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.
Agentic AI Driven Enterprise Architecture for Secure Autonomous Decision Making and Continuous Compliance
The rapid advancement of artificial intelligence has transformed enterprise operations by enabling autonomous systems capable of making intelligent decisions with minimal human intervention. Agentic Artificial Intelligence (Agentic AI) represents a significant evolution beyond traditional AI by integrating autonomous reasoning, adaptive learning, goal-oriented planning, and continuous execution within enterprise environments. As organizations increasingly adopt digital transformation strategies, the integration of Agentic AI into Enterprise Architecture (EA) has emerged as a critical approach for improving operational efficiency, strengthening cybersecurity, enhancing governance, and ensuring continuous regulatory compliance. However, autonomous decision-making introduces challenges related to transparency, accountability, trust, data privacy, and regulatory adherence. This study explores the role of Agentic AI-driven Enterprise Architecture in facilitating secure autonomous decision-making while maintaining continuous compliance across complex organizational ecosystems. The research examines architectural frameworks, governance mechanisms, cybersecurity principles, compliance automation, and AI-driven risk management strategies that support resilient enterprise operations. A qualitative research methodology based on extensive literature analysis, industry frameworks, and comparative evaluation is employed to identify best practices and implementation challenges. The findings indicate that integrating intelligent autonomous agents with zero-trust security, explainable AI, continuous monitoring, policy automation, and governance frameworks significantly enhances enterprise resilience, operational agility, and regulatory compliance. The study concludes that Agentic AI-driven Enterprise Architecture provides a sustainable foundation for future intelligent enterprises operating in highly dynamic and regulated digital environments
Agentic AI Orchestrated Conversational Payment Pipelines with Drift-Aware Transaction
Chatbots and voice assistant applications are becoming a part of digital finance as a conversational payment system, but current systems are based on fixed piping and rule-based fraud detectors that fail to keep up with changing behavioral patterns in transactions and concept drift. The present paper suggests an Agentic AI-coordinated conversational payment pipeline with drift-aware transaction validation that is meant to enhance security, flexibility, and automation in financial transactions. The recommended structure uses a multi-agent approach, which includes conversational interface agent, orchestration agent, payment execution agent, and drift-sensitive validation engine. The validation module is based on adaptive machine learning model and behavioral drift technology capable of dynamically measuring transaction risk using user and contextual pattern changes. The framework effectively eliminates the shortcomings of the static validation framework by allowing free coordination of agents and ongoing detection of fraud through transaction streams, with low latency. Experimental analysis on synthetic datasets of payments proves to be better in detection and resistant to behavioral drift than standard rule-based and single-model techniques
A Secure, Zero-Trust Mobile Expert Locator for Global Professional Services Firms
A global professional services organization with over 50,000 employees who operate in diverse geographical locations, multiple types of businesses, and across many disciplines. An important operational issue for employees is their inability to quickly locate other global professional firm employees who have the skills or expertise they require. The traditional method for locating and finding co-workers is through directories and search tools that use on-premise databases. These tools are slow, fragmented, non-mobile, and do not meet the stringent data security requirements for on-premise data only. This paper presents the design, development, and performance characteristics of ExpertFinder, an enterprise-grade, zero-trust mobile expert locator. ExpertFinder is a zero-trust system that secures all sensitive employee information within the organization network and includes a mobile application developed using Microsoft .NET multi-platform app UI, which has native iOS capabilities and is supported by a decoupled .NET-based microservices architecture hosted on Microsoft Azure Web Apps . NET-based microservices architecture hosted on Microsoft Azure Web Apps. The system was tested and evaluated for six months across global professional firm worldwide employee base of more than 50,000. Test results showed an average response time of 1.42 seconds at the 95th percentile; API availability of 99.95%; a mobile crash rate of 0.32%; user adoption of 68% (34,000 active users); and a reduction in the time required to locate co-workers from an average of 8 minutes to 2.6 minutes. ExpertFinder passed all internal security audits and had no security breaches during the testing period. This case study will provide an architecture blueprint for developing zero-trust, highly scalable, and secure enterprise mobile applications in regulated industries where on-premise data security is required.
Architectural Frameworks for Intelligent Enterprises: AI Multi-Cloud Security Data Governance Compliance Automation and Business Process Optimization
The emergence of intelligent enterprises has accelerated the adoption of advanced architectural frameworks that integrate Artificial Intelligence (AI), multi-cloud security, data governance, compliance automation, and business process optimization. Organizations operating in highly competitive and data-intensive environments require scalable, secure, and intelligent systems capable of supporting digital transformation initiatives. AI technologies provide predictive analytics, intelligent automation, and enhanced decision-making capabilities, while multi-cloud architectures offer flexibility, resilience, and vendor independence. However, the increasing complexity of distributed environments introduces challenges related to security, governance, regulatory compliance, and operational efficiency. This study explores architectural frameworks that combine AI-driven technologies with multi-cloud ecosystems to create intelligent enterprise infrastructures. The research examines how data governance mechanisms ensure data quality, privacy, and accountability, while compliance automation reduces regulatory risks through continuous monitoring and policy enforcement. Business process optimization is enhanced through intelligent workflows, robotic process automation, and machine learning-driven analytics. The proposed framework emphasizes interoperability, security-by-design principles, and centralized governance across heterogeneous cloud environments. The findings indicate that integrated architectural frameworks significantly improve operational efficiency, cybersecurity posture, compliance management, and business agility. Despite implementation challenges such as complexity, cost, and organizational resistance, intelligent enterprise architectures provide a sustainable foundation for innovation and long-term competitiveness. The study concludes that the convergence of AI, multi-cloud security, governance, and process optimization is essential for future-ready enterprises.
Reinforcement Learning-Based Dynamic Load Assignment for Automated 3PL Tendering Systems
The use of third-party logistics (3PL) in freight transportation services attracts the issue of 3PL tender. In freight management, the submission of 3PL bids has been and can up to some degree continue to be a supplementary function of the technologies that have been deployed in the area industry. Consequently, companies have had to continue with the tedious routine of grading suppliers, marking a carrier with high marks yet, in real time, he is not available or he charges a high price. Therefore, this proposes an improvement of the existing 3PL system, namely, a reinforcement learning approach to dynamic cargo distribution, which has been experimented in the field. Such a method extends the possibilities of control of the 3PL system, allowing it to adapt to changing external requirements in the process of its functioning. The suggested development allows to improve 3PL services, making it more effective and efficient on the one hand, and operational on the other. The approach loads the agent with respect to an information space based on characteristics of the load, carrier rating, current market trends and accessibility of the transport network, to find the carrier assignment plan corresponding to the increase of aims achievement function. A 93.1% rate of first-tender acceptance (increased from 62.4%) has been verified in historical operational data received from Hub Group and this is further confirmed during its live deployment. It was also found that freight costs reduced by 14.2%, while manual intervention was reduced to just 8.3% of load assignments — a 91.7% reduction — with automation handling all standard freight loads while retaining human oversight for exceptions only. Therefore, based on the above results, the proposed dynamic load assignment system can successfully be deployed in the 3PL operations.
Automating Vulnerability Remediation: A Continuous SAST and FOSS Integration Framework for Production Support Pipelines
Financial businesses houses are increasingly relying on a range of critical foundational applications to support core operations. This has in turn given rise to a problem: How to regulate a growing number of bugs in the software, especially those generated by code written in-house. Application code is scanned through the help of Static Application Security Testing (SAST) software as well as free and open-source libraries having been compiled (not; researched) with the help of Free and Open-Source Software (FOSS) Releases This paper will focus on developing a working model for the incorporation of SAST and FOSS vulnerability as part of the application production support processes This study represents the development, execution and results of embedding a security automation project within day-to-day operational support business processes. The scope covers artifact scanning and risk-based bulleting, along with automated ticketing and principles of iterative feedback, for developer support and delivery, all of which maintains the operational continuity of the applications. The core improvements involve mitigation of vulnerabilities 40% faster on average; effectively reducing the number of ‘open’ vulnerability findings for high severity issues by 65%; and maintaining the integrity of the procedure with respect to in depth customer audits. The study outlines ideas on how organizations can employ a framework on continuous security hygiene especially in such environments
From Requirements to Production Managing Cross-Regional API Deployments at Capital One
The financial sector requires dependable, expandable, and safe API framework solutions that enable seamless communication between customer facing applications and wellness systems. This paper discusses in detail a case study on the design and implementation of the Enterprise Application Programming Interface (EAPI) framework inside the Runtime Container (RTM) at Capital One. The EAPI framework has been designed to support commercial and retail banking users by providing efficient communication throughout a multi-layered service model of Proxy, Restful, and Integration Services. This paper will discuss, in detail, the complete software development lifecycle of creating new APIs, including gathering requirements, managing Agile projects, developing and deploying those new APIs into production across all Capital One locations using a structured approach for version control; continuous integration; and rigorous testing phases including JUnit automation; as well as managing Integration Service (IS) and Oracle Service Bus (OSB) components. The paper will also provide details about using monitoring tools such as Splunk to monitor the reliability of the systems. The methodology presented in this case study has proven the value of using a structured approach for creating API frameworks at large organizations; thus, establishing a blueprint and demonstrating improved efficiency for deploying large enterprise-wide API Frameworks in regulated industries
A Zero-Defect High Sea Sale Automation Framework for Real-Time Ownership Transfer and Compliance in Maritime Trade Systems
Some of the most complicated legal documents in the international marine industry pertain to High Sea Sale (HSS) transactions. Packed with finance, compliance and legal documentation needs, including international one, each stage of the goods cannot move further until logistic, legal, and financial aspects are synchronized. This is done at the customs level before the cargo is mounted, loaded, signed, declared and transported to its final delivery point. The business environment is no longer tolerant of ‘time wasted’, ‘poor skills’, or ‘the money lost` excuses, or ‘a shrinking market.’ Walking away from this issue is easy, especially when very few presentation or critical demand, consumption or distribution of seismology or nature and culture of marketing for the millennial cobalt age, characteristic of the history of robust military upshifting, are not adequate. Traditional approaches at manually processing HSS are faced with a high likelihood of errors, exposed to high demurrage costs, and experience problems of congestion, thus hindering scale of activities worldwide In the following paper we will discuss a Zero- Defect High Sea Sale Automation Framework (ZD-HSSAF) that is specifically designed in and deployed in an Oracle R12 enterprise environment of the international logistics department of a global Pharma company – for high-sea practices. The framework involves a 4d dimension electronic declaration with a unique twist to the business rules for such transaction, a virtual high sea sale auto notification tool with gate logics which prevents legal acceptability being extended with issuance of letter of guarantee It is empirically proven that the application has improved in agreement processing performance i.e. (throughput) 1400% i.e. processing agreements from 1 per week to 15 per day since 99% of Closed defects were removed in the previous Releases, reached zero post-production tickets, and saved more than USD 1 million dollars annually through removal of port demurrage penalties. On the contrary, the reference provides for wide scale and coordinated use of artificial technical changes under time limits
Scaling National Wireless Services a Technical Case Study of T-Mobile's Middleware Evolution on AWS Cloud
With the surge of 5G technology, rapid growth of connected IoT devices, and increasing demand for real-time mobile service, telecom networks find themselves challenged with unprecedented levels of scalability. This case study focuses on the significant middleware modernization project completed by T-Mobile US, the largest nationwide wireless telecommunications carrier with annual revenue of over $40 billion, with millions of retail customers. As part of this initiative, T-Mobile's goal was to migrate their high-volume legacy integrations (Tibco) to a fast and scalable microservices based integration solution built on top of AWS (Amazon Web Services) using Java/J2EE. Some of the key technical contributions resulting from the modernization effort include Spring Boot scalable backend services developed using functional programming (Java 8) to eliminate complex business logic and reduce memory consumption, asynchronous messaging bus (AWS SQS/SNS) to decouple service dependencies, implementation of IAM (AWS) security framework to enforce least-privilege access controls, hibernate ORM with Oracle DB optimized data persistence using stored procedures and triggers for fast data retrieval, and automated CI/CD pipelines developed using Jenkins, Maven, and SonarQube with strict code quality levels. T-Mobile completed this project with a 30% improvement in backend processing speed, 99.99% service availability, a zero-downtime migration of active subscribers, significant reduction of licensing costs, and a rapid, repeatable, and scalable framework for other telecommunications providers and enterprise customers migrating from legacy to cloud based middleware.
Evolutionary Frameworks for Enterprise Resilience Financial Innovation and Cybersecurity Excellence Strategies
The contemporary business environment is characterized by rapid technological advancements, increasing financial complexity, and escalating cybersecurity threats. Organizations must continuously adapt to changing market conditions while maintaining operational stability and competitive advantage. Evolutionary frameworks provide a strategic approach for enterprises seeking to enhance resilience, promote financial innovation, and strengthen cybersecurity capabilities. These frameworks integrate adaptive technologies, intelligent decision-making systems, and dynamic governance mechanisms to support sustainable organizational growth. Enterprise resilience enables organizations to anticipate, respond to, and recover from disruptions, while financial innovation facilitates the development of new products, services, and business models that improve efficiency and value creation. Simultaneously, cybersecurity excellence ensures the protection of critical assets, information systems, and stakeholder trust in increasingly interconnected digital ecosystems. The convergence of these dimensions creates a comprehensive foundation for organizational excellence and long-term sustainability. This study explores the theoretical and practical significance of evolutionary frameworks in supporting enterprise resilience, financial innovation, and cybersecurity excellence strategies. Through an examination of contemporary research, technological developments, and organizational practices, the paper highlights how adaptive frameworks contribute to strategic agility, risk management, and innovation-driven transformation. The findings emphasize the importance of integrating resilience, financial innovation, and cybersecurity into a unified strategic approach capable of addressing emerging challenges and opportunities in the digital economy
Unified Customer Identity and Profile Architecture for Customer Enterprise Orchestration
Digital interaction ecosystems have created many architectural challenges for enterprises in terms of managing, synchronising and activating customer intelligence across multiple distributed systems and interaction channels. Traditional infrastructures for customer engagement tend to suffer from disjointed identity stores, disjointed behavioural data, and batch processing models that hinder scalability, slow down personalization responsiveness, and prevent coordinated delivery of the customer experience. In the age of real-time engagement strategies, there's a growing demand for scalable architectural patterns that allow enterprises to integrate identity resolution, profile computation, and orchestration across channels in a continuously adaptive digital ecosystem This paper proposes a common customer identity and customer profile architecture to meet the requirements of real-time enterprise orchestration by distributed identity resolution, streaming event processing, scalable customer profile computation and low latency activation frameworks. The proposed architecture is capable of a 94% identity match accuracy, achieving profile freshness latency of less than 2 minutes for batch window of 480 minutes as compared to the conventional batch-oriented models, and cross-channel consistency score – 54% to 91%. Implementation results, across telecommunications, retail, financial services and media, showed reductions in activation latency of between 60% and 74% and improvements in personalisation accuracy of between 76% and 92%. The framework delivers an extensible and privacy-enabled approach for enterprise-wide customer intelligence orchestration which can be used for support AI-assisted decisioning, predictive audience modelling and adaptive journey orchestration
Federated Feature Learning for Secure Cross-Organizational Data Science Workflows
In cross-organizational data science, the premises tend to increase prediction performance, which is given by data collection. Although there are restrictions on data sharing due to privacy, security, and regulatory considerations, federal learning may remove the limits that allow collaborative model training without centralizing data communication. The paper introduces a federal feature that uses learning as a secure technique to accept cross-organizational data in the science workflow. Therefore, establishing a federated future learning framework requires the organization to collaboratively learn common feature representations while maintaining people's data at the location level. In this context, the simulation results and enterprise-inspired scenarios typically demonstrate demonstrations of an approach that improves the utility model, maintains data security, and supports and secures inter-organizational cooperation. Furthermore, this study focuses on federated features that demonstrate learning rather than determining conventional federated model aggregation as a feature representation and as a kind of organizational foundations that enable downstream analytics across enterprises. In reality, by offering learning representations without revealing raw data, we will be proposing a strategy that is securing collaboration while also retaining the organization's autonomy and regulatory compliance. This paradigm is particularly well-suited to providing the enterprise environment with data heterogeneity, privacy constraints, and even competition limits that restrict centralized data exchange. The study will focus on federated features learning through the simulation process and enterprise inspiration scenarios, which provide a healthy and practical balance between predictive utility and data protection, resulting in viable solutions that are scalable and secure across organizational data science workflows.
Redefining Digital Inclusion through Connected Technology as a Solution (CTaaS) for Not-For-Profit Organizations
The rapid digitization of U.S. social, economic, and healthcare systems has made digital participation a structural requirement for equitable access to services, employment, and health. Yet Not-for-Profit (NFP) organizations the frontline delivery system for community-based digital inclusion programs face deep structural constraints: capital scarcity, understaffed teams, and an almost total absence of technical infrastructure. These constraints render the traditional asset-heavy technology model not merely inefficient, but fundamentally unsustainable. This paper introduces Connected Technology as a Solution (CTaaS) a managed-service paradigm governed by a closed-loop 6S Lifecycle Framework: Sourcing, Stocking, Staging, Supply, Support, and Sustainability. Drawing on real-world evidence from senior home-health initiatives (Project Silver Echo) and youth workforce programs (Project Horizon), we demonstrate that CTaaS reduces device abandonment from over 40% to below 5%, raises RPM data continuity to 98.5%, and measurably improves workforce readiness. By converting technology from a depreciating asset into a predictable operational subscription, CTaaS aligns with grant funding structures, reduces total cost of ownership, and produces a compelling social return on investment. The result is a national-scale impact model that is simultaneously more affordable, more effective, and more sustainable than any predecessor approach.
Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making
Engineering healthcare data infrastructures for predictive clinical analytics and evidence-based decision making is a complex task that affects several stakeholders and contributes to the growing health data ecosystem. Healthcare data infrastructures combine the people, processes, technologies, policies, standards, and products needed for the effective and efficient integration, sharing, and use of healthcare data. Modern healthcare relies heavily on data to support clinical analytics—computations that summarize, describe, or predict healthcare events—often on a large scale over aggregated population data. Clinical decisions are typically supported through evidence-based medicine, which urges decision-makers to rely chiefly on information from systematic reviews of randomized controlled trials and meta-analyses. However, reliable and timely predictive clinical analytics from trustworthy data still lack the same level of validation. Completeness, accuracy, timeliness, and safety bear a direct relation to the data lifecycle and the quality of the data used. Data sources must be known and governed, and appropriate procedures must regulate the collection and processing of the data throughout their life to ensure sufficient quality for the intended use. Privacy, confidentiality, security, compliance, and ethical aspects must also be adequately addressed in relation to current legislation, organizational policies, and recognized best practices.
Healthcare Data Infrastructure Engineering
Cloud Native Enterprise Intelligence through Autonomous AI and Predictive Analytics for Strategic Decision Making
The rapid digital transformation of modern enterprises has generated unprecedented volumes of data from cloud platforms, connected devices, business applications, and customer interactions. Organizations increasingly require intelligent systems capable of transforming this data into actionable insights that support strategic decision-making. Cloud-native enterprise intelligence, empowered by autonomous artificial intelligence (AI) and predictive analytics, has emerged as a transformative paradigm that enables enterprises to achieve agility, scalability, operational efficiency, and data-driven competitiveness. Autonomous AI systems leverage machine learning, deep learning, reinforcement learning, and intelligent automation to continuously monitor business environments, identify patterns, and make recommendations with minimal human intervention. Predictive analytics further enhances organizational capabilities by forecasting future trends, customer behaviors, operational risks, and market opportunities. Cloud-native architectures provide the computational elasticity, distributed processing, and real-time analytics necessary for implementing advanced AI-driven intelligence systems across enterprise ecosystems. This study explores the integration of cloud-native technologies, autonomous AI, and predictive analytics in enterprise intelligence frameworks for strategic decision-making. The discussion examines theoretical foundations, technological advancements, implementation approaches, and organizational implications. Through an extensive review of existing literature and a comprehensive methodological framework, the study highlights how intelligent cloud-native systems improve decision quality, accelerate innovation, optimize resource allocation, and strengthen competitive advantage. The findings suggest that enterprises adopting autonomous AI-powered predictive intelligence can achieve superior strategic outcomes while addressing challenges related to data governance, security, ethics, and organizational change management
MarketingMind: A Multi-Agent LLM Architecture for Federated Natural Language Querying Across Enterprise Marketing Data Systems
In enterprise marketing environments, there are diverse data ecosystems that encompass customer relationship management (CRM) applications, marketing automation tools, advertising networks, and enterprise data warehouses. One major remaining challenge is the capability to communicate with such systems using natural language interfaces, which is made difficult by the schema heterogeneities, data governance requirements across systems, and the tendency of large language models (LLMs) to hallucinate. MarketingMind is proposed as a multi-agent LLM architecture to handle federated natural language queries in these systems. The framework combines a ReAct-based orchestrator [14] with six dedicated agents responsible for schema discovery, natural language to SQL (NL-to-SQL) translation, hallucination mitigation, federated query routing, CRM analytics augmentation and response synthesis. Retrieval-augmented generation (RAG) [3] is used at various points in the pipeline to anchor the generation of queries to fact-checked schema information. A validation agent that uses self-reflection mechanisms [5] obtains a hallucination rate of 7.3%, whereas the hallucination rate is 38.4% in unconstrained LLM settings. The execution accuracy of the proposed architecture in the Spider benchmark is 91.3%, which is close to the human expert baseline of 94.5%. Federated queries are distributed across six enterprise data sources secured through OAuth 2.0 authentication [2] and column-level encryption, and cover 97.4% personally identifiable information (PII). The architecture is able to handle 138.2 queries per second (QPS) with 500 concurrent users, with a mean latency of 5.4 seconds. MarketingMind highlights that multi-agent orchestration paired with domain-specific RAG grounding and adaptive hallucination mitigation is a viable route towards enterprise-level natural language business intelligence for marketing analytics.
Cognitive Enterprise Ecosystems: Integrating AI Cloud Platforms Cybersecurity DevOps and Data Intelligence for Digital Transformation
Digital transformation has become a strategic imperative for organizations seeking competitiveness, innovation, resilience, and sustainable growth in an increasingly data-driven economy. The emergence of cognitive enterprise ecosystems represents a significant evolution in organizational design, integrating artificial intelligence (AI), cloud platforms, cybersecurity frameworks, DevOps practices, and data intelligence capabilities into a unified operational environment. These interconnected technologies enable organizations to automate decision-making, enhance agility, optimize resource utilization, strengthen security postures, and generate actionable insights from vast amounts of structured and unstructured data. The concept of a cognitive enterprise extends beyond technology adoption by emphasizing intelligent collaboration among systems, processes, and stakeholders. This essay examines the role of cognitive enterprise ecosystems in accelerating digital transformation and creating organizational value. It explores the theoretical foundations and practical implications of integrating AI, cloud computing, cybersecurity, DevOps, and data intelligence while highlighting their synergistic contributions to innovation and operational excellence. The discussion reviews existing literature on enterprise digitalization, identifies critical success factors and challenges, and proposes a comprehensive research methodology for investigating integrated digital ecosystems. Findings from contemporary research suggest that organizations that successfully align technological integration with strategic objectives achieve higher levels of adaptability, efficiency, customer satisfaction, and competitive advantage. Consequently, cognitive enterprise ecosystems are emerging as foundational architectures for future-ready organizations navigating complex and dynamic business environments
Unified AI Framework for Data Governance Fraud Prevention Quality Intelligence and Real-Time Transaction Monitoring in Modern Enterprises
The rapid expansion of digital ecosystems has significantly increased the volume, velocity, and complexity of enterprise data. Organizations face growing challenges in ensuring data governance, preventing fraudulent activities, maintaining data quality, and monitoring transactions in real time. Artificial Intelligence (AI) has emerged as a transformative technology capable of addressing these challenges through intelligent automation, predictive analytics, and continuous monitoring. This research proposes a Unified AI Framework that integrates data governance, fraud prevention, quality intelligence, and real-time transaction monitoring into a cohesive enterprise architecture. The framework leverages machine learning, deep learning, natural language processing, anomaly detection, and predictive analytics to create a centralized intelligence layer capable of supporting enterprise-wide decision-making. By combining governance policies, automated quality assessment, fraud detection mechanisms, and transaction monitoring systems, the proposed framework enhances operational efficiency, regulatory compliance, and organizational resilience. The study examines the architectural components, implementation strategies, and performance benefits associated with AI-driven enterprise governance systems. Furthermore, it evaluates how integrated AI capabilities improve risk management, data accuracy, customer trust, and business continuity. The findings indicate that a unified AI approach provides enterprises with a scalable, secure, and adaptive solution for managing modern digital operations while supporting strategic objectives and sustainable growth in highly competitive business environments.
Quantum Safe Public Key Infrastructure: Hybrid Classical PQC Certificate Chains and Migration Framework for Enterprise TLS
The imminent threat of cryptographically relevant quantum computers (CRQCs) to RSA and elliptic curve cryptography — combined with the 'harvest now, decrypt later' attack paradigm — necessitates an urgent enterprise PKI migration to post quantum cryptographic (PQC) algorithms. This paper presents a comprehensive hybrid PKI framework enabling phased, backward compatible migration from classical to post quantum certificate chains. The framework embeds dual signatures (ECDSA + CRYSTALS Dilithium3) within X.509v3 certificates following the emerging RFC 9480 composite certificate standard. We provide Algorithm 2 (HybridCertIssue) specifying the complete certificate generation and verification protocol, and a migration roadmap structured across five phases from 2021 to 2035+. Experimental evaluation on a 10,000 node enterprise PKI demonstrates: TLS handshake overhead of +4.6 KB, latency increase of +38ms (LAN p50), and Dilithium3 signing throughput of 800 ops/sec on FIPS 140 3 HSMs. Security proofs establish that the hybrid scheme is secure if either the classical or PQC scheme is unforgeable — requiring an adversary to simultaneously break both ECDSA and Dilithium3. The migration cost model scales O(n log n) for n node PKI hierarchies. A 30 organization survey quantifies migration readiness: only 41% have completed cryptographic asset inventory, representing the critical first blocker.
Adaptive SAP Security Control Framework for ML Driven Anomaly Detection, Role Based Access Hardening, and Continuous Compliance Monitoring in SAP S/4HANA Environments
Enterprise SAP systems — deployed across more than 440,000 organisations globally — present a uniquely high value and complex attack surface. The convergence of centralised financial data, deeply integrated business processes, and historically under monitored application layer controls has made SAP environments a primary target for advanced persistent threats, insider fraud, and supply chain compromise. This paper presents the Adaptive SAP Control Security Framework (ASCSF), an integrated security architecture combining intelligent access control analysis, machine learning based anomaly detection, and continuous compliance monitoring for SAP S/4HANA 2020 environments. The framework's two principal algorithms — Algorithm 1 (ASCSF: composite user risk scoring from SoD conflict analysis, authorisation object evaluation, critical access detection, and behavioural modelling) and Algorithm 2 (SSADE: an Isolation Forest + BiLSTM + Autoencoder ensemble for security audit log anomaly detection achieving AUC 0.978) — provide real time, adaptive security coverage across all five layers of the proposed SAP security taxonomy. Experimental evaluation on 18 months of production SAP S/4HANA data spanning 4.9 million security events, 15,284 users, and three industry sectors demonstrate: 98.1% anomaly detection rate, 0.9% false positive rate, 2.1 second mean detection latency (9,143x improvement over weekly manual review), and a 67% reduction in security incidents post deployment. Cross comparison against seven baseline systems including SAP Enterprise Threat Detection, SecurityBridge, and Splunk SIEM confirms statistically significant superiority across all metrics (McNemar's test, p < 0.001). Validation on the SAP Security Baseline 2021 hardening checklist demonstrates 31 percentage point improvement in patch compliance over six months.
Next-Generation Intelligent Enterprise Architecture for Generative AI Secure Cloud Computing and Business Optimization
The rapid evolution of artificial intelligence, particularly generative AI models, has fundamentally reshaped the design and operational paradigms of enterprise architecture. Organizations are transitioning from traditional centralized IT infrastructures to next-generation intelligent enterprise architectures that integrate generative AI, secure cloud computing, and advanced business optimization frameworks. These architectures are designed to enable autonomous decision-making, predictive intelligence, adaptive workflows, and scalable digital ecosystems that respond dynamically to complex business environments. Generative AI systems, including large language models and multimodal foundation models, are now embedded within enterprise platforms to automate knowledge work, enhance customer engagement, optimize supply chains, and improve strategic forecasting. However, their integration introduces significant challenges in terms of data security, model governance, regulatory compliance, latency management, and ethical deployment. Secure cloud computing serves as the backbone of this transformation, offering elastic infrastructure, distributed processing capabilities, and integrated cybersecurity frameworks that ensure resilience and confidentiality. The convergence of generative AI and secure cloud ecosystems enables enterprises to move toward self-optimizing digital infrastructures where data, intelligence, and operations are continuously synchronized. Business optimization in this context is no longer limited to static analytical dashboards but evolves into real-time, AI-driven decision intelligence systems that continuously refine business processes. This essay explores the conceptual and methodological foundations of next-generation intelligent enterprise architecture, emphasizing the synergy between generative AI, secure cloud computing, and enterprise optimization strategies.
AI-Driven Autonomous Cloud Operations: A Framework for Intelligent Infrastructure Management
The increasing complexity of cloud computing environments has created significant challenges in managing infrastructure performance, availability, security, and resource optimization. Traditional cloud operations rely heavily on manual monitoring, rule-based automation, and human intervention, which often struggle to keep pace with dynamic and large-scale cloud ecosystems. AI-Driven Autonomous Cloud Operations (AIOps) has emerged as a transformative approach for intelligent infrastructure management by integrating artificial intelligence, machine learning, predictive analytics, and automation into cloud operations. This research explores a framework for autonomous cloud operations that enables self-monitoring, self-healing, self-optimization, and self-governing capabilities within modern cloud infrastructures. The framework leverages real-time data analytics, anomaly detection, predictive maintenance, intelligent orchestration, and automated decision-making to improve operational efficiency and system reliability. Cloud-native technologies such as containers, microservices, orchestration platforms, and distributed computing environments further enhance the scalability and effectiveness of autonomous operations. The study examines the architectural components, implementation strategies, governance mechanisms, and performance benefits associated with AI-driven cloud management systems. Findings indicate that autonomous cloud operations significantly reduce operational costs, improve service availability, optimize resource utilization, and strengthen infrastructure resilience. The research concludes that AI-driven intelligent infrastructure management will become a foundational element of future cloud ecosystems, enabling organizations to achieve greater agility, scalability, and operational excellence.
Explainable Artificial Intelligence and Adaptive Risk Governance for Next-Generation Digital Enterprises
The rapid digitalization of enterprises has transformed organizational operations, decision-making processes, and competitive strategies through the extensive adoption of artificial intelligence, cloud computing, big data analytics, and intelligent automation. While these technologies enhance efficiency, innovation, and scalability, they also introduce significant challenges related to transparency, accountability, ethics, cybersecurity, compliance, and risk management. Traditional governance models often struggle to address the complexity and dynamic nature of AI-driven environments, creating a need for adaptive governance mechanisms capable of responding to evolving technological and regulatory landscapes. Explainable Artificial Intelligence (XAI) has emerged as a critical approach for improving the transparency and interpretability of AI systems, enabling stakeholders to understand, trust, and effectively govern algorithmic decisions. This study explores the integration of Explainable Artificial Intelligence and Adaptive Risk Governance as a strategic framework for next-generation digital enterprises. The proposed perspective emphasizes the importance of combining explainability, continuous monitoring, intelligent risk assessment, regulatory compliance, and organizational resilience to support responsible AI adoption. By integrating explainable models with adaptive governance structures, enterprises can improve decision quality, strengthen stakeholder trust, enhance accountability, and mitigate emerging risks. The study contributes to the growing discourse on digital governance by proposing a comprehensive approach that aligns technological innovation with ethical principles, regulatory requirements, and strategic organizational objectives in increasingly complex digital ecosystems
Next Generation Enterprise Ecosystems Powered by Predictive Intelligence and Cloud Native Innovation
The integration of these technologies creates agile enterprise environments that support continuous innovation, automation, and digital transformation. Organizations adopting predictive intelligence and cloud-native solutions gain competitive advantages through improved resource utilization, accelerated product development, enhanced cybersecurity, and data-driven governance. However, challenges such as data privacy concerns, integration complexity, skill shortages, and regulatory compliance must be addressed for successful implementation. This study examines the evolution of enterprise ecosystems, reviews existing literature on predictive intelligence and cloud-native technologies, and proposes a research methodology to evaluate their impact on organizational performance. The findings contribute to understanding how intelligent and cloud-driven ecosystems can shape sustainable business growth, innovation, and resilience in the digital economy
AI-Driven Data Integration for Mergers and Acquisitions: Automating Entity Resolution and System Consolidation
Mergers and acquisitions (M&A) are critical strategic initiatives that enable organizations to expand market presence, achieve operational synergies, and enhance competitive advantage. However, one of the most complex and time-consuming challenges in M&A execution is the integration of heterogeneous data systems across merging entities. Traditional data integration approaches often rely on manual processes, rule-based matching, and rigid transformation pipelines, which are insufficient to handle the scale, diversity, and ambiguity of modern enterprise data landscapes This article explores the role of Artificial Intelligence (AI) in transforming data integration during M&A, with a specific focus on automating entity resolution and system consolidation. AI-driven techniques such as machine learning-based record linkage, natural language processing (NLP), and graph-based data modeling enable organizations to accurately identify, match, and unify entities across disparate systems. These approaches significantly reduce data duplication, improve data quality, and accelerate integration timelines Furthermore, the paper examines architectural strategies for implementing AI-driven data integration, including the use of data lakes, integration platforms, and metadata-driven pipelines. It highlights how intelligent automation can streamline schema mapping, data transformation, and system harmonization across legacy and modern platforms. Real-world scenarios and conceptual models are discussed to demonstrate how enterprises can achieve scalable, resilient, and efficient integration outcomes The study concludes that AI-driven data integration is not merely an enhancement but a necessity for successful M&A execution in the digital era. Organizations that adopt intelligent integration frameworks can significantly reduce risk, lower operational costs, and unlock faster time-to-value from their mergers and acquisitions
AI IN CONSUMER DIGITAL BANKING: ENABLING SMART PERSONALIZATION AND FRAUD DETECTION
The rapid digital transformation of the banking sector has led to a paradigm shift in how financial institutions engage with consumers. Artificial Intelligence (AI) has emerged as a critical enabler, driving both operational efficiency and enhanced customer experiences. This paper explores the dual application of AI in consumer digital banking: smart personalization and fraud detection. On one front, AI-powered personalization tailors services, recommendations, and interfaces based on user behavior, transaction history, and preferences, thereby fostering deeper customer engagement and loyalty. On the other front, advanced AI algorithms detect fraudulent activities in real time by identifying anomalous patterns and behaviors across massive data streams—significantly reducing financial crime and loss. This research analyzes current AI methodologies including machine learning, deep learning, and natural language processing within the context of banking applications. Through a combination of literature review, market data analysis, and illustrative case studies, we present key findings that highlight the transformative impact of AI on both personalization and fraud management. Quantitative data supports the effectiveness of AI models, demonstrating improvements such as a 30–50% increase in customer satisfaction and up to a 90% reduction in false positives in fraud alerts. Moreover, the paper discusses the challenges of AI implementation in digital banking, including data privacy, ethical concerns, regulatory compliance, and model interpretability. The study concludes by offering strategic insights into the future of AI in banking, including the integration of federated learning, explainable AI, and generative models for more intuitive customer experiences. By synthesizing technical, operational, and ethical considerations, this paper aims to provide a comprehensive framework for financial institutions seeking to responsibly harness AI for enhanced consumer trust and digital innovation.
AI-DRIVEN CLOUD COST OPTIMIZATION STRATEGIES FOR LARGE-SCALE MULTI-REGION INFRASTRUCTURE PLATFORM
Cloud computing has revolutionized how organizations develop and operate their applications based on the scalable and adaptable infrastructure services. Nevertheless, as large-scale multi-region cloud architectures are becoming more and more popular, the operational costs have become a significant issue to manage. Such environments entail an unstable workload, a geographically distributed resource base, and sophisticated pricing models, which tend to result in poor use of resources and high spending. AI technology is essential in helping to overcome these challenges because it can be used to perform predictive analytics, automated decision-making, and ongoing optimization. The paper will conduct a detailed theoretical discussion of the AI-driven cloud cost optimization, including all the key concepts and models, algorithms and practical strategies. In and real world research results of cost savings as high as 40 percent and service level agreements (SLAs).
AI-POWERED OPERATIONAL INTELLIGENCE FOR MANAGING HIGH-SCALE CLOUD-NATIVE DISTRIBUTED SYSTEMS
The explosion in cloud-native architectures and microservice-based distributed systems has brought about a level of complexity that cannot be effectively handled using traditional monitoring systems and manual incident handling strategies. This paper introduces a novel operational intelligence framework, based on the artificial intelligence, streaming, and auto-remediation facilities, to manage the high-scale cloud-native distributed systems. It comprises anomaly detection, predictive analysis, smart alerting, and auto-remediation. By using the data produced by containers, service mesh technologies, and cloud infrastructure, the system can deliver operational intelligence by employing machine learning, stream processing, and autonomous actions, and minimal human interaction is necessary. The proposed system architecture is vendor-neutral and extensible, which allows it to be compatible with popular observability products on the market. The paper addresses the system architecture, data flows, AI/ML model development, and deployment issues, putting a particular focus on how operational intelligence is applied in DevOps and SRE.
INTEGRATING AI-DRIVEN AUTOSCALING MECHANISMS IN KUBERNETES-BASED MICROSERVICES ARCHITECTURES
The increasing popularity of cloud-native software has led to the need for improved efficiency in managing resources in containerized environments. The fact that Kubernetes is a de-facto solution when it comes to orchestrating workloads of containers offers inherent autoscaling mechanisms in the shape of Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). However, such solutions do not cope well with the dynamics and burstiness of modern micro service-oriented architectures. This paper discusses how a brand-new solution combining Kubernetes- based autoscaling with smart prediction based on algorithms developed by Artificial Intelligence and Machine Learning can be applied. In particular, we will dwell on the combination of Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA) models, and Reinforcement Learning (RL) to predict the next workload peaks. The given approach will be implemented and tested in a sample microservices environment that consists of auth, order, and payment services. We shall consider the technical design of the approach, its challenges and potential integration possibilities. Early findings indicate that the proposed architecture performs better as compared to the traditional reactive autoscalers and enables the implementation of higher SLO compliance levels and reduction of the risks of latency peaks. We conclude by outlining further research directions.
NeuroSeg Decoding the Brain a Multimodal Intelligence Framework for MRI Brain Tissue Segmentation Methods Machines and Medicine
Magnetic Resonance Imaging (MRI) brain segmentation is a cornerstone of neuroimaging analysis, enabling clinicians and researchers to precisely delineate white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compartments. This comprehensive review spans classical intensity-based methods (thresholding, k-means, fuzzy C-means), probabilistic atlas-guided frameworks (Expectation-Maximization, Gaussian Mixture Models), deformable surface models (active contours, level sets), and contemporary deep learning architectures including 3D U-Net, nnU-Net, and Swin-UNet transformers. Essential preprocessing stages — bias field correction (N4ITK), skull stripping (BET, HD-BET), and multimodal image registration (ANTs SyN) — are examined with original synthesised MRI visualisations illustrating each stage. The partial volume effect (PVE) and its correction strategies are discussed in detail. Validation methodologies using BrainWeb phantoms and the IBSR real-data repository are described alongside Dice similarity and Jaccard index benchmarks. Emerging frontiers including federated learning for privacy-preserving multi-site studies, uncertainty quantification, self-supervised pretraining, and neuroimaging foundation models are critically reviewed. This article serves as a structured reference for clinical researchers, biomedical engineers, and AI practitioners engaged in brain MRI analysis.