International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management
The International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM) is a bimonthly, peer-reviewed, open-access academic journal dedicated to the advancement and dissemination of knowledge in science, engineering, technology, and management. The journal welcomes high-quality original research, review papers, case studies, and short communications. Scope of the Journal IJMRSETM covers a wide range of topics including but not limited to: Science: Physics, Chemistry, Biology, Environmental Science, Life Sciences Engineering: Mechanical, Civil, Electrical, Electronics, Computer Science, etc. Technology: AI, IoT, Data Science, Cybersecurity, Software Engineering, etc. Management: Business Administration, Operations, Finance, HR, Marketing, Strategic Management
Cloud and AI Integrated Platforms for Intelligent Decision Making and Adaptive Enterprise Risk Management Systems
Cloud and artificial intelligence (AI) technologies are transforming enterprise risk management (ERM) by enabling real‑time data processing, predictive analytics, automation, and adaptive learning across complex organizational environments. This paper examines the integration of cloud computing with AI platforms to support intelligent decision‑making and adaptive ERM frameworks that respond dynamically to emerging threats and opportunities. It explores how scalable cloud infrastructures facilitate centralized data aggregation while AI algorithms enhance risk detection, classification, and prioritization. By synthesizing current research, industry practices, and methodological frameworks, this study outlines the value of unified cloud‑AI systems in strengthening organizational resilience and strategic foresight. Key benefits include improved risk visibility, faster response cycles, and the ability to simulate scenario outcomes using machine learning models. However, challenges such as data privacy concerns, model interpretability, dependency on quality data, and cloud service vulnerabilities are also highlighted. The research proposes a mixed‑method approach combining qualitative case analysis with quantitative performance evaluation of cloud‑AI ERM systems. Ultimately, the paper contributes an integrated theoretical framework and practical considerations for deploying cloud‑AI solutions that support adaptive, intelligent risk management in modern enterprises.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Integrated Cloud Resource Management Model for Sustainable High Performance Enterprise Computing
The growing adoption of cloud computing in enterprise environments has transformed the way organizations manage computing resources, enabling scalable, high-performance, and flexible IT infrastructures. However, the dynamic and heterogeneous nature of cloud environments presents challenges in resource allocation, energy efficiency, workload scheduling, cost optimization, and sustainability. This study proposes an artificial intelligence integrated cloud resource management model designed to optimize performance, reduce energy consumption, and enhance sustainability for enterprise computing systems. By leveraging machine learning, deep learning, and reinforcement learning algorithms, the model enables intelligent monitoring, predictive workload allocation, dynamic scaling of resources, and proactive fault management. Experimental simulations demonstrate that AI-driven resource management improves server utilization, reduces energy consumption by up to 25%, minimizes service-level agreement violations, and enhances overall system throughput. Additionally, the model incorporates predictive analytics for demand forecasting and anomaly detection, allowing proactive mitigation of resource bottlenecks and performance degradation. This research contributes to the development of sustainable high-performance computing architectures that balance operational efficiency, cost-effectiveness, and environmental responsibility, providing enterprises with a robust framework for AI-enabled cloud resource optimization and intelligent decision-making in dynamic IT environments.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Autonomous Cloud Native Enterprise Systems Using Machine Learning-Based Resource Optimization and Governance
The increasing adoption of cloud-native architectures in enterprises has led to complex resource management and governance challenges. Modern enterprise systems demand high scalability, optimal resource utilization, and dynamic workload management to ensure cost efficiency, reliability, and operational continuity. Traditional static resource allocation methods and manual governance frameworks are insufficient to handle the dynamic and heterogeneous nature of cloud-native infrastructures. This research proposes a Machine Learning (ML)-based framework for autonomous cloud-native enterprise systems that optimizes resource allocation and enforces governance policies. The framework leverages predictive analytics, reinforcement learning, and anomaly detection to monitor cloud workloads, predict resource demands, and dynamically allocate computing, storage, and network resources. Simultaneously, it integrates governance mechanisms to ensure compliance with internal policies, industry standards, and regulatory requirements. The methodology involves system architecture modeling, simulation-based performance evaluation, and comparative analysis with conventional cloud management frameworks. Results indicate that ML-driven resource optimization significantly reduces operational costs, enhances system performance, and ensures compliance with governance standards. The proposed autonomous cloud-native framework facilitates intelligent decision-making, reduces human intervention, and improves scalability, reliability, and governance in enterprise cloud environments.
Next Generation AI Driven Secure SAP Cloud Architecture with Analytics Automation and Scalability
The rapid evolution of enterprise technologies has significantly transformed organizational information systems, particularly in areas such as enterprise resource planning (ERP), cloud computing, and artificial intelligence. Modern enterprises increasingly rely on cloud-based ERP platforms to manage business operations, integrate data across departments, and enable real-time decision-making. Among these platforms, SAP cloud systems play a crucial role in supporting enterprise operations such as finance, supply chain management, human resources, and customer relationship management. However, traditional enterprise architectures often face challenges related to scalability, security, data integration, and intelligent automation. This research proposes a next-generation AI-driven enterprise architecture designed to support secure SAP S/4HANA Cloud environments, predictive intelligence, and autonomous business operations. The proposed architecture integrates artificial intelligence, cloud-native infrastructure, advanced data analytics, and automated orchestration mechanisms to create a resilient and intelligent enterprise ecosystem. Machine learning models are incorporated to enable predictive insights, risk detection, and intelligent decision support across business processes. The framework also emphasizes security, governance, and compliance to protect enterprise data within distributed cloud environments. By combining AI-driven analytics with modern cloud-based ERP systems, organizations can achieve higher operational efficiency, improved decision-making, and scalable digital transformation. The study presents architectural design principles, system integration strategies, and evaluation methods for implementing intelligent enterprise platforms that support autonomous business operations in modern digital enterprises.
Intelligent AI Driven Enterprise Data Architecture for Secure SAP Cloud Platforms with Hybrid Infrastructure and Digital Innovation
The rapid growth of digital technologies and data-driven business models has compelled enterprises to redesign their data architectures to support scalability, security, and intelligent analytics. Enterprise systems such as SAP generate large volumes of structured and unstructured data that require efficient processing, integration, and governance. Artificial Intelligence (AI) combined with modern cloud and hybrid infrastructure technologies provides a powerful solution for building intelligent enterprise data architectures. This research explores the development of an AI-driven enterprise data architecture designed to support secure SAP cloud platforms, hybrid infrastructure environments, and digital innovation initiatives. The proposed architecture integrates cloud computing, AI-based analytics, intelligent data integration frameworks, and automated security mechanisms to enhance enterprise data management capabilities. Hybrid infrastructure allows organizations to maintain critical workloads within on-premise environments while leveraging cloud resources for scalability and advanced analytics. AI technologies enable predictive analytics, automated data processing, anomaly detection, and intelligent decision support. The study also examines architectural layers, implementation strategies, and governance frameworks required for secure and scalable enterprise data ecosystems. The findings indicate that AI-driven enterprise data architectures significantly improve data accessibility, operational efficiency, system scalability, and security in SAP cloud environments. This research contributes to the development of modern enterprise architectures capable of supporting digital innovation and sustainable organizational transformation.
Autonomous AI Agents for Cybersecurity Operations and Continuous Compliance in Cloud Native Enterprise Platforms
The rapid expansion of cloud native enterprise platforms has introduced significant challenges in maintaining robust cybersecurity operations and continuous regulatory compliance. Modern digital ecosystems rely heavily on distributed microservices containerized applications and automated infrastructure management frameworks. While these technologies improve scalability and operational flexibility they also increase the complexity of monitoring security threats and enforcing compliance policies. Autonomous artificial intelligence agents have emerged as a promising solution for managing these challenges by enabling intelligent threat detection automated incident response and continuous compliance monitoring. This paper presents a conceptual framework for deploying autonomous AI agents within cloud native enterprise environments to enhance cybersecurity operations and regulatory governance. The proposed architecture integrates intelligent monitoring agents threat intelligence analytics compliance validation modules and automated response systems to create a self regulating security ecosystem. The framework leverages machine learning models behavioral analytics and real time telemetry to identify anomalies detect vulnerabilities and enforce policy compliance across enterprise infrastructures. Through coordinated interaction autonomous agents can analyze security events respond to incidents and ensure adherence to regulatory requirements without extensive human intervention. The study demonstrates how AI driven cybersecurity operations can improve threat mitigation reduce response latency and strengthen organizational resilience against evolving cyber threats. The findings highlight the importance of integrating intelligent autonomous systems into enterprise security architectures to support secure and compliant digital transformation in modern cloud environments.
Intelligent Multi-Agent AI and DevOps-Oriented Cloud Architecture for Autonomous Enterprise Operations and Infrastructure Optimization
The increasing complexity of enterprise IT infrastructures has driven organizations to adopt advanced technologies that enable automation, scalability, and intelligent decision-making. Cloud computing and DevOps practices have become essential components of modern enterprise systems, enabling rapid application development, continuous integration, and scalable deployment environments. However, managing large-scale cloud infrastructures and enterprise operations still requires significant manual intervention and monitoring. The integration of intelligent multi-agent artificial intelligence (AI) systems with DevOps-oriented cloud architectures offers a promising solution for achieving autonomous enterprise operations and infrastructure optimization. This research proposes an intelligent multi-agent AI framework integrated with DevOps-based cloud architecture to support autonomous monitoring, decision-making, and resource management across enterprise platforms. Multi-agent systems operate collaboratively to monitor system performance, detect anomalies, optimize resource allocation, and automate infrastructure management tasks. DevOps practices such as continuous integration, continuous delivery, infrastructure as code, and automated testing are incorporated into the architecture to enable efficient software development and deployment cycles. The proposed architecture aims to enhance system resilience, operational efficiency, and scalability in enterprise environments. Additionally, AI-driven agents utilize machine learning techniques to analyze operational data, predict system failures, and optimize infrastructure performance. The study highlights the potential benefits and challenges associated with implementing intelligent autonomous enterprise architectures within modern cloud-based digital ecosystems.
AI-Driven Cyber-Resilient Cloud-Native Enterprise Architecture for Secure Financial Healthcare Industrial and Government Digital Ecosystems
The rapid digital transformation of financial, healthcare, industrial, and government sectors has significantly expanded the attack surface of enterprise systems, introducing sophisticated cyber threats such as ransomware, advanced persistent threats (APTs), insider attacks, and supply chain compromises. Traditional perimeter-based security architectures are inadequate in addressing dynamic, cloud-native, and distributed environments. This paper proposes an AI-driven cyber-resilient cloud-native enterprise architecture designed to ensure adaptive security, operational continuity, regulatory compliance, and real-time threat intelligence integration across critical digital ecosystems. The proposed architecture integrates zero-trust principles, microservices-based modularity, containerized workloads, DevSecOps automation, and AI-powered behavioral analytics. Machine learning models are embedded across infrastructure, platform, and application layers to enable predictive threat detection, automated response orchestration, anomaly detection, and intelligent recovery mechanisms. The framework emphasizes resilience through self-healing systems, immutable infrastructure, distributed ledger-based audit trails, and multi-cloud redundancy strategies. A layered security governance model aligned with global compliance standards ensures sector-specific adaptability. The study contributes a unified architecture blueprint capable of protecting heterogeneous enterprise environments while enabling scalable innovation. The model supports proactive cyber defense, real-time risk assessment, and sustainable digital trust in mission-critical national and global digital infrastructures.
Cloud Powered Enterprise Healthcare Risk Analytics Using Adaptive AI Engines and Intelligent Data Pipelines
The rapid digital transformation of healthcare has generated massive volumes of clinical, operational, financial, and population health data. However, fragmented legacy systems and static analytics frameworks limit the ability of healthcare enterprises to proactively identify and mitigate risks. This research proposes a Cloud-Powered Enterprise Healthcare Risk Analytics Framework leveraging adaptive AI engines and intelligent data pipelines to enable scalable, real-time, and continuously learning risk intelligence. The architecture integrates cloud-native infrastructure, automated data ingestion pipelines, distributed processing engines, and adaptive machine learning models capable of self-optimization through feedback loops and drift detection mechanisms. The proposed system supports multi-domain risk analytics, including clinical deterioration prediction, hospital readmission risk, fraud detection, operational inefficiencies, and epidemiological surveillance. Intelligent data pipelines ensure data normalization, validation, governance, and interoperability using standardized healthcare protocols. Adaptive AI engines employ supervised, unsupervised, and reinforcement learning algorithms to dynamically recalibrate models in response to evolving patient populations and healthcare conditions. By embedding scalability, elasticity, automation, and governance within cloud ecosystems, healthcare enterprises can transition from reactive reporting systems to proactive, predictive, and resilient risk management infrastructures. This framework provides a comprehensive methodological and architectural blueprint for implementing enterprise-wide cloud-enabled healthcare risk analytics.
Privacy Preserving Big Data Streaming and Secure Cryptographic Architectures for Cloud Native Applications
The proliferation of cloud-native applications and real-time analytics platforms has transformed the way organizations process, analyze, and derive value from large-scale streaming data. Industries such as finance, healthcare, telecommunications, and smart infrastructure increasingly rely on continuous data streams generated from IoT devices, user interactions, transactional systems, and distributed sensors. While big data streaming frameworks enable high-throughput, low-latency analytics, they also introduce substantial privacy and security challenges. Sensitive personal, financial, and operational data often traverse distributed cloud environments, making them vulnerable to breaches, unauthorized access, and regulatory non-compliance. Privacy-preserving big data streaming systems integrate advanced cryptographic mechanisms with scalable cloud-native architectures to ensure data confidentiality, integrity, and availability without sacrificing performance. This research proposes a comprehensive framework that combines secure stream processing, cryptographic data protection techniques, and cloud-native orchestration models. The framework incorporates encryption-in-transit and encryption-at-rest, homomorphic encryption for secure computation, secure multi-party computation (SMPC) for collaborative analytics, differential privacy for anonymization, and zero-knowledge proofs for authentication and verification. The architecture leverages event-driven microservices deployed through container orchestration platforms and integrates distributed messaging systems such as Apache Kafka for high-throughput ingestion. Secure key management, identity federation, and role-based access controls enforce policy-driven governance across cloud-native environments. Furthermore, privacy-preserving machine learning models are integrated within the streaming pipeline to enable encrypted inference and federated analytics. A comprehensive evaluation methodology assesses performance metrics including throughput, latency, cryptographic overhead, scalability, and privacy budget guarantees. Simulation results demonstrate that optimized cryptographic acceleration and selective encryption strategies can significantly reduce performance penalties while maintaining robust privacy guarantees. The proposed model achieves secure real-time analytics suitable for regulatory-compliant cloud deployments. This study concludes that integrating cryptographic safeguards directly into cloud-native big data streaming architectures provides a resilient foundation for secure digital transformation. By balancing privacy protection with scalable performance, organizations can enable trustworthy real-time intelligence in increasingly interconnected cloud ecosystems
Real Time Fraud Detection and Risk Exception Handling in Financial Enterprise Platforms using AI and Cloud Native DevOps
The exponential growth of digital financial services has increased the exposure of enterprise platforms to fraud and operational risks. Traditional rule-based monitoring systems often fail to respond to evolving threats in real time, necessitating intelligent solutions capable of adaptive detection and rapid mitigation. This study investigates the application of artificial intelligence (AI) integrated with cloud-native DevOps practices for real-time fraud detection and risk exception handling in financial enterprise platforms. Leveraging machine learning and deep learning algorithms, the proposed framework identifies anomalous transactions, suspicious API calls, and unusual user behavior with high accuracy and low latency. Integration with cloud-native DevOps pipelines enables automated deployment, monitoring, and scaling of AI models, ensuring continuous availability and resilience under variable workloads. The research outlines a methodology for feature extraction, model training, and evaluation, as well as orchestration of risk handling workflows. Results demonstrate that AI-powered platforms can proactively detect potential fraud, generate risk alerts, and execute automated remediation actions, reducing financial loss and operational downtime. This study emphasizes the synergy between AI and DevOps practices, presenting a scalable, adaptive, and compliant framework for securing financial enterprise platforms in dynamic cloud-native environments.
AI Driven Enterprise Cloud Architecture with Blockchain Governance for Proactive Healthcare Risk Mitigation
The rapid digital transformation of healthcare systems has introduced unprecedented opportunities for proactive risk mitigation through artificial intelligence (AI), cloud computing, and blockchain governance. However, fragmentation of health data, cybersecurity vulnerabilities, regulatory complexities, and lack of interoperability continue to hinder predictive and preventive healthcare strategies. This paper proposes an AI-driven enterprise cloud architecture integrated with blockchain governance to enable secure, scalable, and intelligent healthcare risk mitigation. The architecture leverages machine learning models for predictive analytics, cloud-native microservices for scalability, and blockchain-based smart contracts for trust, transparency, and compliance enforcement. AI algorithms process multimodal healthcare data—including electronic health records (EHRs), wearable sensor data, imaging systems, and genomics—to predict clinical risks, operational inefficiencies, and financial fraud. Cloud infrastructure ensures elastic resource allocation, real-time analytics, and distributed system resilience. Blockchain technology introduces immutable audit trails, decentralized identity management, and automated regulatory compliance mechanisms. This integrated framework enhances interoperability, strengthens cybersecurity, reduces fraud, and improves patient-centric care delivery. The study explores design principles, literature foundations, and a comprehensive research methodology for implementation and validation. The proposed model supports predictive healthcare governance and sustainable digital health ecosystems, contributing to resilient, transparent, and data-driven healthcare enterprises.
AI and DevOps Driven Cloud Enterprise Networks for Digital Banking Healthcare and Government Platforms
Artificial Intelligence (AI) and DevOps-driven cloud enterprise networks are reshaping digital ecosystems across banking, healthcare, and government sectors. The convergence of AI-powered analytics, cloud-native architectures, and DevOps automation enables organizations to build scalable, secure, and resilient digital platforms capable of handling complex transactional workloads and regulatory requirements. Major cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud offer AI-integrated DevOps tools that streamline continuous integration, continuous deployment (CI/CD), infrastructure as code (IaC), and intelligent monitoring. In digital banking, AI enhances fraud detection, credit risk assessment, and personalized financial services. In healthcare, it automates claims processing, patient data analytics, and regulatory compliance. Government platforms leverage AI and DevOps to deliver citizen-centric services, digital identity systems, and transparent governance models. However, adoption introduces challenges related to cybersecurity, regulatory compliance, ethical AI, and operational complexity. This research explores architectural frameworks, implementation methodologies, governance models, and performance evaluation metrics for AI- and DevOps-driven cloud enterprise networks. It provides a structured approach to understanding technological integration, operational benefits, potential risks, and strategic pathways for sustainable digital transformation.
Continuous Integration and Continuous Deployment (CI/CD)
Real Time AI and Deep Convolutional Architectures for Cloud Digital Banking Healthcare and Telecom with DevOps and Secure Open Banking APIs
Real-time Artificial Intelligence (AI) and deep convolutional architectures are increasingly central to the transformation of cloud-based digital banking, healthcare, and telecom systems. This paper presents a unified framework that integrates deep convolutional neural networks (CNNs), real-time analytics, and cloud-native DevOps practices to support secure, scalable, and intelligent service delivery across highly regulated domains. The architecture is designed to operate within modern open banking ecosystems, leveraging secure API frameworks and continuous deployment pipelines to enable rapid innovation while maintaining compliance and cyber resilience. The proposed approach incorporates real-time data ingestion, AI-driven decision engines, and automated DevSecOps workflows to enhance fraud detection, clinical intelligence, and telecom service optimization. Deep convolutional models enable high-accuracy pattern recognition for transaction monitoring, medical image analysis, and network traffic classification, while AI-enhanced DevOps pipelines ensure continuous security validation and performance optimization. Secure open banking APIs and policy-aware access controls facilitate interoperability across financial, healthcare, and telecom platforms, including enterprise deployments built on SAP cloud environments. By unifying real-time AI, deep learning architectures, and secure cloud-native DevOps, the framework delivers improved operational agility, enhanced cyber defense, and reliable real-time intelligence. The paper demonstrates how cross-domain AI platforms can support next-generation digital services while safeguarding sensitive data and ensuring end-to-end system trust.
Scalable AI and DevOps Architectures for Big Data Storage and API Led Enterprise Transformation
Scalable AI and DevOps architectures are central to modern enterprise transformation, enabling organizations to harness big data, accelerate software delivery, and operationalize intelligent decision-making at scale. By integrating distributed storage systems, data lakes, and real-time streaming platforms with AI-driven analytics, enterprises can process high-volume, high-velocity, and high-variety data efficiently. Cloud-native DevOps practices—leveraging containerization, Kubernetes orchestration, Infrastructure as Code (IaC), and CI/CD automation—ensure resilient, elastic, and continuously deployable systems that support evolving business needs. API-led connectivity plays a critical role in this transformation by enabling modular integration across applications, data services, and AI models. Through layered API architectures—system APIs, process APIs, and experience APIs—organizations can decouple legacy systems, accelerate innovation, and create reusable digital assets. Intelligent observability, automated scaling, security-by-design principles, and policy-driven governance further enhance reliability and compliance. Together, scalable AI, modern DevOps, big data storage frameworks, and API-led architectures provide a foundation for agile, data-driven enterprises capable of sustained digital growth and operational excellence.
Machine Learning–Powered Enterprise Ecosystems for Compliance Monitoring and Security and Predictive Intelligence in Cloud-Native Governance Systems
The rapid adoption of cloud-native architectures has transformed enterprise systems into highly distributed, dynamic, and scalable ecosystems. While this transformation enhances agility and innovation, it simultaneously introduces complex governance, security, and regulatory compliance challenges. Traditional rule-based monitoring systems and manual audit mechanisms are insufficient to manage real-time threats, regulatory obligations, and operational risks in cloud-native environments. This study proposes a machine learning–powered enterprise ecosystem that integrates compliance monitoring and security and predictive intelligence within cloud-native governance systems. The proposed framework embeds machine learning models into event-driven microservices architectures, leveraging container orchestration platforms, policy-as-code mechanisms, and real-time data pipelines. Supervised, unsupervised, and reinforcement learning techniques are applied to continuous compliance validation, anomaly detection, identity risk scoring, and predictive risk modeling. The architecture incorporates automated governance controls aligned with regulatory standards such as GDPR, HIPAA, SOC 2, ISO 27001, and NIST frameworks. Results demonstrate enhanced detection of policy violations, reduced security incident response times, improved risk forecasting accuracy, and strengthened enterprise resilience. By integrating machine learning into governance workflows and cloud-native infrastructure, organizations transition from reactive compliance management to proactive and predictive governance intelligence. The research establishes a scalable architectural model for intelligent, secure, and compliant enterprise ecosystems operating in mission-critical cloud environments.
Enterprise Cloud Architecture for Healthcare Integrating Privacy Unified Payments AI APIs and CI CD Pipelines
The healthcare sector is rapidly adopting cloud technologies to enhance scalability, interoperability, and patient-centered services. However, enterprise-level healthcare systems must address complex challenges including data privacy, financial integration, artificial intelligence (AI) enablement, and continuous software delivery. This research proposes an Enterprise Cloud Architecture for Healthcare that integrates privacy-by-design principles, unified payment systems, AI-powered APIs, and CI/CD pipelines within a cloud-native ecosystem. The architecture leverages microservices, container orchestration, zero-trust security models, and standardized healthcare interoperability frameworks such as HL7 FHIR. Privacy is embedded through encryption, tokenization, role-based access control, and regulatory compliance automation aligned with HIPAA and GDPR. Unified payment modules streamline billing, insurance claims, and digital transactions into a secure financial framework. AI APIs enable predictive analytics, clinical decision support, fraud detection, and personalized healthcare services. CI/CD pipelines automate development, testing, security scanning, and deployment to ensure continuous innovation and system resilience. The proposed architecture addresses fragmentation, cybersecurity risks, operational inefficiencies, and delayed software updates common in legacy healthcare systems. This study outlines architectural components, governance mechanisms, validation strategies, and performance evaluation metrics, offering a comprehensive enterprise solution for secure, intelligent, and financially integrated digital healthcare transformation.
Secure AI Enabled Enterprise Ecosystems for Fraud Prevention Compliance Automation and Real Time Analytics
The rapid digitalization of enterprises has led to increasingly complex data ecosystems where vast amounts of sensitive information are processed across distributed platforms. While artificial intelligence (AI) enhances operational efficiency, fraud detection, and analytics capabilities, it simultaneously introduces new security, governance, and compliance challenges. This paper explores the design and implementation of secure AI-enabled enterprise ecosystems that integrate fraud prevention, automated regulatory compliance, and real-time analytics within a unified architectural framework. The proposed model emphasizes secure data pipelines, AI-driven anomaly detection, automated policy enforcement, encryption mechanisms, identity-centric access control, and continuous monitoring. By embedding security controls directly into AI workflows and analytics infrastructures, enterprises can proactively detect fraud, ensure compliance with evolving regulations, and generate actionable insights in real time. The study outlines architectural components, governance models, AI integration strategies, and system validation approaches. A comprehensive research methodology is proposed to evaluate performance, security resilience, and compliance effectiveness. While the integration of AI and security enhances enterprise intelligence and risk mitigation, implementation complexity, resource demands, and model vulnerabilities remain challenges. The paper concludes by identifying benefits, limitations, and future research directions for secure AI-enabled enterprise ecosystems.
Governance-Aware Secure Architecture for Real-Time Enterprise Data Exchange across Financial Healthcare and Advertising Platforms Using Generative AI
The rapid digital transformation of enterprises has intensified the need for secure, interoperable, and governance-aware data exchange across heterogeneous domains such as financial services, healthcare systems, and digital advertising platforms. These sectors manage highly sensitive information, including personal health records, financial transactions, and behavioral analytics, making them prime targets for cyber threats, data misuse, and regulatory non-compliance. Traditional data exchange frameworks often lack real-time governance enforcement, cross-domain trust mechanisms, and intelligent automation required to support modern enterprise ecosystems. This paper proposes a governance-aware secure architecture that integrates Generative AI with cloud-native microservices, zero-trust security models, and policy-driven data exchange protocols to enable real-time and compliant enterprise communication across financial, healthcare, and advertising environments. The proposed architecture introduces a multi-layered framework comprising secure data ingestion pipelines, AI-driven governance engines, federated identity management, privacy-preserving computation, and intelligent monitoring systems. Generative AI models are utilized for adaptive policy enforcement, anomaly detection, automated compliance reporting, and contextual data transformation. The architecture supports interoperability across enterprise resource planning (ERP), healthcare information systems, and programmatic advertising platforms while ensuring adherence to regulatory standards such as GDPR, HIPAA-like policies, and financial compliance requirements. Results indicate that integrating Generative AI within governance-aware architectures significantly enhances threat detection accuracy, reduces policy violations, and improves cross-domain data exchange efficiency. The framework also demonstrates improved scalability, reduced latency in secure data transactions, and enhanced transparency through audit-ready logs and explainable AI governance mechanisms. By combining real-time analytics, zero-trust principles, and AI-driven compliance automation, the proposed architecture provides a unified and resilient solution for secure enterprise data ecosystems. This research contributes a novel cross-domain architecture that bridges governance, security, and AI-enabled automation for modern enterprise environments. The findings highlight the potential of Generative AI to transform enterprise data governance by enabling adaptive, intelligent, and secure real-time data exchange across multiple regulated sectors.
AI-Powered Cloud-Native ERP Enterprise Systems with Information Retrieval Decision Analytics Cybersecurity and Zero-ETL Analytics
Modern enterprises increasingly rely on intelligent and resilient digital infrastructures to support real-time decision-making across finance, healthcare, manufacturing, and retail domains. Traditional enterprise resource planning (ERP) systems, while central to operational management, often suffer from rigid architectures, delayed analytics, and fragmented data pipelines. This paper proposes an integrated AI-powered cloud-native enterprise framework that enhances ERP operations through advanced information retrieval, decision analytics, cybersecurity, and Zero-ETL real-time analytics. The framework leverages microservices, container orchestration, and serverless computing to enable scalable and adaptive enterprise platforms. AI-driven modules support predictive analytics, anomaly detection, and reinforcement learning–based decision intelligence for financial risk management, fraud detection, and operational optimization. Information retrieval models using natural language processing and semantic search improve enterprise knowledge discovery and contextual decision support. A Zero-ETL architecture enables direct querying of operational data from cloud warehouses, reducing latency and enabling real-time dashboards. Cybersecurity is strengthened through zero-trust architecture, AI-based threat detection, and compliance-aware governance mechanisms. The proposed system demonstrates improved responsiveness, scalability, and decision accuracy compared to conventional enterprise platforms. This study contributes a unified enterprise architecture that integrates AI, cloud-native design, and real-time analytics to support secure, intelligent, and resilient enterprise operations.
Post Covid Teaching Approaches and Methodologies
Education is the pillar of every country’s development. The COVID-19 has produced a transformation in society that has, in turn, influenced the field of education. There has been a paradigm shift from face to face teaching to technology based curriculum delivery. Online learning during covid-19 pandemic has awakened and affirmed the necessity of learning based on digital technology. The face to face learning is still necessary. As a result, the learning pattern developed during post-covid pandemic combines face to face and online learning called as blended learning. It is a way of learning, combining the traditional way of face-to-face classroom instruction and teaching with the help of digital learning platforms and tools. This mode of learning is also known as Hybrid Learning, as it integrates technology and digital media with the traditional teacher-led classroom activities, providing more flexibility for students to customize their learning experience. The online mode of classes can be categorically classified under two heads, synchronous and asynchronous, based on the conduction of the classes. Synchronous mode refers to the type of learning where students and teachers are present at the exact location and at the same time for teaching and learning. This comprises in-person classes (where teachers and students are present in the same classroom), online meetings and live streaming of classes or demonstrations on Zoom, MS Teams, Google meet, and other platforms. Precisely it is a “real-time” type of learning where a group of learners is engaged simultaneously. Hence, it enables collaborations amongst the students and teachers to ask doubts and get them resolved on the spot. For example, webinars, online classrooms, and video conferences are examples of synchronous classes. Asynchronous mode refers to the universal form of teaching and learning that does not happen simultaneously or in the same classroom. The students are not present in the class at a prescribed time. However, they have access to the previously recorded lecture videos of their teachers in addition to online study materials. Students can respond through emails and any social media network. The teachers generally record their classes. This recording is made available to the students; it is a learner-centered approach, where the students can undertake any course without fulfilling the criteria of being present at the same time and exact location as the tutor. For instance, blogs, you tube videos, and online lectures are examples of asynchronous classes. One cannot deny the importance of the offline education system; the future would be directed towards blended learning guided towards online pivots and a digital mindset.
AI-Driven Cloud Enterprise Decision Platform for Predictive Fraud Detection and Secure Biometric Authentication across Networks
The rapid digitalization of enterprise ecosystems has significantly increased exposure to cyber fraud, identity theft, and unauthorized network access. Traditional rule-based security mechanisms are insufficient to detect sophisticated fraud patterns across distributed cloud and mobile environments. This paper proposes an AI-driven cloud enterprise decision platform that integrates predictive fraud analytics, secure biometric authentication, and cross-network intelligence to enhance organizational security and decision-making. The proposed architecture leverages machine learning, deep learning, and behavioral analytics within a scalable cloud infrastructure to identify anomalies, prevent financial fraud, and ensure secure identity verification across enterprise systems. The framework combines biometric modalities such as facial recognition, fingerprint scanning, and behavioral biometrics with real-time fraud detection engines. Data from enterprise resource planning (ERP) systems, mobile devices, financial applications, and network logs are aggregated into a centralized cloud data platform. AI models perform predictive risk scoring, anomaly detection, and contextual decision analysis. A zero-trust network model and encryption protocols ensure secure data transmission and regulatory compliance. The system incorporates explainable AI (XAI) and compliance-driven governance to support transparency, auditability, and ethical decision automation. Experimental simulations demonstrate improved fraud detection accuracy, reduced false positives, and enhanced authentication reliability compared with conventional enterprise security systems. The results highlight the platform’s effectiveness in enabling proactive threat detection, secure identity verification, and real-time decision intelligence. The proposed approach provides a scalable and compliance-aware solution for enterprises operating across cloud, mobile, and networked environments.
AI-Enabled Enterprise Platforms Integrating Financial Risk Analytics, Mobile Healthcare Communication, Intelligent Manufacturing, and Cybersecure SAP Systems
Artificial Intelligence (AI) has emerged as a transformative force in enterprise platforms, enabling organizations to integrate complex operational domains such as financial risk analytics, mobile healthcare communication, intelligent manufacturing, and cybersecure SAP systems. Modern enterprises operate within data-intensive, interconnected ecosystems where real-time decision-making, regulatory compliance, and system resilience are critical. AI-enabled enterprise platforms leverage machine learning, predictive analytics, natural language processing, and intelligent automation to enhance efficiency, accuracy, and adaptability across organizational functions. In financial risk analytics, AI improves forecasting, fraud detection, and credit assessment. In mobile healthcare communication, AI enhances patient engagement, diagnostics, and remote monitoring. Intelligent manufacturing benefits from AI-driven predictive maintenance, quality optimization, and supply chain coordination, while cybersecure SAP systems rely on AI to detect anomalies, prevent cyber threats, and ensure business continuity. This paper explores the integration of these domains within unified enterprise platforms, examining their technological foundations, strategic benefits, and implementation challenges. A comprehensive literature review and research methodology are presented to analyze current practices and future trends. The study concludes that AI-enabled enterprise platforms offer significant competitive advantages but require careful governance, ethical oversight, and robust cybersecurity strategies to ensure sustainable adoption.
A Secure AI/ML-Enabled Cloud Enterprise Framework with SAP and Data Warehousing for Mobile Healthcare Communication and Financial Web Platforms
The rapid convergence of mobile healthcare communication systems and financial web platforms has intensified the demand for secure, intelligent, and scalable cloud enterprise architectures. This paper proposes a secure AI and machine learning–enabled cloud enterprise framework that integrates SAP-based enterprise systems and centralized data warehousing to support real-time analytics, intelligent automation, and compliance-aware operations across healthcare and financial domains. The framework leverages cloud-native microservices, AI-driven decision intelligence, and secure mobile communication channels to enable reliable data exchange and adaptive service delivery. Advanced security mechanisms, including identity and access management, encryption, and policy-driven governance, are embedded to address data privacy, regulatory compliance, and trust requirements. The incorporation of SAP data warehousing enhances enterprise-wide data integration, reporting, and analytical consistency, while machine learning models support predictive healthcare insights and financial risk detection. The proposed framework emphasizes fairness, transparency, and auditability to ensure equitable and responsible AI adoption. Experimental analysis and architectural evaluation demonstrate improved scalability, data integrity, and operational efficiency, making the framework suitable for modern cloud-enabled healthcare and financial enterprise environments.
Secure Generative AI–Enabled Cloud Lakehouse for SAP Financial and Healthcare Analytics with Tableau-Driven Decisions
The increasing volume, velocity, and sensitivity of enterprise data in financial and healthcare domains demand advanced analytics architectures that are scalable, secure, and intelligent. Traditional SAP-centric data warehouses struggle to support real-time insights, unstructured data processing, and advanced artificial intelligence (AI) workloads. This paper proposes a Secure Generative AI–Enabled Cloud Lakehouse architecture integrated with SAP financial and healthcare data, enhanced by Tableau-driven decision intelligence. The architecture combines the flexibility of cloud data lakes with the governance and performance of data warehouses, while embedding generative AI for predictive, prescriptive, and narrative analytics. Security and compliance are enforced through encryption, role-based access control, data masking, and regulatory alignment with standards such as HIPAA and SOX. Tableau serves as the visualization and decision layer, enabling self-service analytics and AI-augmented insights for business and clinical stakeholders. Experimental evaluation and use-case analysis demonstrate improved query performance, enhanced data accessibility, reduced time-to-insight, and stronger governance compared to traditional architectures. The results confirm that the proposed approach effectively bridges SAP transactional systems, advanced analytics, and secure AI-driven decision-making in highly regulated industries.
AI-Driven Cloud and LLM Architectures for Risk-Sensitive Banking Analytics and Secure Web Applications in 5G Environments
The rapid digitization of banking services and trade platforms has created unprecedented opportunities for efficiency and accessibility, while simultaneously introducing complex financial, operational, and cybersecurity risks. Traditional fraud detection systems and risk management methods are increasingly inadequate in processing high-volume, high-velocity, and multi-modal data streams, particularly in real-time 5G-enabled environments. This study proposes an AI-driven cloud and Large Language Model (LLM) architecture for risk-sensitive banking analytics and secure web applications. The framework integrates predictive AI for anomaly detection, generative AI for simulating complex risk scenarios, and LLMs for analyzing unstructured data, generating interpretable insights, and supporting compliance efforts. Secure Extract–Transform–Load (ETL) pipelines standardize and anonymize data prior to analysis, while cloud-native deployment ensures scalability, fault tolerance, and low-latency performance. Privacy-preserving mechanisms, including differential privacy and secure multi-party computation, protect sensitive financial and transactional data. Experimental evaluations using real and simulated banking datasets indicate detection accuracy exceeding 95%, significant reductions in false positives, enhanced operational efficiency, and improved interpretability for human analysts. This work provides a comprehensive blueprint for deploying adaptive, secure, and intelligent financial analytics systems capable of operating efficiently in 5G networks, combining risk-sensitive decision-making with privacy-preserving mechanisms for modern banking and trade operations.
Secure GAN-Driven Cloud Intelligence for Big Data Healthcare Analytics with Randomized Interference Evaluation
The growing adoption of cloud-based healthcare platforms and large-scale medical data analytics has introduced new challenges related to security, robustness, and reliability of artificial intelligence models. This paper presents a secure GAN-driven cloud intelligence framework designed for big data healthcare analytics with a focus on randomized interference evaluation. The proposed approach leverages Generative Adversarial Networks (GANs) to model complex data distributions, enhance anomaly detection, and generate synthetic healthcare data for robust testing and validation. Cloud-native security mechanisms, including encryption, access control, and isolation policies, are integrated to protect sensitive patient information and ensure regulatory compliance. Randomized interference evaluation is employed to assess model resilience under data perturbations, adversarial noise, and workload variability, enabling systematic analysis of stability and performance degradation. Experimental results demonstrate improved robustness, scalability, and fault tolerance of healthcare analytics pipelines under diverse interference scenarios. The framework provides a reliable foundation for secure, scalable, and trustworthy AI-driven healthcare analytics in cloud environments.
Secure AI-Driven Cloud Framework for SAP-Based Healthcare Business Processes and Big Data Analytics
The rapid adoption of cloud technologies in healthcare enterprises has accelerated the migration of SAP-based business processes and large-scale data analytics to distributed cloud environments. However, this transformation introduces critical challenges related to data security, regulatory compliance, system scalability, and intelligent threat detection. This paper proposes a Secure AI-Driven Cloud Framework designed to support SAP-based healthcare business processes while enabling robust big data analytics. The framework integrates AI-powered security mechanisms, including anomaly detection and intelligent access control, with governed cloud data platforms to ensure data integrity, confidentiality, and availability. Secure APIs and network-aware cloud orchestration enable seamless interoperability across SAP systems, healthcare applications, and analytics pipelines. The proposed architecture supports real-time data processing, policy-driven governance, and compliance with healthcare regulations such as HIPAA-aligned security principles. Experimental evaluation and architectural analysis demonstrate improved threat detection accuracy, scalable data processing, and reduced operational risk. The framework provides a resilient foundation for secure digital transformation in healthcare enterprises leveraging SAP and cloud-native analytics platforms.
End-to-End Intelligent Automation Using AI and Machine Learning in DevOps Pipelines for Real-Time Analytics
<span style="font-size: 10.0pt; line-height: 115%; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: normal;">The growing complexity of modern software systems and the demand for rapid, data-driven decision-making have accelerated the adoption of intelligent automation within DevOps pipelines. This paper presents an end-to-end intelligent automation framework that integrates artificial intelligence (AI) and machine learning (ML) to enable real-time analytics and autonomous decision support across the software delivery lifecycle. The proposed approach embeds ML-driven monitoring, anomaly detection, and predictive analytics into continuous integration and continuous deployment (CI/CD) pipelines, allowing systems to adapt dynamically to changing workloads and operational conditions. By leveraging automated feedback loops, the framework improves deployment efficiency, system reliability, and operational visibility while reducing manual intervention. Experimental observations demonstrate enhanced pipeline performance, faster incident detection, and improved decision-making accuracy. The results highlight the effectiveness of AI- and ML-enabled DevOps automation in supporting scalable, resilient, and data-driven enterprise systems.
Secure Cloud-Native AI Finance Architectures with SAP Integration for Real-Time Machine Learning Feature Engineering and Identity-Aware Access Control in Healthcare
The convergence of cloud computing, artificial intelligence, and enterprise resource planning has transformed financial operations within healthcare organizations, enabling real-time analytics, intelligent automation, and data-driven decision-making. However, the integration of AI-powered finance platforms with SAP systems in cloud-native environments introduces significant challenges related to security, identity governance, data privacy, and regulatory compliance. This paper presents secure cloud-native AI finance architectures with SAP integration for real-time machine learning feature engineering and identity-aware access control in healthcare. The proposed architecture integrates SAP financial and operational data with cloud-native AI pipelines to support real-time feature engineering, model training, and inference for use cases such as revenue cycle optimization, fraud detection, and cost forecasting. Machine learning–driven feature stores and streaming analytics enable low-latency insights while maintaining data consistency and auditability. Security is enforced through identity-aware access control, zero-trust principles, and fine-grained authorization across SAP, cloud platforms, and AI services, ensuring that sensitive healthcare and financial data is accessed only by authorized users and systems. By combining SAP integration, real-time machine learning, and robust identity and access management, the architecture delivers scalable, compliant, and resilient AI finance platforms tailored for healthcare environments. The proposed approach demonstrates how cloud-native security and AI capabilities can enhance financial transparency, operational efficiency, and cyber resilience in mission-critical healthcare systems.