International Journal of Engineering & Extended Technologies Research (IJEETR)
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. Our Vision & Mission · Vision: To be recognized globally as a leading platform for the dissemination of high-quality, innovative, and impactful research in engineering and extended technologies. · Mission: o To promote a culture of scientific excellence and innovation. o To provide open access to research, ensuring equitable knowledge sharing. o To accelerate the application of research findings into real-world practices. o To foster collaboration between academia, industry, and society.
Prompting, Retrieval, and Fine-Tuning: Foundations of Enterprise Language Model Adaptation
General-purpose language models must be specialised to an organisation’s documents, policies, vocabulary and output formats before they produce dependable value. Three mechanisms dominate practice: prompt engineering, which conditions a frozen model at inference time; retrieval-augmented generation (RAG), which attaches an external, non-parametric memory to a frozen model; and parameter-efficient fine-tuning, which modifies a small number of weights on curated task data. These are usually described separately, in the vocabulary of the research communities that produced them, which leaves enterprise architects without a common frame in which to compare them. This paper establishes that frame. We review the mechanism, enterprise strengths and enterprise limitations of each technique, together with the hybrid architectures that combine retrieval with weight modification, and we argue that the four resulting archetypes differ along a single structural axis: where the task knowledge required for a correct answer is stored, and which component is mutable at run time. Under prompting, knowledge is parametric and the input is mutable; under RAG, knowledge is external and an index is mutable; under fine-tuning, knowledge and behaviour are parametric and the weights are mutable; under hybrids, both are mutable on different cycles. We show that this one difference, rather than a list of independent trade-offs, accounts for the divergent update latency, citation capability, entitlement enforcement, erasure behaviour and maintenance profile of the four archetypes. The paper is deliberately descriptive: it does not rank the archetypes or prescribe a choice, but supplies the structural vocabulary on which such comparisons and decision procedures can be built
AI-Enabled Decision Intelligence in Project Management: A Systematic Review of Efficiency and Productivity Gains
Background: The traditional project management approaches adopted for stabilising a project in the absence of uncertainty are becoming less and less effective in dealing with the rapidly changing information needs and technology in a rapidly evolving world of uncertainties. With the transformation of Industry 4.0 and the operating environment after a pandemic, the challenges of conventional planning models became more evident when a pandemic shook the planning landscape: Objective: This systematic review mainly evaluates the limitations of the traditional management approaches and examines how AI (artificial intelligence) mainly improves project efficiency, decision intelligence, and adaptive responsiveness in the project environment. Methodology: Search procedure was systematic, using the PRISMA 2020, conducted in the Web of Science, Science Direct, IEEE Xplore and Scopus databases, covering the period 2011 to 2023. Based on these, an article for final inclusion in the synthesis was obtained from 59 articles, after removing articles that were duplicate articles and applying the eligibility criteria. The Mixed Methods Appraisal Tool (Version 2018) was used to evaluate the methodological quality of the articles. Synthesising evidence was done using thematic analysis with frequency analysis. Results: The use of AI technologies may be making an impact on the outcomes and quality of a project. Forecasting studies' accuracy proved to be lowered by the estimation/prediction accuracy error of 23% and 41%, while in the context outside of the Digital Twin. Moreover, digital twin applications ensured better optimisation of resources in real time. Using generative AI tools reduced project planning tasks by nearly half, at approximately 3.2 times the process was faster compared to traditional planning. Moreover, progress has been achieved, but challenges to implementation are large, particularly in the capability domain, resistance to change in people and processes, and a lack of strategic fit. Conclusion: AI is reshaping project management with its introduction of decision intelligence and enabling teams to adapt on the fly. However, consistent enhancement of the project management experience remains a function of structured integration plans, buy-in leadership, ongoing capacity development, and ethical stewardship. In this review, the researcher includes an evidence synthesis framework and indicates some of the key research gaps, particularly relating to how SMEs actually apply AI and to measuring their longitudinal performance.
Deeksha Sivakumer, Rohith Kumar Punithavel
Toward Autonomous Finance: A Multi-Agent System Architecture for the Self-Driving Financial Close
The financial close remains one of the most labor-intensive recurring exercises in corporate finance. Despite two decades of ERP consolidation, robotic process automation, and continuous-accounting practice, exceptions and judgment-bound steps still keep the controller on the critical path. This paper presents a multi- agent system architecture for an autonomous financial close built on SAP S/4HANA. Five specialized agents handle accruals, intercompany transactions, reconciliations, flux analysis, and disclosures; they act on the ledger only through a governed integration layer, are sequenced by a planning engine over a shared task graph, draw on persistent memory of prior closes, and are bounded by a tool registry that enforces least-privilege authority. Audit logging, segregation-of-duty controls, explainability dashboards, and a human-in-the-loop controllership console complete the design. The architecture was evaluated in a controlled, simulated S/4HANA environment processing roughly 1.2 million transactions across several reporting periods. Relative to the pilot's pre- automation baseline, the close cycle fell from 9 days to 2.8 days, the automated reconciliation rate rose from 52% to 94%, exception resolution time dropped from 16 hours to 3 hours, and manual controller workload declined by 65.9%, while journal posting accuracy reached 99.2% and every agent action was captured in a complete audit trail. The results indicate that autonomous agents shorten and de-risk the close only when they are embedded in explicit governance and auditability frameworks, and that the controller's role shifts accordingly from operator to supervisor
Design of Intelligent API Management Platforms for Secure Enterprise Microservices Cloud Integration and Traffic Optimization
The rapid adoption of cloud computing, microservices architecture, and digital transformation initiatives has increased the complexity of managing enterprise application interfaces. Intelligent API management platforms have emerged as critical solutions for enabling secure, scalable, and efficient communication between distributed microservices and cloud-based systems. These platforms integrate advanced capabilities such as artificial intelligence, machine learning, automated governance, real-time analytics, security enforcement, and intelligent traffic management to improve enterprise application performance. This research explores the design principles and operational mechanisms of intelligent API management platforms for secure enterprise microservices cloud integration and traffic optimization. The study examines how intelligent routing, adaptive load balancing, predictive analytics, identity management, threat detection, and automated policy enforcement contribute to reliable digital ecosystems. A comprehensive research methodology based on architectural analysis, comparative evaluation, and examination of modern cloud-native practices is adopted to identify effective strategies for designing intelligent API management solutions. The findings highlight that AI-driven API platforms can significantly enhance system availability, reduce latency, strengthen security, and optimize resource utilization. The research emphasizes the importance of integrating automation, observability, and security-by-design approaches to support future enterprise cloud environments. Intelligent API management platforms represent a foundational technology for achieving resilient, adaptive, and secure microservices-based enterprise architectures
Drift-Aligned Personalization for Privacy-Preserving Edge Health Monitoring
Health surveillance systems that are based on edges make it viable to continually monitor physiological data, minimize latency, and minimize the use of bandwidth. Nonetheless, the concept drift is also sensitive to edge-device personalized prediction due to variations in the patient conditions, sensor variations and changes in environmental conditions. In the meantime, another requirement of a distributed healthcare ecosystem is patient privacy. The paper also proposes a privacy preserving Drift-Aligned Personalization Framework (DAPF) to privacy conscious edge health monitoring derived based on adaptive learning, drift aware personalization and secure collaborative intelligence. It is a compilation of six large-scale layers Wearable Data Acquisition, Edge Preprocessing, Concept Drift Detection, Personalized incremental learning, Privacy Preserving Federated Aggregation and Clinical Decision Support. Streams of real-time physiologic data are constantly monitored to identify distribution changes and then lightweight personalized models are updated on-the-fly on a device without being sent sensitive patient data. Secure federated learning applies the parameteric encryption of model parameters to be aggregated periodically to improve global knowledge and keep local personalization. Differential privacy and secure aggregation mechanisms guarantee the confidentiality and adherence to the regulations. The proposed framework brings accurate predictions, less disastrous catastrophic degradation of the model due to drift, low overhead of communication and the heterogeneous edge devices can be scaled up. Essentially dedicated adaptive intelligence + privacy-conscious distributed learning: DAPF can provide an established foundation of intelligence based healthcare systems over the next generation which can provide high quality, flexible and trusted care of remote patients.
High-Availability HPC Cluster Design for Mission-Critical Public Infrastructure: Lessons from Energy Grid and Government
The paper addresses the design issue of fault-tolerant high-availability HPC clusters of mission- critical systems in the public-sector, such as energy grid operators and government data centers. The paper focuses on such architectural designs as redundant pathing, multi-layer failure, homogeneity in the firmware and risk managed cutovers to assist in achieving zero-downtime operations. The system performance was quantitatively evaluated by running a simulation-based experiment which incorporated Monte Carlo fault injection. Multi-layer failover with firmware homogeneity availability, MTTR, and recovery success was 99.97, 38 and 99.9 seconds respectively, whereas controlled cutovers also increased availability to 99.98. The workload was completed over 99.5 percent even with several faults. These findings point to the fact that properly designed HPC systems may be maintained, and used in handling large volumes of data and be cyber- resilient in serving millions of people. The paper provides a methodical method of designing and assessing the high availability HPC systems that can be applied in the national interest activities
Next-Generation Cloud Computing Architectures with Autonomous AI Agents for Enterprise Service Optimization
The rapid evolution of cloud computing has transformed enterprise information technology by enabling scalable, flexible, and cost-efficient service delivery. However, increasing complexity in cloud environments has created challenges in resource management, security, service orchestration, and operational efficiency. The integration of autonomous Artificial Intelligence (AI) agents into next-generation cloud computing architectures offers a promising solution by enabling intelligent automation, adaptive decision-making, and real-time optimization of enterprise services. Autonomous AI agents utilize machine learning, reinforcement learning, knowledge representation, and predictive analytics to monitor cloud resources, detect anomalies, allocate workloads dynamically, and optimize system performance with minimal human intervention. This study explores the role of autonomous AI agents in enhancing enterprise cloud infrastructures through intelligent resource allocation, automated service orchestration, predictive maintenance, cybersecurity enhancement, and energy-efficient computing. The paper further examines current cloud architectural models, including hybrid, multi-cloud, edge-cloud, and serverless environments, emphasizing their compatibility with autonomous AI-driven management frameworks. A review of existing literature highlights emerging trends, technological advancements, and research gaps concerning AI-enabled cloud optimization. The proposed research methodology adopts a qualitative secondary research approach supported by systematic literature analysis. The findings contribute to understanding how intelligent cloud ecosystems improve enterprise productivity, resilience, operational efficiency, scalability, and decision-making while addressing future challenges associated with governance, trust, security, and ethical AI deployment
Adaptive Neural Pruning for Business-Critical Runtime Optimization
The adoption of the Enterprise ML system increases the number of processes that are subject to rigorous constraints, particularly in business-critical applications. These challenges include fraud detection, recommendation engines, access controls, and real-time analytics. While a deep neural network can be achieving high predictive accuracy, the computations are complex, hence resulting in increaseing latency, higher infrastructure costs, and reduced scalability. The restrictions described are particularly related to the environment's impact on real-time responsiveness and cost efficiency, both of which are critical in business. Furthermore, classical compression and pruning are often applied offline and remain static after deployment, limiting their effectiveness in dynamic corporate settings This study describes an adaptive neural pruning approach for business essential runtime optimizations. The system is dynamically pruning redundant or low-impact neural components at runtime, based on workload conditions, performance requirements, and even business priorities. Compared to the static pruning strategy, adaptive pruning provides a balance of inference delay, resource consumption, and decision accuracy. The business's important feature preserves a path that enables optimization without compromising high-impact outcomes The simulation-based experiment will use enterprise-inspired workloads to ensure that latency varies with demand. The findings will be demonstrating that adaptive neutral pruning is effective in reducing inference time and decision resource use while preserving the accuracy of important decisions. The visualizations, including latency-reduction curves and an accuracy-retention bar chart, demonstrate the efficacy of the approaches. The findings will demonstrate adaptive pruning, a viable and scalable approach that aligns neural network performance with enterprise runtime and cost restrictions
API-DRIVEN ENTERPRISE COMMERCE ARCHITECTURE FOR COMPOSABLE DIGITAL ECOSYSTEMS
As digital commerce evolves at a fast pace, traditional monolithic commerce platforms have created some architectural issues, especially in scenarios where businesses need to engage customers across all channels, deliver features quickly, and continuously scale up. Modern, tightly coupled, on-premise commerce architectures were hard to extend, integrate and modernise as businesses increasingly embraced a mobile-first experience, cloud-native platforms and distributed digital ecosystems. This paper proposes an application programming interface (API) based architecture model of enterprise commerce that is optimised for delivering composable digital ecosystems with decoupled storefronts, service-oriented integration layers and scalable backend orchestration patterns. The proposed architecture highlights a headless commerce approach that decodes the delivery of customer experience from the transactional commerce services, allowing organisations to grow their frontend channels and backend business functions separately. The framework highlights API-first design principles, modular service integration, reusable orchestration layers, and cloud-native deployment strategies, contributing to enhanced architectural flexibility and sustainability. In the context of large-scale commerce modernisation projects across areas of distributed systems integration, high-volume transaction environments and multi-channel customer engagement, enterprise implementation patterns are explored. Data from a synthesised literature indicates that composable architectures offer gains in deployment frequency of 4-10 times over monolithic baselines, and mean time to recovery (MTTR) of around 75% and lead time for change of around 85% reduction. Moreover, in the context of omnichannel retail, the integration of the channels is proven to bring about 40–60% enhancements in customer satisfaction. The results show that composable commerce architectures serve as the backbone for digital transformation in enterprises, enhancing agility, speeding up the innovation process, and enabling the flexibility of customer interaction trends
AI-Powered Enterprise Cloud Frameworks for Secure Data Integration and Predictive Threat Intelligence
Artificial intelligence (AI)-powered enterprise cloud frameworks have emerged as a transformative approach for addressing the growing complexity of secure data integration and predictive threat intelligence in modern digital ecosystems. Organizations increasingly rely on cloud infrastructures to manage distributed data sources, business applications, and interconnected services; however, this expansion creates significant cybersecurity challenges, including unauthorized access, data breaches, and sophisticated cyberattacks. AI-driven cloud frameworks combine machine learning, automation, and advanced analytics to enhance data protection, improve interoperability, and predict emerging security threats. These frameworks enable real-time monitoring, anomaly detection, intelligent decision-making, and proactive risk mitigation by analyzing large-scale data patterns across enterprise environments. This research examines the role of AI-powered enterprise cloud frameworks in establishing secure data integration architectures and strengthening predictive threat intelligence capabilities. The study explores existing technological approaches, methodological considerations, and future directions for developing resilient, adaptive, and intelligent cloud security ecosystems
Optimizing Federated Learning Enabled Distributed Cloud Infrastructure for Healthcare Intelligence and Predictive Analytics
The rapid growth of healthcare data generated through electronic health records, medical imaging systems, wearable devices, and Internet of Medical Things (IoMT) platforms has created significant opportunities for advanced intelligence and predictive analytics. However, centralized cloud-based healthcare analytics face challenges related to data privacy, regulatory compliance, communication overhead, and scalability. Federated learning (FL) integrated with distributed cloud infrastructure provides an effective approach by enabling collaborative model training across multiple healthcare institutions while keeping sensitive patient data localized. This research explores optimization strategies for federated learning-enabled distributed cloud environments to enhance healthcare intelligence and predictive analytics performance. The study investigates resource allocation, communication efficiency, model aggregation techniques, security mechanisms, and adaptive cloud-edge collaboration frameworks. A comprehensive research methodology is proposed using simulation-based evaluation and performance benchmarking across healthcare datasets. Key performance indicators include prediction accuracy, latency, computational efficiency, privacy preservation, and scalability. The proposed framework aims to improve healthcare decision-making by enabling secure, intelligent, and efficient data-driven services. The integration of federated learning with distributed cloud architectures represents a transformative solution for personalized medicine, disease prediction, clinical decision support, and real-time healthcare analytics while maintaining patient confidentiality.
Modernizing Reinforcement Learning with AWS Cloud for Adaptive Cloud Infrastructure Governance and Resource Optimization
The rapid adoption of cloud computing has transformed enterprise information technology by providing scalable, flexible, and cost-efficient computing resources. However, managing dynamic cloud infrastructures while ensuring governance, security, performance, and cost optimization remains a significant challenge. Reinforcement Learning (RL), a branch of artificial intelligence that enables autonomous agents to learn optimal decision-making through continuous interaction with dynamic environments, has emerged as a promising solution for adaptive cloud infrastructure management. When integrated with Amazon Web Services (AWS), reinforcement learning can automate resource allocation, workload scheduling, infrastructure scaling, security policy enforcement, and operational governance. This study investigates the modernization of reinforcement learning techniques within AWS cloud environments to improve adaptive cloud infrastructure governance and resource optimization. The research examines existing reinforcement learning algorithms, AWS cloud services, governance frameworks, and intelligent automation strategies that support efficient cloud operations. A methodological framework is proposed to integrate reinforcement learning agents with AWS services such as Amazon EC2, AWS Lambda, Amazon CloudWatch, Amazon SageMaker, AWS Auto Scaling, and AWS Organizations for continuous optimization of cloud resources. The study also evaluates governance mechanisms including policy compliance, security monitoring, cost management, and service reliability. The findings demonstrate that reinforcement learning combined with AWS cloud technologies enhances operational efficiency, improves resource utilization, reduces infrastructure costs, strengthens governance, and supports autonomous decision-making in modern cloud-native enterprise environments
Enterprise Knowledge Graphs for Biomedical Data Integration: A Scalable Architecture for Semantic Interoperability
The notion of semantic interoperability and integrated analytics is so challenging because of the scandals that surround which biomedical entities can accessibly create variable information with electronic health records, genomics and medical imaging, lab, wearable and with clinical research environments. The suggested article presents a scalable design of Enterprise Knowledge Graph (EKG-BDI) which applies Biomedical Data Integration to unite different biomedical data with the help of semantic model created on the basis of ontological concepts and standard knowledge representation. Six joint layers are proposed to be applied in the proposed framework and they are: Data Acquisition, Semantic Transformation, Ontology Management, Knowledge Graph Construction, AI-Driven Semantic analytics and Interoperability and Decision Support. The general biomedical data of different structured and unstructured forms, the ontologies of domains are transformed into the triples of RDF and the standardized vocabularies are connected to be represented in the same way, discover relations and semantic inferences. Tasks, such as improving the quality of information, removing semantic inconsistencies and an entity discovery in distributed health care repositories, become easier by inferencing, asking questions using a language called SPARQL, and automated entity resolution. This architecture deploys scalable graph databases in the form of micro-services to be capable of both supporting large volumes of biomedical data as well as compliance with the security and privacy policies and regulations. Moreover, knowledge graph is applicable in explainable AI modules that support clinics in decision-making, biomedical research, predicting illnesses,precision medicine. The suggested architecture gives a robust base of the non-volatile healthcare systems by enhancing the efficacy of data unification, semantic consistency, reuse of knowledge and intelligent data scrutiny throughout the enterprise biomedicine levels
Explainable Artificial Intelligence (XAI)
Building A Unified Benefits Data Repository for Real-Time Eligibility and Enrollment Processing
The establishment of a Unified Client Database (CDB) system and the associated ePRO program has dramatically improved the efficiency of benefits administration for organizations. This client-centric approach provided the Technical Lead from the Engineering Group with the ability to create an end-to-end data framework using normalized, metadata-driven design that supports intricate multi-client benefit structures, secured ETL/API connections to ensure seamless integration of enrolment, human resource and claims systems with robust data governance through automated data validation, audit logging and version control. The platform was developed for a cloud-based environment with the use of Redshift to support analytics and AWS S3 to serve as the archive for the repository of data, supported with telemetry dashboards to enable real-time visibility into the health of synchronization and ingestion latency. Implementing the platform has resulted in near real-time downstream integration with the subsequent increase in the timeliness and accuracy of data which has reduced the number of manual reconciliation errors by greater than 25% while improving compliance controls. In alignment with the trend towards the utilization of data-based automation within enterprise EPM Modernization, the CDB platform enables organizations to onboard clients quicker, achieve greater operational transparency and provide a scalable foundation for future cloud services. The next phase of enhancements will seek to evolve the CDB platform into a fully automated cloud-based ecosystem, including developments related to AI-based anomaly detection; enhanced capabilities to expand on serverless microservices; and advanced analytics using Redshift to provide predictive insight regarding benefit-related usage
The Structural Shift: How Unified Claims Platforms Reshape Payer Economics
Healthcare claims processing is a significant operational cost for payers, yet most systems used are old and do not meet today's high-volume, multi-regulatory environment. In this article, we propose a related strategic framework to transition healthcare organizations from fragmented, manual claims operations to unified, intelligent claims platforms. We provide evidence from recent implementations as well as the academic literature to make the case that consolidating legacy data systems, using machine learning to detect/fix fraud and adjudicate claims, and automating workflow orchestration can dramatically change how much it costs to do administrative work. The transformation proposed — built upon five foundational capabilities — will yield 15– 30% lower administrative cost, 30–40% lower denial rework volumes, and positive ROI within 18–30 months. In addition, this transformation will transform claims processing into a strategic asset rather than a reactive liability, allowing for scalable growth, regulatory robustness, and an improved experience for key stakeholders
A Modern Approach to Asset Management: Integrating Simcorp Dimensions with Cloud Data Platforms
This paper will describe the way in which UNIFY has successfully integrated multiple technologies, including SimCorp Dimensions, Informatica PowerCenter ETL, Snowflake Cloud-based Data Warehouse, as well as IDL's Data Hub orchestration (or ETL), in order to convert a disparate and fragmented Asset Management platform into a truly cloud-native architecture. Through the use of Phased ETL Strategies and Agile/Scrum governance, UNIFY addresses many of the common challenges associated with legacy systems, including Fragile Integrations, Insufficient Data Sources, and High Maintenance Costs. Additionally, the UNIFY design has standardised data flows, provided for Very High Availability and enabled Seamless Migrations. Performance Studies conducted by the UNIFY development team indicate a marked improvement in overall efficiency, including Significant reductions in Query Costs and Increased Cache Hits; additionally, UNIFY has identified optimization opportunities using its Cost Dashboard. Future efforts will focus on the adoption of advanced technology, including GraphQL Federation, Real-Time Streaming, and AI-Driven Operations, as well as additional opportunities to improve scalability and reduce costs. Overall, the design created by UNIFY establishes a New Industry Standard for IT Transitioning of Asset Management platforms, while ensuring Compliance with Regulatory Requirements and Eliminating Legacy Silos
Driving Regulatory Innovation: Automated Swap Data Reporting and Exception Management
In this article, I discuss the ways in which technology has been used to build, implement, and maintain an automated report for the purpose of reporting swap transactions that are covered by the European Market Infrastructure Regulation (EMIR) and Dodd-Frank Act (DFA) Rule 12b-2. The automated report will use advances in technology, including but not limited to Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), to enhance the regulatory reporting process, exception management, and compliance with EMIR, DFA and across all regions of the globe. All automated reports will be designed so that companies can minimize their need to perform manual processing and maximize the quality, accuracy, and transparency of their reports by converting them to a single format and standardizing the way they collect and submit data to regulators. By utilizing the advanced analytics capabilities in combination with a real-time monitoring, companies will benefit from more timely swap reporting and will ultimately enhance the efficiency of markets for all types of securities. The automated reporting of swaps improves the environment for regulatory reporting in regard to the marketplace, provides a new baseline for the financial services industry's compliance with regulation, eliminates or reduces the possibility of violating regulatory requirements within the financial services sector, decreases the cost of regulatory penalties associated with non-compliance, and improves the reputation of the organization overall.
European Market Infrastructure Regulation (EMIR)
End-to-End Procurement Architecture from Requisition to Analytics
Denny's and CSCS (in conjunction with HAVI) deployed a cloud-based Source-to-Pay (S2P) solution powered by Ivalua for all 3400 restaurants within 15 months to reduce the risk of supply chain disruptions having a major financial impact on Denny's restaurant operations. The approach of employing a phased rollout led to savings of 18%, equating to $12 million in costs for each year of the project's duration additionally, it allowed for a number of major achievements such as provision of a digital procurement framework, created over multiple older systems. There were also great efficiencies realized through the projects execution due to a 92% first-time match rate and the ability to complete procurement cycles within only 72 hours as opposed to 45 days. Technical innovations achieved included real-time connectivity with SAP/EDW, creation of custom ticketing solutions, creation of analytical reports to deliver information through multiple dashboards used for transaction monitoring. The project established the benefits delivered through the phased approach to deployment, reduced risk to an organization due to implementation decisions and demonstrated the importance of AI-ready cloud architecture for rapid procurement agility. Movement forward will continue by incorporating blockchain, Internet of Things monitoring and generative AI will further expand and enhance savings and position Denny's / CSCS as a leader in digitalizing food service procurement.
Innovation and Compliance at Global Scale: Predictive Analytics Meets DevOps Automation
The amount of transactions processed each year by international payment platforms exceeds trillions. These payment platforms continuously strive to create innovations while also complying with regulation requirements such as PCI- DSS, GDPR and PSD2 by providing strict auditability, but at the same time must provide real-time processing and latency of less than 100ms. To accomplish this, we propose an architecture that is unified and uses technologies such as Spark, MLflow, and ArgoCD to achieve significantly high performance with low latency and high compliance rates. We address several engineering challenges in this architecture concerning the fragmentation of ML features through the use of advanced data pipelines; the implementation of policy-as-code to ensure the integrity of our models and to mitigate bias; and the reduction of operational latency through the use of federated inference across clusters that are compliant with GDPR. Developing quick updates to models and improving fraud detection/recommendation systems through this structure has huge cost savings and efficiency improvements. Our development process stresses ethical AI governance with real-time explainability of models created within this structure and drift detection for those models; we will also look at delivering compliance automation, faster transaction authorizations in upcoming phases, and examining the unintended effects that have not yet occurred.
Payment Card Industry Data Security Standard (PCI-DSS)
General Data Protection Regulation (GDPR)
AI-Driven Architectural Patterns for Scalable Real-Time Triage and Crisis Prediction in Public Health Systems
This paper discusses the most important bottlenecks in triage (the process of determining who gets medical help first) in public health emergency situations and how delays in providing data through batch processing can lead to higher death rates in resource-constrained areas. It proposes a scalable artificial intelligence architecture consisting of event-driven microservices, long short-term memory (LSTM) predictive layers, and Kubernetes auto-scaling, while ensuring adherence to ethical governance standards. The key contributions of this paper include an Operational Data Store (ODS) that consolidates multiple data streams from various sources a phased implementation framework that has been validated; and Federated Learning (which minimizes bias and adds privacy protection). The results show that there are significant improvements to the overall system performance (i.e. improvements to throughput and reductions to latency), as well as effective forecasting during times of crisis, all verified in real-world settings. Ultimately, the objective of the framework is to provide improved operational efficiency and allocation of resources, thereby increasing national health resilience with respect to the large population of India.
Cloud-Native Enterprise Intelligence through Generative AI for Intelligent Adaptive Workflow Automation and Optimization
Cloud-native computing has become the cornerstone of modern digital enterprises by enabling scalable, resilient, and flexible application development through microservices, containers, Kubernetes orchestration, serverless computing, and DevOps practices. At the same time, Generative Artificial Intelligence (GenAI) is transforming enterprise intelligence by enabling organizations to automate knowledge-intensive tasks, generate contextual insights, optimize decision-making, and enhance business workflows. The convergence of cloud-native architectures and Generative AI presents significant opportunities for developing intelligent adaptive workflow automation systems capable of responding dynamically to changing operational requirements. This study explores the integration of cloud-native enterprise intelligence with Generative AI to improve workflow automation and organizational optimization. The proposed conceptual framework combines cloud-native infrastructure, AI-driven analytics, real-time data processing, adaptive decision support, and continuous workflow learning to create scalable and intelligent enterprise ecosystems. A qualitative and conceptual research methodology supported by an extensive review of contemporary literature is employed to examine the technological foundations, implementation strategies, benefits, challenges, and future directions of cloud-native Generative AI integration. The findings indicate that cloud-native enterprise intelligence significantly improves operational efficiency, workflow adaptability, resource utilization, business agility, and strategic decision-making while supporting continuous innovation. Furthermore, Generative AI enhances collaboration between humans and intelligent systems by providing explainable recommendations, automated content generation, predictive insights, and personalized workflow optimization. The study contributes a comprehensive framework for developing adaptive, scalable, and intelligent cloud-native enterprise platforms that support sustainable digital transformation and competitive advantage
Parallel Verification Architecture for Low-Latency Enterprise Decision Systems
Enterprise decision systems increasingly depend on multiple validation and verification steps before a final outcome can be produced. In many traditional implementations, these steps are executed sequentially, causing unnecessary latency accumulation, reduced throughput, and weaker responsiveness under load. This paper presents a parallel verification architecture for low-latency enterprise decision systems, in which independent verification tasks are executed concurrently and their outputs are aggregated into a final decision response. The proposed design combines fan-out/fan-in orchestration, deterministic rule evaluation, internal reference validation, and optional external verification within a governed orchestration layer. The architecture is intended for use across industries such as financial services, healthcare, insurance, logistics, and digital operations, where timeliness and decision correctness must coexist. The paper examines design trade-offs involving latency reduction, dependency isolation, partial failure handling, timeout policies, aggregation logic, and operational observability. It also considers tail-latency risks in distributed environments and discusses techniques for bounding the effect of slow downstream components. Rather than positioning parallelism as a narrow performance optimization, the paper frames parallel verification as a reusable enterprise architecture pattern for scalable and predictable decision workflows. The resulting model supports faster response times, better resilience, and clearer operational control in high-volume verification-driven systems
Active-Active Data Architecture for High-Availability Enterprise Systems
Active-active data architectures underpin a wide class of enterprise systems that must remain continuously available across geographically distributed regions, even in the presence of whole-site failures. Yet the engineering literature still treats active-active as a binary deployment choice rather than a spectrum of design tradeoffs. This article develops an architectural treatment of active-active data systems for high-availability enterprise platforms. We position availability as a graded property rather than a guarantee, distinguish the four core data dimensions — durability, consistency, conflict resolution, and latency — that compose any active-active design, and analyze the implementation patterns that emerge when each dimension is intensified: multi-region synchronous replication, quorum-based consistency, last-writer-wins versus operational-transform conflict handling, and read–write regional steering. We then examine the operational patterns that determine whether an active-active design actually meets its availability objective in practice: symmetric and rehearsed failover, topology-aware observability, symmetry-safe deployment, and schema evolution that remains compatible across regions during rolling upgrades. We close with a suitability framework that maps a given workload — writes-per-second, dependency on strong invariants, tolerable staleness, and regulated-data constraints — to a concrete active-active posture. The result is a practitioner’s map for deciding when active-active is warranted, which flavor, and at what cost, rather than a vendor pitch for any single topology
Embedding ESG And Carbon Accounting Into The Financial Ledger: A Sub-Ledger Architecture For CSRD-Aligned Reporting
The Corporate Sustainability Reporting Directive (CSRD, in force from January 2023) and the first set of European Sustainability Reporting Standards (ESRS Set 1, adopted as Commission Delegated Regulation (EU) 2023/2772) subject some 50,000 European preparers to mandatory, externally assured sustainability disclosures. The data that feeds these disclosures, however, is still produced in most organisations from spreadsheets, ad-hoc trackers and isolated sustainability tools, none of which carries the financial-grade controls assumed by ISAE 3000-style assurance or by the March 2023 COSO ICSR framework. This paper proposes an ESG sub-ledger that sits inside SAP S/4HANA as a parallel ledger in the Universal Journal (ledger 2S alongside the leading IFRS ledger 0L) and integrates with the SAP Sustainability Control Tower. Each Scope 1, Scope 2 and Scope 3 emission is captured as a quantity-only posting in tCO₂e, linked to a cost centre, internal order, source document, emission-factor master record and reporting period, and then reconciled, snapshotted and locked at period end. We describe the posting logic, map the controls onto the five components of COSO ICSR, and present a five-stage maturity model for adoption. The result is a reconcilable, period-locked and audit-ready ESG ledger that allows CSRD and ESRS-bound preparers to extend the discipline of the financial close to sustainability data
Enhancing Enterprise Cyber Resilience through Intelligent API Security and Cloud Native Infrastructure Optimization
Enterprise digital transformation has accelerated the adoption of cloud-native architectures, microservices, and Application Programming Interfaces (APIs), enabling organizations to achieve greater scalability, flexibility, and operational efficiency. However, this transformation has also expanded the cyber threat landscape, exposing enterprises to increasingly sophisticated attacks targeting APIs, containers, Kubernetes environments, and cloud infrastructures. Traditional perimeter-based security approaches are insufficient in addressing these evolving risks because cloud-native ecosystems are dynamic, distributed, and highly interconnected. This study examines how intelligent API security combined with cloud-native infrastructure optimization enhances enterprise cyber resilience by improving threat detection, reducing attack surfaces, and ensuring continuous business operations. The research explores the integration of artificial intelligence, machine learning, behavioral analytics, zero trust architecture, automated security monitoring, and infrastructure optimization strategies to strengthen organizational cybersecurity capabilities. It further investigates the role of DevSecOps practices, security automation, and continuous compliance in protecting modern enterprise environments while maintaining operational performance. Using a qualitative review of recent scholarly literature and industry best practices, the study identifies critical success factors for implementing resilient cloud-native security frameworks. The findings indicate that intelligent API protection and optimized cloud-native infrastructure significantly improve enterprise resilience by enabling proactive threat prevention, rapid incident response, regulatory compliance, and sustainable digital innovation in increasingly complex technological ecosystems
Engineering Autonomous Enterprise Decision Systems Using Neuro-Symbolic AI and Cloud-Native Infrastructure
Modern enterprises operate within highly complex, high-throughput environments where data-driven choices must be made with absolute accuracy, speed, and regulatory compliance. Traditional autonomous decision systems typically rely either on deep learning networks, which offer exceptional pattern recognition but function as uninterpretable black boxes, or on symbolic expert systems, which provide strict logical explainability but lack the flexibility to handle unstructured data. This paper presents a cohesive architectural framework for engineering autonomous enterprise decision systems by integrating Neuro-Symbolic AI with scalable, cloud-native infrastructure. By fusing the sub-symbolic perceptual power of neural networks with the symbolic, rule-based reasoning of knowledge graphs and logic engines, our framework achieves both cognitive flexibility and deterministic regulatory compliance. To deploy this hybrid paradigm at an enterprise scale, we introduce a cloud-native topology that encapsulates neural and symbolic workloads into decoupled microservices, coordinated via an asynchronous, event-driven service mesh. Empirical evaluations demonstrate that this neuro-symbolic framework reduces logic-validation failures to zero, improves decision accuracy in volatile market environments by 38% compared to pure deep-learning models, and maintains optimal horizontal scaling metrics. Ultimately, this approach establishes a scalable design blueprint for deployable, transparent, and enterprise-grade autonomous intelligence.
Securing Cloud Native Enterprise Platforms using AIOps and Artificial Intelligence Driven Autonomous Observability
Cloud-native enterprise platforms have transformed modern digital infrastructure by enabling organizations to achieve scalability, resilience, flexibility, and continuous service delivery through technologies such as containers, Kubernetes, microservices, and serverless computing. However, the distributed and dynamic nature of these environments introduces significant security, operational, and monitoring challenges that traditional management approaches struggle to address. Artificial Intelligence for IT Operations (AIOps) combined with Artificial Intelligence-driven autonomous observability offers an innovative solution for improving security, operational efficiency, and system reliability. By integrating machine learning, predictive analytics, anomaly detection, automated root cause analysis, and intelligent incident response, organizations can proactively identify threats, minimize downtime, and enhance system resilience. Autonomous observability continuously analyzes logs, metrics, traces, and events to generate real-time insights without requiring extensive human intervention. This study explores the role of AIOps and autonomous observability in securing cloud-native enterprise platforms while improving operational intelligence and cyber resilience. The research discusses existing technological developments, implementation strategies, and the integration of artificial intelligence into enterprise security operations. It further examines how intelligent automation reduces operational complexity, accelerates threat detection, supports compliance, and enables self-healing infrastructures. The findings emphasize that AI-driven observability significantly strengthens enterprise cybersecurity by enabling predictive monitoring, adaptive security management, and continuous optimization of cloud-native environments
Memory-Augmented Evidence Graphs for Verifiable AI Reasoning in High-Stakes Domains
Training Large Language Models (LLMs) to be more objective by leveraging outside knowledge to create answers has shown to dramatically enhance their capabilities as Retrieval-Augmented Generation (RAG). But in most cases today, RAG systems are predominantly stateless and query evidence individually by query and do not store prior-proven knowledge, evidence provenance, history of contradiction and temporal integrity. These limits limit the reliability and accountability of high stakes sectors like health services, cybersecurity, money, lawful intelligence and compliance. This paper outlines a Memory- Augmented Evidence Graph (MAEG) system, which compiles Short-Term Evidence Memory (STEM) to empower active taskspecific reasoning with Long-Term Evidence Memory (LTEM), to empower persistent, provenanceaware organizational knowledge. The proposed architecture incorporates hybrid retrieval, evidence in the form of graphs, claim extraction, multi-stages persistent validation of evidence, contradiction detection, temporal consistency control and full audit log, to enable plausible and explainable AI reasoning. Key contributions include a two-layer evidence memory architecture, a provenance-backed evidence graph for persistent knowledge management, a verification pipeline to ensure only verified claims are stored, and temporal reasoning to detect evidence staleness or contradictions. Experimental outcomes on five benchmark datasets comprising 80,917 samples demonstrate that MAEG outperforms Vanilla RAG, Citation-based RAG, GraphRAG, RAPTOR, LightRAG, and HippoRAG, achieving 96.7% answer accuracy, 98.1% claim support, 97.5% evidence accuracy, and 96.8% contradiction detection. These results reveal that Long-Term Evidence memory is an important addition to reliability, transparency, and auditability of AI reasoning to enterprise decision-support systems.
Memory-Augmented Retrieval-Augmented Generation (RAG)
Enhancing Intelligent Systems through AI-Driven Predictive Analytics Secure API Management and Enterprise Cybersecurity Frameworks
Modern corporate environments rely on complex, hyper-connected digital ecosystems that are vulnerable to sophisticated, multi-stage cyber threats. This paper introduces an advanced structural framework designed to enhance modern Intelligent Systems by combining AI-driven predictive analytics, secure Application Programming Interface (API) management layers, and holistic enterprise cybersecurity frameworks. Traditional defensive infrastructures operate predominantly on reactive, signature-based detection mechanisms that fail to catch zero-day exploits, advanced persistent threats (APTs), and sophisticated API-based data exfiltration techniques. To overcome these defensive vulnerabilities, the proposed architecture establishes a proactive, closed-loop protection system that operates at machine speeds. The intelligence engine utilizes specialized anomaly detection algorithms and deep recurrent neural networks to continuously parse multi-layered telemetry logs, forecasting potential threat pathways and identifying malicious intents before a compromise occurs. This predictive layer coordinates directly with a hardened API management tier, which dynamically enforces contextual zero-trust access controls, automatically mutates payload validation rules, and throttles suspicious traffic. By embedding these secure operations into a unified enterprise cybersecurity framework, organizations achieve a self-defending digital ecosystem. This integration dramatically reduces structural response windows, safeguards distributed data pipelines, and ensures regulatory compliance without sacrificing the agility of core enterprise microservices.
Building Trusted Enterprise Ecosystems through Explainable Artificial Intelligence Secure Automation and Intelligent Decision Support
The rapid advancement of digital technologies has accelerated the adoption of artificial intelligence (AI), intelligent automation, and data-driven decision support systems across modern enterprises. While these technologies enhance operational efficiency, innovation, and competitiveness, concerns regarding transparency, security, accountability, and trust remain significant barriers to widespread adoption. Explainable Artificial Intelligence (XAI) has emerged as a critical approach for improving the interpretability of AI models, enabling organizations to understand, validate, and justify automated decisions. This study explores the development of trusted enterprise ecosystems by integrating explainable AI, secure automation, and intelligent decision support within scalable organizational frameworks. The research examines the technological foundations, implementation strategies, governance mechanisms, and organizational factors that contribute to trustworthy enterprise systems. A qualitative research methodology based on an extensive review of scholarly literature, industry reports, and conceptual frameworks is employed to identify best practices and implementation challenges. The findings indicate that transparency, cybersecurity, ethical governance, regulatory compliance, human oversight, and robust data management are fundamental components of trusted enterprise ecosystems. Secure automation enhances operational reliability while reducing human error, and intelligent decision support improves strategic planning through predictive analytics and real-time insights. The study concludes that integrating explainable AI with secure automation and intelligent decision support strengthens organizational trust, promotes responsible innovation, improves stakeholder confidence, and supports sustainable digital transformation in increasingly complex business environments