution of resources and system performance. The strategy will enable businesses to have a high performing site with minimal human intervention and making the site more accessible to individuals with disabilities. The outcomes of experimental testing lead to a great improvement of accessibility scores, as well as responsiveness of the system in case of the enterprise web platforms. The results of this study demonstrate how AI solutions and cloud computing may apply to fulfill the growing demand to provide inclusive and in large volumes digital services.
Adaptive Multi-Cloud AI Architectures for Predictive Healthcare Analytics and Cybersecurity Automation
Adaptive multi-cloud AI architectures are transforming predictive healthcare analytics and cybersecurity by integrating intelligent automation, scalable computing, and secure data orchestration across distributed cloud environments. These architectures leverage machine learning, deep learning, and automated workflows to process large volumes of heterogeneous healthcare data, including electronic health records, medical imaging, and real-time sensor data. Automation plays a critical role in optimizing resource allocation, model deployment, threat detection, and incident response, thereby improving operational efficiency and reducing human intervention.In predictive healthcare analytics, automated AI models enable early disease detection, risk stratification, and personalized treatment planning. Simultaneously, in cybersecurity, adaptive multi-cloud systems enhance threat intelligence by continuously monitoring network activities, identifying anomalies, and mitigating risks in real time. The multi-cloud approach ensures redundancy, fault tolerance, and vendor flexibility, reducing dependency on a single provider while enhancing system resilience.However, challenges such as interoperability, data privacy, and system complexity persist. This paper explores the integration of automation within adaptive multi-cloud AI frameworks, emphasizing their role in improving healthcare outcomes and strengthening cybersecurity infrastructures. The study highlights the need for standardized protocols, explainable AI, and advanced encryption mechanisms to ensure secure and efficient deployment.
Operational Intelligence for SAP: How AI Agents Transform Incident Response and System Health
For over two decades, the management of deeply integrated enterprise ecosystems has relied on the rigid, human-in-the-loop governance of traditional IT Service Management (ITSM) frameworks a paradigm that sprawling SAP and hybrid cloud infrastructures have rendered functionally obsolete. The industry’s subsequent pivot to legacy AIOps attempted to stem this operational bleeding through deterministic log parsing and alert suppression, which has become the approach for a decade. Such deterministic tools operate as narrow, procedural toys that flag anomalies but fundamentally lack the distributed cognitive architectures required for automated remediation. But what, then, is the actual operational utility of merely observing a failure without the algorithmic capacity to heal it? To move beyond the methodological navel-gazing that currently plagues the field, this research proposes a foundational reorientation: an Integrated Incident Response Model (IIRM) wherein goal-driven Agentic AI autonomously ingests unstructured SAP application logs and AWS telemetry, utilizing contextual multi-armed bandit optimization to dynamically negotiate root cause analysis. Empirical evaluation within a rigorously simulated, high-volatility hybrid cloud deployment demonstrates that these communicating agents profoundly reduce Mean Time to Resolution (MTTR) and maintain high diagnostic accuracy, entirely decoupling system recovery from human latency. While mathematically provable guardrails remain an absolute necessity to prevent catastrophic compliance failures, this transition from reactive monitoring to proactive algorithmic agency capable of reflecting, learning, and improving over time finally provides the architecture required to redefine the twenty-year arc of enterprise system resilience.