International Journal of AI, BigData, Computational and Management Studies
Secure and Zero-Trust Middleware Architectures for Real-Time Enterprise Data Exchange
Modern enterprise systems are no longer confined to a single data center or a predictable network boundary. They sprawl across hybrid cloud platforms, edge nodes, and SaaS (Software as a Service) ecosystems, stitched together by middleware that moves sensitive data at high speed and high volume. For years, securing that middleware meant securing what surrounded it firewalls, API gateways, and identity brokers positioned at the perimeter. That model has not held up well. When an attacker or a compromised internal service is already inside the integration pipeline, perimeter controls offer little protection. Zero-Trust Architecture, formalized through NIST guidance, rejects the idea that network location confers trust and demands continuous verification of every transaction, every time. Bringing that principle into the middleware runtime itself rather than delegating it to external controls is the central challenge this article addresses. The Zero-Trust Middleware Architecture explained here includes features like checking who is sending messages, evaluating rules based on context, giving permission for each transaction, encrypting messages from start to finish, and monitoring. The practical domains where this matters most include financial settlement platforms, cross-institutional healthcare data exchange, enterprise API ecosystems, and any regulated environment where real-time data movement cannot be separated from real-time security enforcement. Rather than treating Zero-Trust as a network-layer concern, this framework extends it into the integration fabric where enterprise data actually flows.
Social Media Bot Fraud and Automated Abuse: Detection, Risk Scoring, and Real-Time Mitigation Frameworks
The exponential growth of social media platforms has transformed global communication while simultaneously expanding the digital attack surface for malicious activities. Modern platforms that rely on user-generated content and engagement-driven algorithms have become increasingly vulnerable to bot-driven fraud, coordinated manipulation, and impersonation attacks. These threats undermine information integrity, distort public discourse, and expose users to phishing, spam, and large-scale abuse. This study presents a comprehensive detection framework that integrates multi-dimensional feature engineering with hybrid machine learning and graph-based analytics to address the evolving complexity of automated attacks. The proposed approach captures account-level attributes, behavioral patterns, content characteristics, and network interactions to distinguish between legitimate users and coordinated malicious entities. In addition, a dynamic behavioral risk scoring mechanism is introduced to evaluate the likelihood of automation and malicious intent in real time, enabling adaptive and prioritized response strategies. Temporal analysis is incorporated to identify synchronized activities and burst patterns commonly associated with coordinated bot campaigns. Experimental evaluation demonstrates improved detection accuracy and enhanced visibility into coordinated attack structures compared to traditional approaches. The findings highlight the effectiveness of combining behavioral analytics with network intelligence in identifying sophisticated bot activities. The proposed framework offers a scalable and explainable solution for modern content moderation systems, supporting timely intervention while maintaining transparency and fairness in automated decision-making processes.
Distributed RAG Architecture for Enterprise Knowledge QA on Databricks
Organizations rely on centralized knowledge repositories like Salesforce Knowledge to support internal teams and external teams. The traditional keyword/rule-based search often doesn’t provide accurate, up-to-date information at scale. This paper presents a unified data-centric and model-centric Retrieval-Augmented Generation (RAG) architecture implemented on Databricks using Apache Spark. The architecture integrates large-scale ETL processing with efficient model execution for vector embedding generation within a single distributed framework. The proposed solution employs open and modular design with separate components for data extraction, data cleansing, workflow orchestration, embedding generation, vector retrieval and LLM inference. In the proposed approach, knowledge articles are extracted by executing Salesforce Object Query Language (SOQL) queries against a Salesforce Knowledge object REST API endpoint and stored in a Databricks Lakehouse. The RAG pipeline employs stateful model reuse and fine-grained resource allocation coupled with concurrent multithreaded encoding within Databricks to perform large-scale vector embedding generation. The generated vector embeddings from the extracted content are stored in a vector database. At inference time, user queries from the chatbot are converted into vectors and matched against the vector database to find most relevant information chunks which are then sent along with prompts to an LLM to generate responses. This design ensures that answers are traceable to authoritative knowledge sources, reduces hallucinations, and supports continuous updates as knowledge articles evolve.The RAG pipeline implemented on a Databricks cluster demonstrated 100% CPU utilization with high execution efficiency on a vector embedding pipeline as shown in the experimentation. The proposed architecture provides fine-grained control over resource allocation, concurrency, and batching strategies, enabling enterprises to generate high quality embeddings while leveraging existing infrastructure without significant redesign.
AI-Driven Real-Time Decision Support in Arthroscopic Procedures Using Computer Vision
The clinical efficacy of arthroscopic repair remains heavily dependent on a surgeon's spatial orientation and real-time interpretation of constrained visual fields. While intraoperative assistance is evolving, current platforms often struggle with high-latency processing and poor anatomical differentiation. We developed a high-performance computer vision framework that synchronizes anatomical segmentation with instrument tracking to provide instantaneous surgical guidance. By deploying hybrid CNN-Transformer architecture via edge computing, the system achieves sub-30ms latency meeting the strict requirements for fluid, real-time feedback in the operating room. Our evaluation across multi-institutional datasets shows a significant reduction in procedural deviation and improved accuracy in identifying critical structures like ligaments and meniscal boundaries.
Integrating Generative AI with Real-Time Data Pipelines for Operational Decision Intelligence
This paper presents a comprehensive conceptual framework for integrating generative artificial intelligence (AI) with real-time data pipelines to enable operational decision intelligence. As organizations increasingly adopt AI-driven solutions, the ability to leverage real-time data streams with generative AI models becomes crucial for timely, informed decision-making. We propose a multi-layered architecture that encompasses data ingestion, AI processing, and decision intelligence generation, complemented by continuous feedback loops for model improvement. The framework addresses key challenges including latency management, model deployment strategies, and ethical considerations in AI-driven operations. Through examination of existing literature and emerging best practices, we demonstrate how this integration can enhance operational agility and decision quality across diverse domains. The paper discusses implementation considerations, trade-offs between performance metrics, and governance requirements. Finally, we outline future research directions including advanced interpretability methods, federated learning approaches, and multi-model orchestration strategies. This work contributes to the growing body of knowledge on AI systems architecture and provides practitioners with actionable insights for building intelligent operational systems.
Governance-in-the-Loop: Runtime Policy Enforcement for Autonomous and Distributed AI Systems
AI governance mechanisms today are predominantly procedural. Documentation standards, audits, and risk assessments improve transparency but do not constrain runtime behavior. As AI systems evolve into autonomous, distributed platforms that invoke tools, spawn sub-agents, and operate across services, governance violations manifest as execution events rather than documentation failures. This structural mismatch prevents existing approaches from providing enforceable guarantees. We introduce Governance-in-the-Loop (GiL), a runtime architecture that embeds non-bypassable policy enforcement directly into AI execution primitives. GiL integrates Governance Enforcement Points (GEPs) into schedulers, model invocation paths, and inter-service communication layers. Policies support three outcomes: permit, deny, and modify – unlike deny, which cascades into workflow failure, modify preserves system availability by transforming the action into a policy-compliant alternative before execution. Each decision is bound to a verifiable audit artifact. We formalize governance as a complete mediation problem over distributed execution traces, define enforcement invariants, formally argue two core safety properties, and demonstrate differentiation from existing policy enforcement systems. The central argument is that enforceable AI governance requires architectural embedding, not procedural overlay.
Evaluating the Role of Contact Center Applications in Enhancing Customer Experience in Banks
In the digital banking era, customer experience (CX) has become a primary determinant of competitive differentiation and long-term profitability. Contact center applications have evolved from operational support units into strategic customer engagement platforms integrating artificial intelligence (AI), omnichannel communication, analytics, and core banking systems. This study evaluates the role of contact center technological capability in enhancing customer experience and customer loyalty in banking institutions. Drawing upon Service Quality Theory, the Technology Acceptance Model, and the Resource-Based View, the paper proposes and develops a structural equation model (SEM) linking technological capability to operational efficiency, service quality, customer experience, and customer loyalty. Human-centric system architecture is presented to illustrate technological integration across layers. The study contributes theoretically by positioning contact center capability as a strategic digital resource and empirically by proposing measurable constructs suitable for Q1/Q2 journal evaluation. Managerial implications emphasize strategic IT investment alignment for sustainable competitive advantage.
Architecting Large-Scale Identity Governance Frameworks for Zero Trust Enterprises
Identity governance has emerged as the foundational control plane of modern enterprise security. As organizations increasingly operate across hybrid and multi-cloud environments, traditional perimeter-based security models have proven inadequate. This paper examines the architectural principles, design patterns, and operational frameworks required to implement large-scale Identity Governance and Administration (IGA) within a Zero Trust paradigm. We analyze the convergence of identity lifecycle management, least-privilege enforcement, continuous verification, and AI-augmented risk intelligence. Drawing on current industry frameworks including NIST SP 800-207 and NIST SP 800-63 [8], we propose a five-layer reference architecture for scalable, policy-driven identity governance that spans human and non-human identities across enterprise-scale deployments. Empirical data from industry research underscores the urgency of this transition and informs our proposed design guidance.
QoS aware routing in Software Defined Networks
The growing demand for bandwidth-intensive applications such as video streaming, multimedia services, and Internet of Things (IoT) deployments has intensified the need for efficient resource management that secures network operations without compromising Quality of Service (QoS). Addressing these challenges requires a comprehensive and centralized view of network resources. Software Defined Networking (SDN), as an emerging paradigm, offers centralized control and programmability, allowing administrators to dynamically manage network behavior and optimize routing. This paper introduces a QoS-aware routing scheme for SDN that considers parameters such as available bandwidth, packet delay, and packet loss to determine the most efficient routing paths. Optimal paths are selected based on predefined threshold criteria to meet users’ QoS expectations. Furthermore, we explore the integration of SDN with the Service-Oriented Architecture (SOA) model, emphasizing a user-centric approach to traffic engineering and Quality of Experience (QoE) enhancement. QoS guarantees remain essential for optimizing performance metrics and ensuring reliability, especially under constrained network conditions. Controllers dynamically identify optimal routes to meet clients’ core QoS requirements, highlighting QoS-aware delivery as a cornerstone for next-generation network design.
Modernizing Anti-Money Laundering (AML) Data Pipelines Using Cloud-Native Architectures on Azure and Databricks
Financial institutions must analyze large volumes of transactional data while complying with strict regulatory requirements aimed at preventing financial crime. Many existing Anti-Money Laundering (AML) systems rely on legacy infrastructures that use batch-oriented ETL pipelines and static rule-based monitoring, resulting in limited scalability and delayed fraud detection. This paper proposes a cloud-native AML data pipeline architecture implemented using Microsoft Azure and Azure Databricks. The framework integrates real-time data streaming, scalable data lake storage, and machine learning–based analytics to detect suspicious financial behavior. Data governance, lineage tracking, and automated compliance checks are embedded within the pipeline. The proposed approach enhances processing throughput, improves analytical accuracy, and enables financial institutions to respond more effectively to emerging financial crime patterns.
Clean Before Predict: A Governance-First Methodology for High-Stakes AI Systems
Artificial intelligence (AI) systems, especially high-stakes ones, particularly in clinical domains, require not only predictive accuracy but also robustness, fairness and reliability. Conventional machine learning pipelines are mainly concerned with optimization of prediction, and usually fail to consider data quality, bias and risk-related matters that may result in unsafe or unreliable results. To overcome this shortcoming, this paper suggests a Governance-First, Clean-Before-Predict (CFP) model that re-organizes the traditional pipeline by imposing data cleaning and governance limitations before model training. The suggested methodology includes the four phases of data cleaning and quality checks, implementation of governance according to fairness and compliance indicators, risk-sensitive predictive modeling, and overall assessment. The experiments on the MIMIC-III clinical data with Logistic Regression, Random Forest and XGBoost show that CFP framework can be as effective as baseline models in terms of Accuracy, F1-score, and ROC-AUC, with the added benefit of increasing data reliability and decreasing the impact of noisy and biased samples. It is worth noting that XGBoost performs better in CFP setting. These findings suggest that the suggested solution increases stability and reliability without affecting the predictive accuracy of the tool considerably, which is why it can be used in high-stakes AI systems.
A Hybrid Push–Pull Notification Model for Maritime Android Devices with Intermittent Satellite Links
Android applications deployed in maritime environments such as cruise ships, offshore vessels, and research platforms operate under fundamentally different network constraints than terrestrial mobile systems. Connectivity is typically provided via satellite links characterized by high latency, limited bandwidth, intermittent availability, and asymmetric up-link–downlink behavior. Traditional mobile notification systems, which rely heavily on continuous push-based delivery models, degrade severely under these conditions, resulting in delayed, dropped, or inconsistent notification delivery. This paper introduces a Hybrid Push–Pull Notification Model (HPPNM) designed specifically for Android devices operating over intermittent satellite links. The proposed model combines opportunistic push delivery during periods of link availability with adaptive pull-based synchronization during offline or de-graded connectivity windows. By decoupling notification intent from immediate delivery and dynamically switching between push and pull modes based on network conditions, HPPNM ensures reliable, timely, and bandwidth-efficient notification de-livery in maritime environments. The framework operates entirely at the application layer and integrates with Android notification APIs, local persistence, lifecycle-aware execution, and constrained network schedulers. Through architectural design and experimental evaluation un-der simulated satellite conditions, this paper demonstrates that HPPNM significantly improves notification reliability, reduces bandwidth waste, and preserves user trust compared to push-only approaches.
A Data-Driven Framework for Intelligent Procurement Decision-Making Using Machine Learning and Predictive Analytics in Global Supply Chain Networks
Global supply chains are concurrently facing mounting demands, uncertain suppliers, price fluctuations, and low real-time visibility in procurement, making this a major challenge that leads to lack of efficiency in young decisions and an increasing cost of doing business. Conventional systems, which are based on rules, are reactive, and do not use the increasing amount of enterprise and external data to provide predictive information. The paper suggests a machine learning (ML)-based predictive analytics-powered framework to make intelligent procurement decisions. This framework has a modular structure that includes the data ingestion, preprocessing and feature engineering, and a decision engine powered by machine learning. It uses regression and ensemble cost and demand forecasts, and time-series models like LSTM and ARIMA to capture time variation. Supplier evaluation is done using multi-criteria decision-making (MCDM). Experimental findings on real and simulated data show that the accuracy of forecasting, cost reduction in procurement and selection of suppliers are better than traditional methods. The suggested framework opens the prospects of decisions that are proactive and real-time as well as enhances resilience in the supply chain. All in all, the study provides a scalable and extending answer to data-driven procurement to aid greater efficiency and strategic decision-making in intricate global supply chain settings.
Performance Evaluation and Testing Optimization Techniques for Cloud-Native Systems in Edge-Cloud Continuum
The concept of cloud-native systems has transformed the manner in which application is deployed today utilizing microservices, containerization, and orchestration frameworks to provide scalability, resilience and agility. As the edge-cloud continuum rapidly emerges, with the computational resources being spread across centralized cloud data centers and decentralized edge nodes, new issues emerge in performance evaluation and testing optimization. The paper provides a detailed analysis of the performance assessment strategies and testing optimization tricks that are designed to suit cloud-native systems working in a heterogeneous edge-cloud setup. The edge-cloud continuum presents latency, bandwidth, resource availability, and workload distribution variability dynamically, thus requiring conventional performance testing methodologies to be inadequate. The study examines the innovative testing systems that integrate real-time telemetry, distributed tracing, and predictive analytics powered by AI in order to improve performance visibility. The paper also highlights the significance of workload modelling, chaos engineering and adaptive testing pipelines in the reliability of systems in different operational contexts. The performance metrics, including response time, throughput, resource utilization, and fault tolerance are analyzed in a comprehensive way. The paper offers a hybrid testing architecture that brings continuous testing practices and edge-aware optimization strategies together. Experimental evidence shows that optimized testing pipelines can enhance the efficiency of system performance up to 35 percent and reduce the latency by 20 percent in edge-intensive workloads. Moreover, this paper identifies how container orchestration platforms facilitate effective resource distribution and scaling processes at edge and cloud layers. The suggested methodology includes automated test case generation, dynamic provisioning of resources and optimization loops run by feedback. The results imply that the combination of performance testing and intelligent optimization methods can greatly boost the resilience and performance of cloud-native systems in the distributed context. This study is also an addition to the emerging research in the area of edge computing since it offers a systematic approach to assessing and optimizing performance in the next generation of distributed systems.
Architecting Autonomous Testing Pipelines for Cloud-Native Systems Using AI-Driven Fault Injection and Predictive Analytics
The rapid adoption of cloud-native architectures, characterized by microservices, containerization, and dynamic orchestration, has significantly increased the complexity of software systems. Conventional methods of testing do not always suffice to tackle the issues related to distributed environments, scaling dynamically and deploying continuously. In this paper, the author introduces an autonomous testing pipeline architecture which combines AI-based fault injection and predictive analytics to promote the reliability, resilience, and performance of a cloud-native system. The suggested model establishes a smart fault injection engine that can emulate realistic and adaptive failure modes according to past system dynamics and run-time environment. Simultaneously, a predictive analytics engine is used to predict possible failures, high-risk components, and prioritize test execution using machine learning models. These modules are easily incorporated in CI/CD pipelines by the use of a centralized test orchestrator that facilitates the execution of continuous, automated, and proactive tests in dynamic settings. Also, the architecture includes a monitoring and observability layer and a feedback and learning module that creates a closed-loop system that recursively optimizes testing strategies and promotes self-healing. Experimental evidence shows that it has a large improvement on fault-detection rates, prediction accuracy, and system resilience, without incurring a large performance overhead. The study will help to improve the intelligent DevOps in the future by facilitating self-adaptive and scalable efficient testing pipelines, which will lead to the development of robust and reliable delivery of cloud-native applications.
CareerMate - One Stop Solution for Job Preparation
CareerMate AI is an integrated solution designed to assist students in the entire job preparation pro-cess by bringing together assessment, practice, feed-back, and resume-building activities in one system. Current job preparation solutions work in a discon-nected manner, requiring students to work on differ-ent systems that lack integrated performance mea-surement and individualized feedback, making it challenging to track progress and adjust job prepa-ration approaches accordingly. The proposed solu-tion will be developed by bringing together four AI-based modules: adaptive MCQ-based skill assess-ment modules, an AI-based mock interview mod-ule that offers structured feedback on answers, a smart resume builder module that produces ATS-compliant resumes, and an automated resume re-view module that analyzes the relevance of content and keyword alignment for particular job roles. Nat-ural Language Processing methods will be applied to assess text-based answers, improve resume con-tent, and offer feedback, and adaptive logic will be applied to adjust question difficulty levels based on user performance. The four modules will be inte-grated into a single workflow system that allows for systematic assessment and progress measurement at various stages of job preparation.
Intelligent Predictive Maintenance through Machine Learning in Industry 4.0 Environments: A Review of Methods and Applications
Rapid advancements in industrial automation and digital transformation have underscored the significance of intelligent predictive maintenance (Pd.M.) in ensuring the durability, efficacy, and reliability of industrial assets. It is common for reactive and preventative maintenance procedures to lead to expensive downtime, wasteful use of resources, and unforeseen breakdowns. As an alternative, Pd.M. anticipates equipment failures using machine learning techniques, sensor fusion, and real-time data analytics, which minimizes operational disturbances and permits proactive decision-making. This paper offers a thorough analysis of ML-driven Pd.M., highlighting significant methodologies for industrial equipment health assessment, anomaly prediction, and Fault detection using deep learning, supervised learning, reinforcement learning, and unsupervised learning. Furthermore, the research delves into the most significant obstacles related to cybersecurity, scalability, feature selection, data quality, model interpretability, and Eliot system integration. The impact of ML-driven Pd.M. on industrial maintenance, including its ability to optimize asset performance, lower upkeep expenses, and boost operational effectiveness, is demonstrated through a thorough examination of real-world case studies and developing trends. The findings underscore the transformative impact of intelligent maintenance solutions and emphasize the need for continuous advancements in ML techniques to further enhance predictive capabilities in complex industrial environments.
Developer Productivity in Platform Engineering: Anchoring Large-Scale Migrations and Onboarding Strategies for Diverse Business Units
As cloud-native platforms evolve, the ”Human Bottleneck” the friction associated with migrating diverse business units to standardized infrastructure becomes the primary inhibitor of enterprise velocity. Traditional” Force-Majeure” migrations lead to technical debt and developer burnout. In this research, I propose a Migration Anchoring Framework that utilizes Productive Friction and Self-Service Abstractions to accelerate onboarding. I introduce a formal model for the Migration Friction Coefficient (Cf ) and demonstrate, through a multi-year study of a 2,000-engineer organization, how “Golden Path” engineering can reduce onboarding time by 72% while maintaining 100% compliance with platform standards.
Automated Remediation Guardrails: A Risk-Aware Framework for Validating AI-Generated Production Scripts in Regulated Financial Infrastructure
The integration of AI coding assistants into DevOps and site reliability engineering workflows has accelerated infrastructure automation, enabling engineers to generate Terraform configurations, shell scripts, and deployment parameter files at speeds previously requiring significantly more time. However, AI-generated infrastructure artifacts introduce a novel category of operational risk: code that is syntactically valid but semantically incorrect in the specific production context, hallucinated API calls, or configurations that create valid infrastructure with unintended security or reliability implications. Research has shown that approximately 40% of programs generated by AI assistants contain exploitable vulnerabilities, and that iterative AI code generation without human validation can increase critical vulnerabilities by as much as 37.6%. In regulated financial services environments where infrastructure changes require documented change control and SOC 2 audit evidence, deploying AI-generated production scripts without a structured validation framework creates compliance gaps that outweigh the productivity benefit. This paper presents a risk-aware remediation guardrail framework developed and deployed at NCR/Candescent for validating AI-generated infrastructure artifacts before their application to production banking environments. The framework implements a four-tier risk classification system based on blast radius analysis, reversibility assessment, and scope validation against existing Terraform state. It integrates with the Jira DCR change control workflow through Model Context Protocol tooling, ensuring every AI-generated artifact modifying production infrastructure is associated with an approved change record. Evaluated against a production corpus of 108 AI-generated artifacts, the automated tier classifier achieved 91% accuracy against expert human review as ground truth, with a 4.6% false negative rate in the safety-critical undertierring direction. The framework caught 7 artifacts containing production errors that visual plan review alone would not have detected.
Legacy System Decomposition Strategies for Cloud Modernization in Global Enterprises
The modernization of legacy information technology systems has emerged as a central strategic imperative for global enterprises seeking to maintain competitive advantage in an increasingly digitized marketplace. As traditional monolithic architectures reach their functional limits, the transition to cloud-native microservice architectures offers the promise of enhanced scalability, resilience, and deployment velocity. However, the process of decomposing established legacy systems often characterized by significant technical debt and undocumented complexities presents a myriad of architectural and operational challenges. This paper provides a comprehensive investigation into legacy system decomposition strategies, analyzing the technical mechanisms, economic implications, and regulatory constraints inherent in large-scale cloud modernization. By synthesizing current research on architectural patterns such as the Strangler Fig, Branch by Abstraction, and the Sidecar pattern, the study delineates a framework for incremental transformation that minimizes business disruption. Furthermore, the role of Domain-Driven Design (DDD) is explored as a foundational methodology for identifying service boundaries and maintaining domain integrity during the transition. The research also addresses the critical impact of global data sovereignty and residency laws on architectural decisions, particularly in highly regulated sectors. Through an analysis of quantitative modularity metrics and the identification of common anti-patterns such as the distributed monolith, this paper offers an expert-level roadmap for navigating the complexities of transitional-state architectures in the global enterprise context.
A Multi-Agent Reinforcement Learning System for Autonomous Optimization of Web Infrastructure and Services
The exponential growth of web-based applications and cloud-native services has introduced unprecedented complexity in managing modern web infrastructure. Traditional rule-based and heuristic-driven optimization approaches are increasingly inadequate to handle dynamic workloads, heterogeneous environments, and real-time service demands. This paper presents a comprehensive framework for a Multi-Agent Reinforcement Learning (MARL) system designed for the autonomous optimization of web infrastructure and services. The proposed system leverages distributed intelligent agents that collaboratively learn optimal strategies for resource allocation, traffic routing, load balancing, and service orchestration. Reinforcement learning (RL), particularly in multi-agent settings, offers a promising paradigm for adaptive decision-making under uncertainty. Unlike centralized optimization models, MARL enables decentralized agents to interact with both the environment and each other, facilitating scalable and resilient infrastructure management. Each agent in the proposed architecture is responsible for a specific subsystem—such as compute resource management, network routing, or service scaling—and learns policies through continuous interaction with the environment using reward signals derived from performance metrics like latency, throughput, and cost efficiency. The architecture integrates advanced techniques including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and cooperative learning mechanisms such as centralized training with decentralized execution (CTDE). The system also incorporates state representation models capturing real-time metrics, action spaces defined by infrastructure control parameters, and reward functions designed to balance multiple objectives such as performance, reliability, and cost. To validate the effectiveness of the proposed approach, simulations are conducted on a cloud-based web service environment with varying workloads and traffic patterns. The results demonstrate that the MARL system significantly outperforms traditional auto-scaling and rule-based optimization techniques in terms of response time reduction, resource utilization efficiency, and system stability. Additionally, the system exhibits strong adaptability to sudden workload spikes and failures, highlighting its robustness in real-world scenarios. The study also explores challenges such as non-stationarity, agent coordination, and scalability, providing insights into potential solutions including communication protocols and hierarchical learning structures. The findings suggest that MARL-based systems can serve as a foundational technology for next-generation autonomous web infrastructure management. This paper contributes to the field by presenting a detailed design, implementation framework, and evaluation of a MARL-based optimization system, offering a scalable and intelligent alternative to existing infrastructure management solutions.
AI-Driven Performance Monitoring and Optimization in SAP BW/4HANA And AWS Hybrid Architectures
The accelerated development of enterprise data ecosystems has led to the need to combine the sophisticated analytics systems with scalable cloud platforms. Next-generation data warehousing solution SAP BW/4HANA provides more in-memory processing and performance, whereas Amazon Web Services (AWS) offers distributed, elastic, cloud computing abilities. Nevertheless, hybrid implementations that use on-premise SAP systems with AWS environments are subject to introducing complexities in terms of performance monitoring, workload optimization and resource allocation. The conventional monitoring devices that are mostly rule-based and reactive are not adequate to deal with the dynamic and heterogeneous character of such systems.The current paper features an AI-based performance monitoring and optimization framework in an SAP BW/4HANA environment deployed on the AWS hybrid environments. The solution proposed depends on machine learning algorithms (supervised regression models, data clustering, and reinforcement learning approaches) to make predictive analytics, anomaly detection, and automated optimization possible. The system will combine SAP application server, SAP HANA database, and Amazon EC2, S3, and CloudWatch telemetry data.Multi-layered architecture is presented, which comprises of data acquisition, feature engineering, model training and decision orchestration layers. The KPIs of seek response time, CPU response and memory response and the I/O throughput are constantly monitored and analyzed. The system bottlenecks are predicted using the predictive models and resource provisioning and query execution strategy are dynamically changed as part of the optimization policy.The study measures the performance of the suggested framework through simulated workload of enterprises. Findings show that there have been great advancements in the performance of systems which are lower latency, improved throughput, and better resource utilization. Proactive system control with the combination of AI-based insights helps to reduce downtimes and service cost.The research paper makes a contribution to the emerging area of intelligent enterprise systems through the optimization of the hybrid architecture on a scale, adjustable, and automated. Those results highlight a critical role of integrating AI methods and business-scale data systems to attain effective and stable system performance.
AI/ML-Driven Predictive Analytics on SAP BW/4HANA Using AWS Athena and Delta Lake for Scalable Financial and Operational Intelligence
The rapid data volume increase in any enterprise has resulted in the need to build data analytics frameworks that are scalable, efficient, and intelligent enough to help convert raw data into actionable insights. Existing organizations are becoming more dependent on sophisticated analytics solutions as a competitive edge, process optimization, and monetary decision-making. The paper will provide a full architecture of AI/ML-based predictive analytics with SAP BW/4HANA, using AWS Athena and Delta Lake to facilitate scale in financial and operational intelligence. SAP BW/4HANA is an effective enterprise data warehouse that is in-memory computing optimized and allows real-time analytics and data modeling speedy. Nevertheless, classic BW systems are challenged with the capabilities of processing unstructured information, processing at scale on distributed computers and machine learning applications. In order to overcome them, in this study, a hybrid architecture, which is a combination of SAP BW/4HANA and AWS-native cloud-native services (aws athena as the serverless querying engine and Delta Lake as trusted data lakes with ACID transactions), will be suggested. The suggested framework allows the easy data extraction of SAP BW/4HANA into a scaled data lake setting, in which Delta Lake is utilized to guarantee the integrity of data, enforce data schemas, and provide time travel. The AWS Athena supports a cost-effective, rapid querying of large data sets in the Amazon S3 storage, without the need to provide infrastructure. The combination of AI/ML models allows making financial forecasting, anomaly detection, and optimization of operation. The current study proposes a multi-layered architecture that includes the data ingestion, storage, processing and analytics as well as visualization layers. Regression, time-series forecasting, and classification algorithms based on machine learning are used to predict financial and operational data. To guarantee accuracy and scalability, the system also includes feature engineering, pipelines of data transformation and mechanisms of model evaluation. The procedure involves ETL/ELT operations, Delta lake schema design, query optimization in Athena and model training via distributed computing frameworks. The performance of the solutions is determined according to the scalability, query latency, accuracy of the predictions, and cost effectiveness. Findings show tremendous response time acceleration in query performance and information reliability in predictive accuracy in comparison to the conventional SAP only systems. Moreover, the article reveals the main issues of data regulation, the complexity of integration, and security concerns and suggests their solutions, including metadata managing, encryptions, and access controls. The results indicate that SAP BW/4HANA and AWS Athena and Delta Lake are an effective system of predictive analytics at an enterprise level. The paper will be relevant in the context of enterprise analytics by showing the possibility to build a scalable, cost-effective, and high-performance architecture that will allow connecting to both the traditional data warehousing and the current technologies of the data lake and machine learning. The suggested structure can be embraced in all the sectors to improve financial planning, risk management, and efficiency of operations, ensuring that intelligent and data-driven businesses emerge.
Guardrails in Generative AI for Retail Financial Planning
Generative Artificial Intelligence (GenAI) is increasingly being adopted in financial planning systems that assist retail investors with investment decisions, budgeting, and long‑term wealth management. While large language models can interpret financial data and provide personalized recommendations, they also introduce risks such as hallucinated advice, regulatory non‑compliance, and biased financial recommendations. Guardrail mechanisms are therefore essential to ensure that AI‑generated financial advice remains safe, compliant, and aligned with financial best practices. This research paper explores the role of guardrails in GenAI‑driven financial planning systems for retail consumers. The study presents the architecture of a guardrail‑enabled financial planning system, discusses datasets used in financial planning models, and explains the calculation logic that supports responsible investment recommendations. Real‑world case studies illustrate how guardrails can prevent harmful outputs and maintain regulatory compliance. The paper also analyzes the integration of policy enforcement layers, risk scoring models, and validation mechanisms in financial AI systems. The findings demonstrate that guardrail frameworks significantly improve reliability and trustworthiness in AI‑driven financial advisory systems. The paper concludes by highlighting the importance of combining machine learning, financial analytics, and governance frameworks to build safe and trustworthy AI‑powered financial planning platforms.
Generative Artificial Intelligence (Genai)
Privacy-Preserving Personalization Using Federated Learning in AEM
Adobe Experience Manager (AEM) relies heavily on personalized digital experiences, as they are a key enabler of brands distributing the right, timely, and user-friendly content that matches each individual customer's needs. On the other hand, reaching that high level of personalization frequently entails the gathering and processing of sensitive data located on the client side, thereby posing privacy problems of user tracking, data centralization, and conformity with rules such as GDPR and CCPA. Consequently, Federated Learning (FL) stands out as an excellent alternative in that it permits the training of machine-learning models to be done on the user's device without having to send the raw personal data to a central server. This paper devises a privacy-respecting personalization mechanism that uses FL to communicate with AEM's personalization engine, consequently allowing a company to obtain behavioral insights without violating the privacy of the users. Herein, the AEM-managed digital touchpoints receive the deployment of lightweight FL models that learn from on-device interactions, and at regular intervals, they send only the encrypted model updates to the server, thus ensuring privacy preservation. From there, AEM aggregates these updates to fine-tune global personalization models that are sent back to the devices for further training. As such, the method retains the sumptuousness of AEM personalization features like content targeting, segmentation, and predictive recommendations while simultaneously achieving a significant cut in privacy risk. The suggested remedy achieves refined model metrics, augmented user engagement, and shorter personalization response time due to execution at the local level, all happening concurrently with the maintenance of data secrecy. The potential of such a solution includes the optimization of e-commerce, the scoring of content relevance, real-time audience segmentation, and adaptive customer journeys across websites and mobile apps.
AI-Driven Predictive Deployment Pipeline for AEM as a Cloud Service
Adobe Experience Manager as a Cloud Service (AEMaaCS) has changed the way businesses build & deliver these digital experiences. However, its highly automated, cloud-native architecture makes CI/CD more difficult because it requires frequent code deployments, strict deployment schedules, continuous platform updates & complicated dependency behaviors that can make these deployment cycles unpredictable. Conventional pipelines usually only fix problems after they happen, which slows down release speed because of delays, rollbacks, and many other inefficiencies. This paper introduces an AI-driven predictive deployment pipeline designed to improve their intelligence, foresight, and adaptive decision-making in AEMaaCS release processes. Before the deployment is run, the pipeline uses machine-learning models to look at historical deployment logs, code modification metadata, AEM health indicators & environmental trends to figure out how likely it is that the build will succeed, the content will regress, the performance will drop, or the environment will become unstable. It works seamlessly with existing DevOps workflows, adding predictive scoring, automatic risk classification, and dynamic deployment strategies like auto-pausing, optimizing rollout timings, or proposing code changes. The experimental setup tests the system with actual deployment datasets and simulated cloud service environments. It shows huge improvements in deployment reliability, fewer failed builds, faster recovery times. The results suggest that forecasting scoring can help teams stay clear of high-risk releases with a precision of up to 85% and decrease the number of pipeline failures by recognizing problems early on. The proposed approach improves AEMaaCS DevOps through shifting how they handle implementations from a reactive to a more proactive way of making decisions using the information they have. The contributions include the latest predictive architecture made for AEMaaCS, a whole approach for forecasting deployment behavior & an empirical study that shows how useful it is in actual life. This research aims to empower teams to accomplish more intelligent, secure, and efficient releases, hence enhancing time-to-market and improving the reliability of digital experience delivery inside modern cloud ecosystems.
Reinforcement-Learning-Based Personalization Engine for Adobe Experience Clouds
This research presents a Reinforcement Learning-Based Personalization Engine designed to enhance the provision of tailored digital experiences within Adobe Experience Cloud during customer journeys. Traditional rule-based or static machine-learning personalization techniques often struggle with changing their user behavior, limited information as well as shifting business goals. This means that these systems need to always be learning & changing in actual time. The proposed engine employs reinforcement learning as its principal decision-making structure, enabling the system to observe user interactions, predict intent & improve content selection through continuous feedback loops. The architecture works well with Adobe Experience Platform, Adobe Target along with Adobe Analytics. It uses customer profiles, event streams & content metadata to express state, and it sets rewards based on their engagement, conversion, and long-term value assessments. A multi-agent reinforcement learning architecture is used to balance exploration as well as exploitation among many other different audience groups, making sure that their personalization is both scalable & consistent. The methodology includes ways to encode states, deep reinforcement learning algorithms to optimize policies as well as an orchestration layer that makes sure that actual time decisions are in line with marketing goals & compliance requirements. A case study demonstrates the engine's successful utilization for tailored online and app experiences, highlighting improvements in click-through rates, dwell time as well as conversion rates compared to baseline models. Experimental results demonstrate that the RL agent proficiently adapts to evolving user behaviors, alleviates overfitting to ephemeral trends & delivers experiences that are more contextually aware. This study introduces a scalable, intelligent, and extensible customization technology that transforms Adobe Experience Cloud into a continuously evolving ecosystem, thereby improving significant & quantifiable user engagement.
AI-Driven Forecasting in Dynamics 365 Sales: What Businesses Need to Know
AI-driven forecasting embedded in contemporary Customer Relationship Management (CRM) systems is the major factor in changing the way businesses predict sales outcomes, optimize decision-making and improve revenue accuracy. Using predictive analytics gives businesses the ability to handle large datasets, discover the hidden patterns, and come up with dependable sales projections that not only help strategic planning but also performance management. Microsoft Dynamics 365 Sales is the most prominent platform that goes hand in hand with advanced AI capabilities, offering such tools as predictive scoring, pipeline intelligence, and automated insights which are the main source of power for sales teams to invest their time in high-value opportunities. This paper is about the use and performance of AI-driven forecasting within Dynamics 365 Sales. It takes a mixed-method approach by combining quantitative analysis of forecast accuracy and qualitative assessment through business case studies. The results show that the integration of AI leads to the highest precision of prediction, the most efficient sales, and better customer engagement while at the same time reducing human bias in sales predictions. The case studies demonstrate the rise in conversion rates and resource optimization that is measurable and common to different sectors of the economy. The research points out that the strategic application of AI forecasting tools is a major factor in the coming of a more agile, data-driven decision-making process which is the main reason organizations can foresee market shifts and still be able to allocate resources effectively. In other words, AI-driven forecasting in corporate sales environments like Dynamics 365 Sales is a vital move forward in business intelligence, with the next changes being anticipated to involve increased automation, context-aware analysis and instant adaptability in sales ​‍​‌‍​‍‌management.
How Enterprises are Achieving 360° Customer Views with Dynamics 365
Modern businesses are trying to get to know their customers better so they can provide them more individualized, persistent. & meaningful experiences every time they connect with them. Microsoft Dynamics 365 is a key part of this transition because it combines both CRM & ERP features with AI-driven insights & advanced analytics. This link lets organizations combine different types of user information from sales, marketing, service & activities into a unified, smart outline. This helps teams make conclusions faster & with more data. Getting a full picture of the customer is hard because of data silos, previous systems & information that isn't always accurate. These things might make it very hard to see & respond to customers. Dynamics 365 overcomes these problems by providing a single data model, a built-in cloud connection & built-in automation that ensures data consistency & availability across departments. This study investigates an organized approach that coalesce their information, automates processes & utilizes their machine learning inside these Dynamics 365 to get thorough buyer insight. A case study demonstrates how firms using these capabilities have improved customer settlement, decreased response times & increased operational efficiency. The findings demonstrate that acquiring a holistic customer view is not only a technology advancement but a strategic enabler of digital transformation enhancing engagement, cultivating loyalty & equipping firms for sustained growth in a customer-centric era.
Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences
This study examines the impact of Microsoft Dynamics 365 Commerce on merchandising by integrating the actual world & many online interactions to create a genuine omnichannel environment. The idea is to figure out how many firms can use this platform to provide each consumer a tailored, data-driven experience while yet keeping these things operating extremely efficiently. The method involves looking at the platform's main features, such as AI-driven product guidance, a coherent commerce architecture & integrated their analytics, to see how they affect customer engagement & make supply chains more efficient. Dynamics 365 Commerce connects all of these retail touchpoints, such as storefronts, online & tasks in the back office. This helps firms guess what customers want, better manage their inventories & respond rapidly to changes in the market. The findings show that this integrated ecosystem makes it easier to run a retail business & gives decision-makers useful information that helps growth & customer loyalty. The technology uses smart automation & predictive by these analytics to help shops provide customers consistent, personalized shopping experiences while cutting down on these expenses & inefficiencies. The research says that Dynamics 365 Commerce encourages retail innovation by bringing together traditional & also digital channels, changing how customers engage with businesses & getting businesses ready for long-term success in a world that is increasingly focused on their information.