International Journal of Research and Applied Innovations
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
AI-Based Big Data Systems for Real-Time Disaster Response and Resource Allocation
AI-based big data systems support near real-time disaster response and resource allocation. These systems shorten reaction times by leveraging scarce resources (data, bandwidth, processing, and decision-making) more efficiently in disaster management. A layered architecture provides distributed computing, data fusion, and decision management for near real-time deployment. Future implementations will prototype alarm generation using social-media and sensor data. Large-scale disasters place severe and often insurmountable demands on emergency agencies. Under such conditions, resource allocation plays a critical role in minimizing disaster impact, but conventional methods based on historical data often cannot meet real-time requirements. The integration of geographical information with data from social networks, sensor networks, and other sources can facilitate the relevant prediction and decision-making processes, but the available data, computing, and human resources are all severely constrained—with the same scarcity of responses and alerts as for other data types. For these reasons, proposed methods shorten response times by applying AI-based big-data systems.
AI-Powered Big Data Analytics for Smart Retail Customer Journey Optimization
Intensifying of competition among modern retail companies necessitates a transition from big data to enhanced intelligence through optimization of the still-unknown customer journey by means of artificial intelligence and business analytics. Such a transition calls for a rethinking of conceptual foundations and development of a data ecosystem. Data constitute a fundamental part of modern intelligent systems. The new-age retail environment produces tremendous amounts and varieties of data about customer experiences during their journey, both online and offline. A model-based customer-journey-mapping and touchpoint-optimization technique and its supporting architecture are presented. Other needed technologies encompass a wide spectrum of artificial intelligence techniques for machine learning, deep learning, and natural language processing. Regulatory and ethical aspects of the exploitation of such a data ecosystem support the smart retail business. Customer journey mapping defines all stages of customer experience with a company or brand and includes its online and offline components. Every stage encompasses several touchpoints for customer experience assessment: awareness, consideration, intent, evaluation, purchase, consumption, and advocacy. For a comprehensive customer-journey model, advanced customer experience metrics are required in addition to those for each touchpoint: net promoter score, customer satisfaction score, customer lifetime value, customer engagement score, and customer effort score. All customer-journey metrics determine the optimization of touchpoint impact on profit.
Scalable Cloud Data Processing Models for Digital Banking Transaction Monitoring
Electronic banking offers new opportunities for credit unions and other community financial institutions. By allowing members to bank 24 hours a day from virtually anywhere in the world, the benefits of electronic banking must be weighed against the potential for increased fraud and illicit transactions. Regulatory requirements mandate that as a result of the Money Laundering Control Act of 1986, banks need to have a formal anti-money laundering program that includes procedures to monitor electronic banking activity. Cloud computing technologies provide scalable computing resources at a reduced operational cost, but their use for anti-money laundering systems has not yet been demonstrated. The characteristics of digital banking transactions indicate these systems may benefit from cloud-based resources. Appropriate cloud architectures and data ingestion and transformation methods are proposed to support real-time rule-based compliance system detection and machine-learning-based anomaly detection. Cloud processing scalability creates processing capacity for compliance requirements without negatively impacting the underlying economic advantage. Cloud computing has emerged as a disruptive technology enabling scalable and affordable data processing in a rapidly evolving environment, and organizations have seized the opportunity to move data storage and processing outside the enterprise firewall. Scalable data processing can be a significant advantage for organizations with irregularly occurring, high-volume access patterns. For compliance monitoring and operational reporting, however, regulatory requirements necessitate monitoring and reporting within a defined period. Such rigid time constraints reduce the potential operational cost benefit of cloud-scale processing resources. Digital transaction characteristics suggest compliance monitoring and anomaly detection can be separated and deployed in a hybrid processing model. The rule-based detection models of the operational system provide anomaly detection ground truth and a source of training data for machine-learning models. Cloud processing enables a scalable architecture to meet current and future capacity demands.
a cloud data management and processing system model
a bank transaction monitoring system that adheres to regulatory requirements
real-time transaction monitoring of digital banking transactions
near real-time monitoring of banking transactions
rule-based monitoring of banking transactions
machine-learning-based monitoring of banking transactions
full-text data storage for banking transactions
Cloud-Native Security and Governance Framework for SAP Enterprise Systems Enabled by Machine Learning
The rapid evolution of digital technologies has significantly transformed the way enterprises operate, manage data, and deliver services. Organizations today generate enormous volumes of data from enterprise resource planning systems, customer platforms, supply chains, Internet of Things devices, and digital business applications. However, many enterprises struggle to integrate these diverse data sources effectively, leading to fragmented systems and inefficient decision-making processes. Artificial Intelligence (AI) combined with cloud-based enterprise platforms offers a promising solution for addressing these challenges. SAP cloud technologies provide an integrated environment that supports intelligent data management, advanced analytics, and scalable digital infrastructures. This research paper presents an AI-driven SAP cloud architecture designed to facilitate intelligent enterprise data integration and enable scalable digital transformation. The proposed architecture combines SAP Business Technology Platform, machine learning services, cloud-native microservices, and automated data pipelines to create an intelligent enterprise ecosystem. The paper discusses the architectural framework, integration strategies, implementation methodology, benefits, and challenges associated with deploying AI-enabled SAP cloud architectures in modern enterprises. The study demonstrates how organizations can leverage AI-powered cloud technologies to improve operational efficiency, enhance real-time decision-making, and achieve sustainable digital transformation.
Secure Enterprise Ecosystems for AI-Enabled Financial Healthcare Intelligence Platforms and Autonomous DevSecOps Automation
The transformation of enterprise digital infrastructures through cloud computing has enabled organizations to deliver services more efficiently, scale operations, and integrate advanced analytics. However, this shift also introduces critical challenges in security, compliance, and operational efficiency, especially in industries handling sensitive data, such as finance and healthcare. Traditional security and operations models are insufficient for dynamic cloud environments, where continuous monitoring, rapid threat mitigation, and automated governance are required. This research proposes an AI-enabled secure cloud ecosystem framework for enterprise platforms, financial technologies, and healthcare intelligence, integrating autonomous DevSecOps automation. The proposed architecture leverages artificial intelligence for real-time threat detection, predictive analytics, and intelligent automation of security and operational workflows. Cloud-native microservices, container orchestration, and continuous integration/continuous deployment (CI/CD) pipelines are combined with AI-driven monitoring to enhance enterprise agility and resilience. The framework also incorporates autonomous DevSecOps principles, enabling automated security policy enforcement, vulnerability remediation, and operational scaling without human intervention. By integrating secure cloud-native infrastructure, AI analytics, and autonomous automation, the proposed ecosystem supports financial transaction integrity, healthcare data privacy, and enterprise operational efficiency. The study provides architectural design principles, integration strategies, and evaluation methodologies for implementing AI-driven secure cloud ecosystems that enable scalable, intelligent, and resilient enterprise platforms.
Liposome-Based Drug Delivery Systems for Targeted Treatment of Breast Cancer
Liposome-based drug delivery systems have emerged as an effective nanotechnological strategy to overcome these limitations by improving drug solubility, stability, pharmacokinetics, and tumor targeting. Liposomes are biocompatible phospholipid vesicles capable of encapsulating both hydrophilic and hydrophobic anticancer agents, enabling controlled drug release and reduced off-target effects. This review provides a comprehensive overview of liposome-based drug delivery systems for targeted treatment of breast cancer. It discusses breast cancer pathophysiology and molecular targets relevant to liposomal design, followed by an in-depth analysis of liposome structure, formulation strategies, and physicochemical properties influencing therapeutic performance. Passive targeting via the enhanced permeability and retention (EPR) effect, active ligand-mediated targeting, and stimuli-responsive liposomal systems are critically examined. The review further highlights therapeutic applications including liposomal chemotherapeutics, combination drug delivery, and gene/siRNA delivery approaches. In vivo pharmacokinetics, biodistribution, clinical progress of approved liposomal formulations, and ongoing clinical developments are also discussed. Additionally, safety, toxicity, and regulatory considerations are addressed, along with key challenges hindering clinical translation. Finally, emerging trends such as multifunctional liposomes, personalized nanomedicine, and scalable manufacturing strategies are explored. Overall, liposome-based drug delivery systems represent a promising and clinically relevant platform for improving the efficacy and safety of breast cancer therapy.
Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks
The rapid digitalization of enterprise ecosystems and financial networks has accelerated the adoption of cloud-first strategies, enabling scalability, flexibility, and operational efficiency. However, this digital transformation also introduces heightened cybersecurity risks, including advanced persistent threats, ransomware attacks, and vulnerabilities in distributed cloud and networked environments. This research proposes a cloud-first AI security architecture designed to protect enterprise digital ecosystems and financial networks through real-time threat detection, adaptive risk mitigation, and intelligent decision-making. The framework integrates artificial intelligence and machine learning models to continuously monitor system activity, network traffic, and user behavior, detecting anomalies and predicting potential attacks. Cloud-native technologies, including containerization, microservices, and orchestration platforms, support scalable deployment, high availability, and resilience. Zero-trust security principles enforce strict identity verification, multi-factor authentication, and behavioral access control across users, devices, and applications. Additionally, the architecture incorporates intelligent data governance to ensure compliance with financial regulations, secure data handling, and privacy protection. By combining AI-driven analytics, cloud-first deployment, and proactive incident response, the framework provides a comprehensive solution for securing complex enterprise infrastructures and financial platforms. The research demonstrates that integrating AI with cloud-first security strategies enhances operational resilience, reduces cyber risk exposure, and strengthens trust in digital financial ecosystems.
Next Generation AI-Integrated Cloud Systems for Secure Digital Transformation in Healthcare, Finance, and Smart Infrastructure
The rapid digital transformation of critical sectors such as healthcare, finance, and smart infrastructure has significantly increased the demand for secure, scalable, and intelligent cloud computing systems. Traditional security frameworks are insufficient to handle evolving cyber threats, data privacy requirements, and the complexity of distributed cloud-native architectures. This paper proposes a next generation AI-integrated cloud system architecture designed to support secure digital transformation across critical industries. The proposed framework integrates artificial intelligence, machine learning analytics, cloud-native infrastructure, and automated cybersecurity mechanisms to enhance threat detection, infrastructure automation, and continuous compliance management. The architecture incorporates intelligent monitoring, predictive vulnerability analysis, and automated response capabilities to ensure resilient and secure digital ecosystems. The proposed model demonstrates how AI-driven cloud systems can improve operational efficiency, protect sensitive data, and enable reliable digital services in healthcare, financial systems, and smart infrastructure environments.
Secure HR Data Exchange between SAP SuccessFactors and Payroll Using AI-Optimized Encryption, Masking, and Data Minimization Controls
Secure exchange of human resource data between cloud based human capital management platforms and downstream payroll systems has become a critical enterprise concern as organizations increasingly rely on distributed integration architectures. This study examines the confidentiality, integrity, and exposure risks inherent in data flows between SAP SuccessFactors and payroll platforms, with particular attention to personally identifiable and financial information subject to stringent regulatory obligations. The paper argues that traditional perimeter based security and static encryption alone are insufficient to address overexposure, misuse, and operational leakage of sensitive HR attributes during routine payroll processing. To address this gap, the study proposes a unified control framework that combines strong cryptographic protection, policy driven masking, and strict data minimization, augmented by AI optimized decision logic appropriate to the technological landscape of 2019. The framework leverages statistical risk scoring, rule augmented learning, and anomaly detection techniques to dynamically select encryption strength, masking profiles, and attribute level payload composition based on data sensitivity, purpose limitation, and operational context. A reference architecture is presented to demonstrate how these controls can be embedded across extraction, transformation, transmission, and ingestion layers without disrupting payroll accuracy or timeliness. Evaluation considerations focus on measurable reductions in data exposure, improved audit evidence generation, and enhanced governance transparency rather than speculative automation claims. The findings suggest that AI optimized security orchestration can materially improve trust and compliance in HR data exchanges while remaining compatible with established enterprise integration patterns. This work contributes a practical and extensible foundation for secure HR system interoperability and offers a defensible baseline for future research on adaptive data protection in enterprise information systems.
From Reactive Alerts to Predictive Intelligence: AI-Assisted Monitoring in Modern Cloud Environments
Modern distributed systems operating across cloud-native, microservices-based, and containerized environments generate massive volumes of high-velocity telemetry data, including logs, metrics, traces, configuration changes, and event streams. As system architectures grow increasingly decentralized and elastic, traditional rule-based monitoring approaches dependent on static thresholds and manually crafted alerts struggle to cope with dynamic workloads, ephemeral infrastructure, and evolving failure modes. These limitations often result in excessive false positives, alert fatigue, delayed root cause identification, and reactive incident management practices. Artificial Intelligence (AI) and Machine Learning (ML) techniques have therefore emerged as critical enablers of proactive incident detection, leveraging unsupervised, semi-supervised, and statistical learning models to automatically identify anomalous behavior patterns before they escalate into service degradation or outages. By incorporating scalable anomaly detection algorithms such as Isolation Forest, structured log parsing mechanisms like Drain, and real-time statistical learning frameworks for continuous anomaly scoring, AI-assisted monitoring systems transform raw telemetry into actionable insights. This paper presents a comprehensive overview of these AI-driven monitoring architectures, examining their algorithmic foundations, data preprocessing pipelines, streaming inference capabilities, and operational trade-offs, while highlighting how they collectively support early detection, adaptive learning, and resilience in modern production environments.
Machine Learning-Enabled Predictive Security and Governance Frameworks for SAP-Integrated Cloud-Native Enterprise Systems
The rapid transformation of enterprise digital ecosystems has led organizations to increasingly adopt cloud-native architectures integrated with enterprise resource planning platforms such as SAP. While these advancements provide greater scalability, operational efficiency, and real-time analytics capabilities, they also introduce complex security and governance challenges. Enterprise environments today must manage large volumes of sensitive data, distributed workloads, and multiple access points across hybrid and multi-cloud infrastructures. As a result, organizations require intelligent and predictive security mechanisms capable of proactively detecting threats, ensuring regulatory compliance, and maintaining governance standards across enterprise systems. Machine learning technologies have emerged as powerful tools for analyzing large-scale operational data and identifying patterns associated with potential cyber threats or governance violations. This research proposes a machine learning-enabled predictive security and governance framework designed specifically for SAP-integrated cloud-native enterprise environments. The framework combines AI-driven anomaly detection models, identity-centric governance policies, and automated monitoring mechanisms to improve enterprise security posture and operational reliability. The study evaluates the proposed framework through simulated enterprise infrastructure environments that generate large-scale operational logs, user activity records, and system transaction data. Experimental results demonstrate that predictive machine learning models significantly enhance threat detection accuracy and reduce response times compared to traditional rule-based security systems. Furthermore, the framework improves enterprise governance by enabling continuous monitoring of access control policies and compliance requirements. The research findings highlight the potential of integrating machine learning with cloud-native enterprise architectures to create resilient, scalable, and intelligent digital ecosystems capable of addressing modern cybersecurity and governance challenges.
Intelligent Data Lakehouse and MLOps Pipelines for Scalable Predictive Analytics in Cloud-Based Enterprise Systems
Enterprise systems increasingly rely on large-scale, cloud-based data platforms to support decision-making, operational efficiency, and strategic planning. Traditional data warehouses and lakes often struggle with scalability, integration, and real-time analytics requirements. The emergence of data lakehouse architectures, combined with machine learning operations (MLOps) pipelines, provides an integrated solution for scalable, high-performance predictive analytics in modern enterprise environments. This research proposes an intelligent data lakehouse architecture integrated with MLOps pipelines to enable scalable predictive analytics in cloud-based enterprise systems. The framework unifies structured, semi-structured, and unstructured data while supporting real-time ingestion, preprocessing, feature engineering, and model deployment. MLOps pipelines automate model training, testing, versioning, deployment, and monitoring, ensuring reproducibility, reliability, and continuous improvement of predictive models. The architecture leverages cloud-native technologies, including distributed storage, containerized services, and orchestration tools, to optimize resource allocation and scalability. Predictive analytics models provide insights for operational optimization, financial forecasting, customer behavior analysis, and risk assessment. The study highlights the benefits of combining intelligent data lakehouses with MLOps pipelines, including improved model performance, operational efficiency, and governance, while addressing challenges such as data heterogeneity, pipeline complexity, and cross-cloud interoperability.
Machine Learning–Enabled Enterprise Lakehouse and Multi-Cloud Data Architecture for Scalable Financial and Business Intelligence Systems
In the era of digital transformation, organizations generate massive volumes of structured and unstructured data from financial systems, enterprise applications, customer interactions, and external platforms. Traditional data warehouse architectures often struggle to handle the increasing complexity, scalability requirements, and real-time analytics demands of modern enterprises. The emergence of data lakehouse architectures and multi-cloud infrastructures has created new opportunities for building scalable and intelligent data ecosystems. This research explores a machine learning–enabled enterprise lakehouse architecture integrated with multi-cloud environments to support scalable financial analytics and business intelligence systems. The proposed architecture combines the flexibility of data lakes with the structured performance capabilities of data warehouses, enabling efficient storage, processing, and analytics of large-scale enterprise data. Machine learning models are integrated within the architecture to automate data analysis, detect patterns, predict financial trends, and support strategic decision-making. The multi-cloud approach ensures high availability, scalability, and vendor flexibility by distributing workloads across multiple cloud platforms. Additionally, the architecture emphasizes secure data governance, data quality management, and real-time analytics capabilities required for enterprise financial operations. The study highlights the benefits of integrating machine learning with lakehouse platforms for advanced analytics while addressing challenges related to data integration, infrastructure complexity, and operational costs.
AI-Augmented Cloud Monitoring Platforms for Predictive Infrastructure Reliability
Given the growing reliance of individuals and organizations on cloud computing services, prediction of failures and assurance of the reliability of these services have become paramount to minimizing associated costs. The observability provided by the telemetry of such services offers data-driven signals for the prediction of failures, as well as indicators of timing and policies for optimal, efficient, cost-effective operation. Experimental services have demonstrated the viability of applying state-of-the-art artificial intelligence techniques—anomaly detection, forecasting, root cause analysis, and recommendation systems—to the telemetry data of cloud service providers in novel ways. These services are ready for deployment, helping enterprises monitor their services, detect anomalies, and predict their future activity, among other needs. Nevertheless, success depends on a careful balance between the quality and the quantity of the telemetry data consumed. Despite significant resources devoted to the telemetry of cloud services, privacy-preserving monitoring remains a challenge. Failure prevention relies on tailored indicators of each service’s failure modes and time-to-failure windows, as well as a hypothetical cost-benefit analysis. Even with sufficient percentages, the predictive models of these signals require careful definition, evaluation, and monitoring. Dynamic sizing of resources and autoscaling policies can help minimize costs while guaranteeing quality of service, particularly when intertwined with the resource allocation policies of the cloud provider. A service can improve its overall stability and performance by controlling its own scaling policies.
Multi-Agent AI Architectures for Automated Customer Experience Management Platforms
A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios. A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios.
Customer Experience Management Platforms
AI-Driven Incident Prediction Models for Proactive IT Operations Management
The combination of AI and Big Data has opened many valuable opportunities for the digital enterprise across various business segments. One area that lacks sufficient attention, especially from the AI community, is proactive (event) management of enterprises’ IT operations. Proactive IT operation management constitutes one of the future trends of IT services, with the promise of lowering the cost of service outages and failures. Most organizations still perform IT operations in a passive manner, implementing preventive measures and performing incident and problem management instead of predicting service outages in advance. As a result, despite their demand and potential, such tools are usually not part of the operational toolbox of most organizations. Building on the available data streams, novel ML models and tools can be designed to predict failures in enterprise IT environments, service outages and IT service degradation. The information produced can be fully exploited in the context of enterprise monitoring, capacity management and business continuity management, and the generated alerts can assist incident management teams. These solutions assist organizations in moving toward a more proactive IT operations management strategy. The models can also serve other areas apart from predictive incident management. In any use case where a time series has to be closely monitored and where sudden changes can have a large impact, the proposed approach can be used. Use cases include real-time alerting and thresholding, predictive capacity planning and budget forecasting.
Next Generation Intelligent Enterprise Framework Integrating Generative AI IoT Analytics Predictive Security and Scalable Hybrid Cloud Infrastructure
The rapid convergence of Artificial Intelligence, Internet of Things (IoT), and hybrid cloud computing is redefining enterprise digital transformation. Modern enterprises require intelligent, scalable, and secure frameworks capable of processing massive real-time data streams while ensuring predictive threat mitigation and operational efficiency. This paper proposes a Next Generation Intelligent Enterprise Framework integrating Generative AI, IoT Analytics, Predictive Security, and Scalable Hybrid Cloud Infrastructure. The framework leverages Generative AI models for automated decision support, content synthesis, anomaly explanation, and adaptive business workflows. IoT analytics enables real-time data acquisition, edge processing, and predictive maintenance across distributed environments. Predictive security mechanisms employ machine learning for proactive threat detection, risk scoring, and automated response orchestration. Hybrid cloud infrastructure ensures elastic scalability, workload portability, and regulatory compliance across on-premise and public cloud environments. The proposed architecture introduces multi-layered intelligence, secure data pipelines, containerized microservices, and automated DevSecOps governance. Experimental modeling demonstrates improved operational resilience, reduced downtime, enhanced predictive accuracy, and optimized resource utilization. This research contributes a comprehensive architectural model and implementation methodology for enterprises transitioning toward AI-driven autonomous ecosystems.
Measuring Business Value of End-to-End Quote-to-Cash with SAP CPQ + CRM + ECC/S4HANA
This paper examines measurement frameworks for assessing business value of integrated quote-to-cash (Q2C) processes spanning SAP CPQ, SAP CRM, and SAP ECC/S4HANA systems. Through secondary research, this study explores key performance indicators, value realization mechanisms, and measurement methodologies for end-to-end Q2C implementations. Evidence demonstrates that integrated Q2C implementations reduce cycle times by 35-50%, improve forecast accuracy by 25-40%, and increase revenue capture by 5-15% through reduced leakage. Organizations implementing comprehensive Q2C strategies achieve competitive advantages through operational efficiency, improved cash flow, and enhanced customer experience. This paper synthesizes findings from academic literature, industry research, and case studies to provide frameworks for measuring and realizing Q2C business value.
Zero Trust AI Architecture for Enterprise Healthcare Risk Governance in Cloud Ecosystems with Secure Data Encryption
The accelerated adoption of cloud computing and artificial intelligence (AI) in healthcare has significantly enhanced predictive risk analytics, operational efficiency, and patient-centered care. However, increased data exchange, distributed infrastructure, and AI-driven automation expose healthcare enterprises to heightened cybersecurity, privacy, and governance risks. Traditional perimeter-based security models are insufficient in protecting sensitive health data within complex cloud ecosystems. This research proposes a Zero Trust AI Architecture designed specifically for Enterprise Healthcare Risk Governance, integrating secure data encryption, identity-centric access control, continuous verification, and AI governance mechanisms. The proposed framework adopts a “never trust, always verify” security paradigm across cloud-native AI pipelines. It embeds encryption at rest and in transit, role-based and attribute-based access controls, multi-factor authentication, secure API gateways, and continuous monitoring. AI risk governance modules incorporate model validation, bias detection, audit trails, explainability tools, and regulatory compliance alignment with HIPAA and GDPR standards. By combining Zero Trust principles with adaptive AI governance in cloud environments, healthcare enterprises can mitigate cyber threats, prevent unauthorized data access, ensure regulatory compliance, and maintain operational resilience. This study provides a comprehensive enterprise architecture and implementation methodology for secure, trustworthy, and compliant healthcare AI risk ecosystems.
Enterprise Risk Management, HIPAA Compliance
Architecting Near Real Time Data Integration Pipelines with PowerExchange and IICS Streaming
By February 2019, enterprises operating large scale transactional platforms increasingly required data integration architectures capable of delivering near real time data movement with strong reliability and minimal impact on source systems. Traditional batch oriented extract transform load processes, while still widely used, were insufficient for use cases involving operational analytics, customer facing applications, regulatory reporting, and downstream system synchronization. Organizations sought integration solutions that could propagate data changes continuously while maintaining transactional integrity and operational stability. Change Data Capture had become a well established technique for addressing these requirements by enabling incremental data movement based on committed changes in source systems. Informatica PowerExchange was widely adopted in enterprise environments as a log based CDC solution capable of capturing database changes with low latency and minimal overhead. At the same time, Informatica Intelligent Cloud Services introduced streaming ingestion and processing capabilities that allowed CDC events to be transported and consumed in near real time within cloud based integration pipelines. This paper examines near real time data integration architectures that combine PowerExchange for change capture with IICS Streaming for event transport and processing, as understood and implemented by early 2019. The discussion focuses on architectural patterns for integrating on premises transactional systems with cloud based targets, emphasizing reliability, ordering, scalability, and operational governance. Particular attention is given to how CDC streams were modeled, buffered, and consumed to support continuous data propagation without tightly coupling source and target systems. Finally, the paper synthesizes design considerations and operational trade offs associated with PowerExchange and IICS Streaming based integration pipelines. These considerations include latency management, failure recovery, schema evolution handling, and monitoring of end to end data flow health. The intent is to provide a practical and historically grounded view of near real time data integration patterns that were actively adopted by enterprises in February 2019.
These keywords capture the technical and architectural focus of enterprise data integration practices as of February 2019
hybrid on premises and cloud architectures
reliable propagation of transactional changes across distributed systems
An Apache Spark Driven AI Framework for Enterprise Healthcare Analytics with Genetic Algorithm Optimization and Blockchain Security
Enterprise healthcare organizations generate vast volumes of heterogeneous data from electronic health records, medical imaging systems, laboratory databases, wearable devices, and insurance platforms. Efficiently processing and securing this data while extracting actionable intelligence remains a critical challenge. This study proposes an Apache Spark–driven Artificial Intelligence framework that integrates Machine Learning (ML), Genetic Algorithm (GA) optimization, and Blockchain-based security for enterprise healthcare analytics. The framework leverages Apache Spark for distributed in-memory processing and scalable machine learning, supported by Apache Hadoop for reliable data storage. Genetic Algorithms are applied for feature selection, hyperparameter tuning, and dynamic resource optimization to enhance predictive performance and computational efficiency. Blockchain technology, implemented through Hyperledger Fabric, ensures secure, transparent, and tamper-proof data exchange among healthcare stakeholders. The integrated architecture improves prediction accuracy, reduces processing latency, enhances data integrity, and optimizes cloud resource utilization. Experimental evaluation demonstrates significant performance gains compared to conventional healthcare analytics systems. The proposed framework provides a scalable, secure, and intelligent solution for enterprise healthcare decision-making in distributed cloud environments.
Cloud-Native AI Pipelines for Real-Time Cyber Threat Intelligence
Cloud-native pipelines are expected to be able to analyze data in cloud data lakes using a reconciliation process to detect and process new types of tactical events relating to cyber threat intelligence. Data-related, source-related, and service-related considerations and requirements for real-time pipelines that deploy deep learning processes to create Cloud-native AI Pipeline for Ransomware Detection are discussed. The architecture is open-source-based, with a collection of repositories containing several online learning processes using cloud-native AI techniques. Cyber threat detection is a dynamic process that depends on the existence of detections of malicious event patterns from indicators and triggers for reactive responses by using relevant updated data sources. The technical challenge derives from the need for these detections, supporting indicators, and tactical action responses to be created and activated in a real-time automated fashion. An extensible and scalable event correlation engine has been proposed, capable of detecting malicious events occurring in real time. However, detection of new types of events is hampered by the requirement for expert-driven feature-extraction processes and supervised learning classifiers capable of recognizing only specific threats, limiting the detection space to only those attack types for which the model has been previously trained. In online learning processes under continuous deployment, the model is continuously trained as new data is ingested in the base, thus enabling the recognition of new types of events as they appear. Distributed cloud-native process architecture enables the continual deployment of adaptation processes into production that can use data stored in cloud data lakes for continuous retraining and adaptation. A new, integrated, end-to-end visual Cloud-native AI Pipeline for Ransomware Detection based on the recognition of the modus operandi of ransomware is implemented. Data-related, source-related, and service-related considerations for real-time pipelines built using cloud-native AI techniques that generate deep learning processes are described.
Continuous Deployment (CD) in AI Systems
Intelligent Security Operations Pipelines
AI-Driven Autonomous Knowledge Assistants for Enterprise IT Helpdesks
In today’s complex information technology (IT) environment, enterprises utilise increasingly sophisticated products and know-how to support critical business operations. Conventional IT assistance mechanisms are costly in terms of time, human resources, and finances. An alternative strategy is to implement an enterprise IT helpdesk knowledge assistant to answer questions and solve problems, primarily for first- and second-line IT Support and Service Desks. Such an autonomous knowledge assistant is an augmented and continuously assisted chatbot powered by artificial intelligence (AI) technologies. It supports omnichannel access, replicates the judgement and experience of human experts, continuously gathers insights from support conversations, provides recommendations for good practices and detection-prevention-action workflows, and ensures data governance. Although the enterprise knowledge assistant is primarily geared towards IT, it can be adapted for self-service in different business functions and industries. AI-driven autonomous knowledge assistants deploy AI technologies, including advanced Natural Language Processing (NLP) techniques, to ingest content, train large Transformer text generators with dedicated knowledge, and converse with users, drawing on internal company resources such as documentation, previously closed incidents, troubleshooting guides, run-book procedures, best practices, and ServiceNow CMDB data. Users may be service desk agents, system administrators, remote work users, colleagues providing assistance, or anyone else engaged in the operations and maintenance of the IT enterprise systems. These autonomous knowledge assistants overcome the data-privacy challenges of ChatGPT and similar systems by ingesting external content to create their own proprietary large language model.
Cognitive AI for Autonomous Security Operations Hybrid Threat Detection Intrusion Avoidance and SOC Resilience in Cloud-Native Ecosystems
The rapid evolution of cloud-native ecosystems has transformed enterprise infrastructure, introducing unprecedented scalability, agility, and complexity. However, this transformation has also expanded the threat landscape, exposing organizations to hybrid cyber threats that combine traditional attack vectors with advanced persistent techniques. Security Operations Centers (SOCs) face mounting challenges in managing alert fatigue, skill shortages, and increasingly sophisticated adversaries. Cognitive Artificial Intelligence (AI) offers a transformative approach to autonomous security operations by integrating machine learning, deep learning, natural language processing, and behavioral analytics into unified defense architectures. This paper explores how cognitive AI enhances hybrid threat detection, intrusion avoidance, and SOC resilience within cloud-native environments such as containers, microservices, and Kubernetes orchestration frameworks. By leveraging predictive analytics, automated incident response, and adaptive learning mechanisms, cognitive AI enables real-time anomaly detection and proactive mitigation of threats. The study examines architectural frameworks, operational models, and implementation methodologies that enable secure, scalable, and self-healing security ecosystems. It further evaluates advantages, limitations, and ethical considerations associated with AI-driven security automation. The findings highlight that cognitive AI significantly improves detection accuracy, response speed, and operational efficiency while redefining the future of autonomous cybersecurity governance.
Scalable Cloud and AI Integration for Intelligent Transportation Safety Systems
Cyber-Physical Systems (CPSs) integrate connected sensing (e.g., LiDAR, cameras), control, computing, wireless communication, and actuation components with physical objects and processes to achieve interactive and adaptive capacity. CPSs can improve safety for autonomous systems, such as Autonomous Land Vehicles (ALVs) and Uncrewed Aerial Vehicles (UAVs), by identifying and avoiding hazardous events during operation. However, the recent surge of application scenarios, traffic data requirements, and the number and size of interactive CPS participants have led to significant challenges in improving and ensuring operational safety for these intelligent CPSs. These challenges have prompted scholars and practitioners to integrate cloud infrastructure and Artificial Intelligence (AI) services with CPSs to create new cloud-enabled Cyber-Physical Systems (C-CPSs). These C-CPSs can reduce the validation and training cost of intelligence models and intelligently respond to unsafe events for traffic safety enhancement. In the context of Intelligent Transportation Systems, the combination of C-CPSs, Drones as a Service (DaaS), and ALV safety is addressed.
Leveraging Cloud-Enabled AI for Intelligent Traffic Prediction and Sustainable Urban Systems
Urban mobility is a major concern for large metropolitan areas. Cloud-enabled Artificial Intelligence (AI) technology may help to manage travel demand and offer a more sustainable urban mobility model for smart cities. An AI-driven, cloud-enabled architectural framework applied to traffic management and control business processes is proposed. It integrates four AI-based predictive models with real-time traffic control and incident-response systems. Different data-driven use cases, proved in two metropolitan areas, illustrate the applicability of this framework in real operational environments. Results show that this approach is capable of managing urban traffic in real time. In addition, it demonstrates how AI-based models can be developed, deployed, and operated within cloud environments, offering a decision-support capability. The central role of predictive traffic management systems in the cloud-enabled AI model for smart cities is assessed. Demand management processes and the integration of Mobility-as-a-Service platforms are also analyzed. These are mandatory steps to offer a sustainable traffic model for large metropolitan areas. Finally, other aspects such as environmental protection and energy consumption in urban mobility are examined. Traffic management by predictive systems enables a more accurate confrontation of real traffic conditions, improving the energy, environmental, and resilience contexts.
Building the Adaptable Enterprise: Trends in Composable and Event-Driven Salesforce Architectures
This article examines the paradigm shift within the Salesforce ecosystem from monolithic implementations to composable architectures. As organizations face increasing pressure to adapt to rapidly changing business requirements, traditional monolithic systems have revealed significant limitations in flexibility and scalability. The transition toward composable architectures enables businesses to assemble solutions from independent, interchangeable components rather than relying on tightly integrated systems. By leveraging platform capabilities such as Platform Events, MuleSoft APIs, Salesforce Functions, and Lightning Web Components, organizations can create more adaptable enterprise solutions. Event-driven design principles form the foundation of these architectures, facilitating loose coupling between components while supporting real-time responsiveness. The article analyzes architectural patterns, implementation strategies, and performance considerations that organizations should evaluate when adopting composable approaches, highlighting the balance between business agility and technical complexity
Autonomous AI Agents for Enterprise Workflow Orchestration in HR Platforms
Autonomous AI agents are an innovative approach to automating enterprise processes, including on Human Resources (HR) platforms. This work focuses on enterprise workflow orchestration – particularly in the HR domain – by enabling autonomous agents to undertake tasks using existing HR technology stacks. The goal of creating real-world enterprise applications leads to emphasizing the integration of these agents into standard HR platforms and ecosystems. HR platforms manage a variety of workflows across the employee lifecycle, such as recruiting, onboarding, and performance management. Although these workflows often follow a step-by-step pattern with well-defined decisions at each stage, they are currently performed either fully by humans or by utilizing automation tools such as Robotic Process Automation (RPA). These workflows could also be fully automated through the deployment of autonomous agents that would interface with the HR platform and take decisions on behalf of their human counterparts.
AI-Augmented Employee Lifecycle Management
Robotic Process Automation (RPA) Evolution
Enterprise AI Orchestration Architectures
Autonomous Decision Support in HR Platforms
Resilient Middleware and API-Driven Interoperable Platforms for Open Banking and Large-Scale Enterprise Integration
The rapid evolution of digital financial services and enterprise ecosystems has intensified the demand for interoperable, secure, and resilient integration platforms. Open banking initiatives, driven by regulatory frameworks and market competition, require financial institutions to expose standardized application programming interfaces (APIs) to third-party providers while maintaining strict security, compliance, and performance guarantees. Simultaneously, large-scale enterprises must integrate heterogeneous legacy systems, cloud-native applications, microservices, and external partner platforms. These dynamics necessitate robust middleware architectures capable of ensuring interoperability, fault tolerance, scalability, and governance. This research proposes a comprehensive framework for resilient middleware and API-driven interoperable platforms tailored for open banking and enterprise integration scenarios. The architecture integrates service-oriented and event-driven paradigms, API gateways, service meshes, message brokers, identity and access management (IAM), and observability layers. It incorporates resilience patterns such as circuit breakers, bulkheads, retries, idempotency controls, and distributed tracing to ensure system stability under high-load and failure conditions. The proposed model leverages cloud-native principles, container orchestration, and hybrid-cloud connectivity to support horizontal scalability and high availability. API lifecycle management—covering design, versioning, security enforcement, throttling, and monetization—is embedded within the middleware framework. Standards-based interoperability mechanisms such as RESTful APIs, OAuth 2.0 authorization, and JSON-based messaging enable seamless collaboration among banks, fintech firms, and enterprise partners. A methodological evaluation framework assesses system resilience, latency, throughput, fault recovery time, compliance adherence, and scalability. Simulation results indicate that implementing resilient middleware patterns significantly reduces downtime, enhances transaction reliability, and improves integration efficiency across distributed enterprise environments. The research concludes that resilient API-driven middleware platforms are foundational to sustainable open banking ecosystems and enterprise digital transformation. By combining architectural robustness, standardized interoperability, and adaptive scalability, organizations can enable secure data sharing, foster innovation, and maintain operational continuity in increasingly complex digital landscapes.
Integrated Cloud-Native AI and ML Framework for Secure and Compliant Healthcare–Financial Systems
The adoption of cloud-native AI and machine learning (ML) platforms is transforming enterprise systems across healthcare and financial sectors by enabling scalable, data-driven decision-making and secure, efficient communication. Cloud-native architectures allow applications to fully leverage cloud computing benefits, including elasticity, resilience, and service orchestration, while AI and ML provide predictive analytics, automation, and intelligent insights. In healthcare, AI-enabled platforms enhance patient communication, streamline clinical workflows, and support telemedicine services, while ensuring compliance with privacy regulations such as HIPAA. In financial systems, AI and ML facilitate fraud detection, risk management, and real-time transactional analysis. Security by design is critical for these platforms, integrating encryption, authentication, and access control into the architecture from inception to prevent data breaches, ensure compliance, and maintain trust among stakeholders. This research investigates the design, implementation, and evaluation of cloud-native AI and ML enterprise platforms tailored for healthcare communication and financial systems, emphasizing security, scalability, and performance. Simulation and case studies demonstrate that integrating cloud-native AI/ML with security by design improves operational efficiency, ensures secure data exchange, and enhances decision-making capabilities. The study provides guidelines for developing resilient, secure, and intelligent enterprise platforms.