International Journal of AI, BigData, Computational and Management Studies
Azure DevOps CI/CD Pipeline Design for Regulated Financial Software Deployment Environments
Continuous integration and continuous delivery in regulated financial software environments must satisfy requirements that extend beyond standard software engineering practice, including change management controls, deployment audit trails, segregation of duties enforcement, and rollback capability within regulatory timeframes. This paper presents a design framework for Azure DevOps CI/CD pipelines that satisfy these requirements while maintaining deployment velocity in enterprise financial software organizations.
Spring Batch Framework Patterns for Large-Scale Data Processing in Cloud-Native Microservices: A GCP Deployment Study
Although Spring Batch is a commonly cited framework for large-scale batch data processing in enterprise Java applications, using it in conjunction with cloud-native microservices architectures on Google Cloud Platform (GCP) brings design challenges that are just as significant as those faced in on-premises deployment scenarios with regards to job partitioning, fault handling, event scales and observability. The aim of this paper is to perform a systematic investigation of the four patterns of the Spring Batch framework specifically tailored for cloud-native microservices running on GCP: job partitioning patterns to achieve distributed computing, remote chunking patterns to achieve horizontal scaling, Cloud Pub/Sub integration patterns for event-driven batch triggering, and Stackdriver-based patterns for bull batch monitoring in production. Benefits of using enterprise data processing workloads are clearly illustrated through a series of case studies that show measurable gains in throughput and operational reliability in workloads when compared to the traditional batch deployment model.
Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.
Intelligent Risk Assessment and Classification in Project Management Using Data-Driven AI Models
The rapid evolution of globalization, digitalization, and technological innovations has led to an increase in complexity of the project management domain; therefore, assessing and classifying the risks involved becomes increasingly complicated. The conventional techniques of risk management, which include risk identification, analysis, and forecasting, cannot adequately detect and control complex risks related to finance, operations, technology, strategy, and cybersecurity. In view of the aforementioned difficulties, the use of “Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), predictive analytics, and big data” has been identified as a smart way to improve the management of the project risks. This study is intended to review the contribution of AI-based solutions for risk assessment and classification in project management settings. This study discusses several types of AI algorithms and predictive models, including “Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Networks, Recurrent Neural Network, and Long Short-Term Memory Models”, focusing on their uses, strengths, and weaknesses in project risk management. AI enhances the accuracy of predictions and decisions, enabling real-time monitoring and planning by uncovering patterns of risks and developing measures to mitigate them. Yet, several barriers remain that prevent the implementation of intelligent risk management practices, including data integrity problems, cybersecurity concerns, ethical issues, AI bias, lack of transparency, and absence of real-life validation studies. This paper concludes that the implementation of Explainable AI (XAI), real-time analysis capabilities, cloud computing technologies, and digital twins can improve the performance of intelligent risk management systems.
A Conceptual Framework for Training Effectiveness through AI-enabled Digital Learning in Luxury Automotive Retail
The luxury automobile retailing industry is witnessing a paradigm shift owing to digitalization, changing customer needs, connectivity of vehicles, advancements in electric vehicles, and the integration of Artificial Intelligence (AI) technologies into different business operations. In this backdrop, the training of employees has gained importance and become a key driver of service delivery, customer satisfaction, brand image, and competitive advantage for organizations. In this regard, the present review paper aims to develop a conceptual model to improve training performance through digital learning using AI technologies in luxury automobile retailing. For the purpose of this research, the exploratory approach will be used along with a conceptual review method based on literature reviews on artificial intelligence, digital learning, organizational learning, adaptive learning systems, employee training, and automotive retailing innovations. The conceptual model under investigation shows that artificial intelligence-enabled learning systems serve as major catalysts to ensure efficient training with engagement, learning satisfaction, motivation, and knowledge retention acting as crucial intermediary variables. Based on the results obtained, AI-based learning environments could have a substantial impact on the development of workers' competencies, fast learning processes, high-quality customer service, organizational efficiency, and competitive advantages in luxury automobile retail settings. Nonetheless, issues of algorithmic discrimination, employee privacy protection, ethical management, and maintenance of human elements in premium customer communications should not be underestimated. Therefore, the research comes to the conclusion that the future of training programs in luxury car dealerships lies in combining artificial intelligence technologies and human-based methods in education.
Impact of IoT and Digital Technologies on Project Management Efficiency: A Survey Study
The Internet of Things (IoT) and digital technologies have made a significant impact on project management, enhancing efficiency, communication, monitoring, and decision-making. Some of the traditional methods of project management can sometimes have problems with poor communication, understated estimation, quality of resources usage, and lack of risk management. Advanced digital solutions, including AI, Cloud computing, Big data analytics, Blockchain technology and IoT devices, hold the promise of delivering innovative solutions to these challenges through automation, real-time monitoring, predictive analysis, and intelligent decision-making. The study is a survey research that explores the effect of Digital Technologies and IoT on the efficiency of the project management, specifically on the efficiency of the following project management tasks: planning, monitoring, communicating, managing risk, and optimizing resources. This research also evaluates the added value of digital tools to increase productivity, reduce operating costs, enable collaboration and support data-driven project management. Additionally, it outlines the critical implementation issues, such as cybersecurity concerns and deployment expenses, technical skill gaps, and integration complexities. The study highlights growing importance of digital transformation in project management and provides recommendations for future studies to recognize impact of implementing digital transformation to increase project efficiency in various industries through a comprehensive literature review.
Examining the Role of Adaptive Policy Frameworks and Organisational Sustainability in the Indian Manufacturing Industry under Uncertain Business Conditions
The Indian manufacturing industry is experiencing a radical change as a result of globalization, digitization, environmental factors, technological disruption, and business uncertainties. Supply chain disruptions, fluctuations in prices of raw materials, geopolitical issues, climate change regulations, and the emergence of Industry 4.0 technology are some of the factors responsible for creating operational and strategic issues for manufacturing firms. The traditional rigid governance strategies are becoming obsolete in managing such uncertain scenarios and, therefore, require new policy strategies that enhance adaptability and sustainability. In this research, an explorative methodology will be used through literature analysis concerning adaptive governance, organizational sustainability, Industry 4.0, and sustainable manufacturing. This study explores the role played by the mechanisms of adaptive governance, which include value chain flexibility, real-time data integration, monitoring mechanisms, and organizational culture with a focus on sustainability, in facilitating organizational resilience, efficient use of resources, innovations, and competitiveness. In addition, the study evaluates the key driving forces and the key hindrances to the uptake of sustainability in India. These factors include institutional pressure, competitive pressure, technical hurdles, financial limitations, and supply-related obstacles. It has been established that manufacturing companies which adopt adaptive governance mechanisms and sustainable practices have better chances of managing uncertainties and enhancing resilience.
Building a PMO from Scratch: A Practical Governance Framework for Organizations Without Project Management Maturity
Given the increasingly dynamic nature of organizational environments in contemporary times, the adoption of projects as tools for driving organizational transformation, innovation, and competitive advantage has gained momentum. Nevertheless, organizations that exhibit low levels of maturity in project management face various issues because they do not have effective governance processes, standardized project management practices, and even PMOs that can help them undertake projects successfully. The current research seeks to explore the issues surrounding the establishment of a PMO within such organizations. In particular, through the use of systematic literature review and conceptual synthesis, the study seeks to analyze different governance models for PMOs, project maturity models, and implementation approaches in order to identify key obstacles to PMO establishment. With these results, the research proposes a phased model of governance that organizations can adopt progressively based on their readiness, maturity levels, and strategic considerations. The model underscores the importance of executive sponsorship, capability building, adaptive governance approaches, and maturity progression as key facilitators for PMO success. Through the provision of a practical and realistic approach for moving from informal to more formal project governance practices, the research makes a valuable contribution to academic discussions and practical applications. It offers an alternative approach to existing models of PMO implementation that are too complex and unrealistic.
Fin Tech Credit Scoring Systems and Financial Accessibility for Small Businesses
With the advent of rapid growth of FinTech technologies, traditional practices regarding the processes of credit scoring and loan making have been transformed, particularly focusing on providing financial inclusion for SMEs. Historically, banks and other financial organizations would use collateral, documentation procedures, and financial records for the purpose of loan granting, which made it impossible for some companies to get access to funds. To solve this problem, credit scoring mechanisms through FinTech started to apply new methods, including such approaches as AI, ML, Big Data, blockchain, and alternative data. This work is going to explore the issue of credit scoring via FinTech and will examine its development and evolution. In addition, the paper will review AI-based credit scoring algorithms that include Random Forest, Logistic Regression, Neural Networks, and XAI. Lastly, the paper will examine the incorporation of alternative data into credit scoring in the context of improving information transparency and financial inclusion among financially excluded borrowers. In addition, the paper examines some of the major obstacles faced in the FinTech lending ecosystem, including cybersecurity, privacy issues, algorithmic bias, lack of transparency, and regulatory challenges. The paper is based on the literature review and employs a critical analysis approach, coupled with theories of Information Asymmetry, Financial Inclusion, Technology Acceptance Model (TAM), and Diffusion of Innovation to explore the existing situation. It can be concluded from the findings that FinTech credit scoring surpasses traditional financing models with regard to performance, efficiency, financial inclusion, and predictive ability. However, for its sustainability, better governance frameworks, transparency, regulations, and cybersecurity measures must be put in place.
A Review on the Role of Innovative Leadership in Educational Technology and Student Learning Outcomes
The paper will provide a literature review of innovative leadership in educational technology and its impact on student learning in contemporary learning contexts. It is with the increasing use of digital technologies that educational establishments continue to employ the use of technology more and more in the effort to enhance the teaching and learning process. It is a trend that has led to the additional application of learning management systems, artificial intelligence solutions, online learning platforms, and virtual learning environments. The methodological component of the research will be developed on the basis of the qualitative literature review, in which research articles, reports, and other scholarly materials on the issues of educational technology, digital leadership, and technology-enhanced learning will be examined. The educational technologies can provoke increased student engagement, motivation, and access to learning resources through the reviewed literature, which will help to provide a flexible and interactive learning environment. It is also reflected in the literature that the new technologies, such as artificial intelligence tools and online platforms, may be applied to provide more personalized learning and improve academic performance. However, there are other elements, such as digital infrastructure, digital skills of teachers, and good leadership, which contribute largely to the successful implementation of technology. It is concluded in the review that innovative and digital leadership are important in the process of leading the process of adoption of technologies, the aspects of assisting the professional growth of teachers, and developing effective technology-based learning environments, which ultimately translate into better learning outcomes among the students.
A Federated Learning Approach to Distributed DevOps Automation in Platform Engineering Architectures
DevOps has shifted from a centralized and automation-driven system to a highly distributed, intelligent, and autonomous system of systems that are driven by cloud-native systems. Traditional DevOps automation pipelines, however, are hindered by centralized telemetry processing, a lack of cross-organization learning and privacy issues in multi-tenant environments. This paper introduces a Federated Learning (FL)-based DevOps automation framework for distributed platform engineering architectures to overcome these limitations. The proposed approach allows multiple DevOps environments (microservices clusters, CI/CD pipelines, Kubernetes-based platforms, etc.) to train machine learning models together, and avoid sharing raw operational data. Rather, only model updates (gradients or weights) are shared with a central aggregator, keeping data private but allowing global intelligence. This paradigm is useful for anomaly detection, predicting scaling, incident classification, and deployment optimization in distributed systems. The architecture brings together FL and DevOps toolchains like GitOps pipelines, observability stacks (Prometheus, Grafana), and infrastructure-as-code systems (Terraform, Helm). A hierarchical federated orchestration layer is added for model aggregation, drift correction, and client selection. In addition, adaptive optimization methods like FedAvg and FedProx are used to enhance convergence for non-IID (Non-Independent and Identically Distributed) system telemetry data. Experimental testing shows that the FL-based DevOps automation framework significantly mitigates mean incident response time, increases the precision of anomaly detection and reduces the downtime of the system when compared to the traditional centralized AIOps method. The results are also notable for the increased scalability in multi-cloud configurations, and for better privacy protection within GDPR-like environments. The research finds that federated learning can be integrated into DevOps and platform engineering architectures to create a scalable, privacy-preserving, and intelligent automation layer that enhances operational resilience and system reliability in today's distributed computing landscape.
FlashAttention and the Co-Evolution of Algorithms and Hardware: From IO-Awareness to Vector Optimization
FlashAttention has transformed transformer efficiency by solving the memory bottleneck of standard attention. However, its significance extends beyond a single algorithm. This paper argues that the FlashAttention family — from FA1 (2022) to VFA (2026) — demonstrates a mandatory co-design loop between algorithms and hardware. Each generation did not simply improve performance; it solved the new bottleneck created by the previous hardware generation. FA1 solved HBM bandwidth. FA2 optimized parallelism for A100. FA3 introduced asynchrony for H100. FA4 targets Blackwell's asymmetric compute. VFA (April 2026) now solves the vector-unit bottleneck. We trace this evolution, synthesize the pattern, and argue that future attention algorithms must be designed to co-evolve with hardware, not merely optimize for today's GPUs.
Controlled Pricing Governance Framework for Sales Order Revision in Oracle Fusion Order Management
Oracle Fusion Order Management (OM) automatically recalculates pricing upon revision of a sales order, aligning transactions with active pricing strategies and enterprise pricing rules. While this dynamic repricing mechanism supports market-responsive pricing models, it introduces contractual and compliance risks for trading enterprises where pricing commitments have already been communicated through Sales Order Acknowledgements (SOA). Once acknowledged, prices are often contractually binding and must not be altered without formal governance controls. This paper proposes a Controlled Pricing Governance Framework (CPGF) that introduces conditional repricing logic, approval-driven override mechanisms, and architectural safeguards within Oracle Fusion OM. Leveraging Order Management Extensions, Pricing Strategy controls, Orchestration governance, and BPM-based approvals, the framework ensures contractual price preservation while maintaining system flexibility. The proposed model strengthens revenue integrity, minimizes dispute exposure, and enhances compliance alignment in high-volume trading environments.
Anomaly Detection in Industrial Pumps Using Streaming Sensor Data
Pumps are the most abundant rotating assets in industrial facilities — yet they remain among the least continuously monitored. A refinery may operate 2,000 pumps; a municipal water authority may control 400 pump stations; an offshore oil platform may depend on 300 pumps across seawater injection, crude transfer, chemical injection, and utility services. When any one of these assets develops a fault — cavitation eroding an impeller, a bearing spall propagating on an outer race, a mechanical seal face wearing to the point of leakage, a motor developing rotor imbalance — the consequences range from process interruption to environmental release to catastrophic equipment damage. Traditional monitoring misses most of these faults: fixed threshold alarms fire only after significant damage is done; periodic manual rounds cannot achieve the continuous coverage the population demands; and pure statistical approaches cannot distinguish genuine fault signatures from normal operational variation. This 2026 Research Paper presents the state-of-the-art in real-time pump anomaly detection using continuous streaming sensor data — examining the complete pipeline from sensor acquisition (flow rate, pressure differential, vibration, temperature, motor current) through edge preprocessing, streaming ingestion via Apache Kafka and Flink, and AI inference using the latest generation of models: the attention-augmented LSTM Autoencoder (LSTMA-AE) with mechanistic constraints, validated on real oilfield injection pump data (Nature Scientific Reports, January 2025); the AquaSentinel Mixture-of-Experts spatiotemporal Graph Neural Network with LLM agent architecture for physics-informed causal localization; the UMLLA-AD Mamba-driven adaptive feature selection framework; and Explainable Anomaly Detection for Industrial IoT (SAC '26) combining online Isolation Forest with incremental feature importance scoring. Two comprehensive real-world case studies are examined in full technical depth: (1) an oilfield water injection pump fleet deploying the LSTMA-AE framework with mechanistic constraints — demonstrating significantly higher anomaly detection accuracy and lower false alarm rates compared to LSTM-AE, Isolation Forest, and Random Forest baselines on real field datasets; and (2) a water pipeline and pump station network deploying the AquaSentinel MoE-GNN-LLM system — detecting 97.5% of 110 simulated leak scenarios with sub-minute alert generation and automated causal localization to the source pump station. The paper concludes with a forward-looking analysis of five 2025–2026 technology advances: Mamba State-Space Models for ultra-long streaming context, neuromorphic anomaly detection for battery-free edge sensors, LLM-augmented explainability for maintenance technicians, federated learning across pump fleets, and autonomous self-healing pump control loops.
Agentic AIOps for Cloud-Native Enterprise Systems: A Governance-Centered Framework for Software Reliability, Security, and Deployment Optimization
Cloud-native enterprise systems increasingly operate through distributed microservices, event-driven data flows, container orchestration, software supply chains, and multi-layer observability pipelines. Although these architectures improve modularity and deployment flexibility, they also introduce operational complexity, hidden failure propagation, security exposure, and fragmented governance across software development, deployment, and runtime operations. Recent advances in artificial intelligence, large language models, and autonomous software agents create an opportunity to move beyond conventional monitoring dashboards and rule-based automation toward agentic AIOps systems that can reason over telemetry, correlate defects with runtime behavior, recommend remediation, and support secure deployment governance. However, existing approaches remain insufficient because many of them treat software defect prediction, anomaly detection, incident response, cybersecurity controls, and deployment optimization as separate functions rather than as integrated lifecycle capabilities. This paper proposes a governance-centered Agentic AIOps Framework for cloud-native enterprise systems. The framework combines predictive software reliability models, graph-based dependency reasoning, secure software development controls, runtime observability, AI risk governance, and human-supervised remediation workflows. The major contribution of this paper is a vendor-neutral reference architecture that connects AI-driven defect prediction, cloud-native telemetry, policy-based security, deployment risk scoring, and enterprise governance into a unified model. The paper argues that agentic AIOps can improve operational decision support when it is designed with traceability, explainability, guardrails, and measurable reliability objectives rather than unrestricted automation. The proposed framework is conceptual and architecture-based, and its value lies in structuring future enterprise implementations, comparative evaluations, and research on trustworthy AI-enabled operations.
Cognitive Agentic AI Framework for Autonomous Enterprise Reliability and Secure Cloud Operations
The rapid transformation of enterprise computing environments toward cloud-native architectures, hybrid infrastructures, and distributed DevOps ecosystems has significantly increased operational complexity and cybersecurity risks. Traditional cloud management and security models are increasingly unable to cope with the scale, dynamism, and heterogeneity of modern enterprise systems. In response to these challenges, Cognitive Agentic Artificial Intelligence (CAAI) has emerged as a transformative paradigm capable of autonomous reasoning, adaptive orchestration, predictive analytics, and self-healing operational intelligence. This research proposes a comprehensive Cognitive Agentic AI Framework designed to enhance autonomous enterprise reliability and secure cloud operations within AI-driven DevSecOps ecosystems. The framework integrates cognitive reasoning agents, autonomous orchestration engines, zero-trust security mechanisms, reinforcement learning-based decision systems, predictive reliability analytics, and explainable AI governance modules into a unified architecture. The study investigates how cognitive agentic systems can continuously monitor enterprise workloads, detect anomalies, predict operational failures, automate remediation processes, and enforce dynamic cloud security policies without excessive human intervention. A detailed literature review highlights limitations in existing cloud automation approaches, including insufficient contextual awareness, fragmented orchestration, reactive threat mitigation, and poor explainability. The proposed framework addresses these research gaps by combining multi-agent cognition, adaptive cloud intelligence, secure orchestration pipelines, and autonomous policy management. The research methodology employs a conceptual architecture-based analytical model supported by comparative evaluation, operational simulation insights, and technical analysis of AI-enabled DevOps workflows. Results demonstrate that cognitive agentic frameworks significantly improve reliability metrics such as incident response time, infrastructure availability, threat detection accuracy, and operational scalability. Furthermore, the framework enhances enterprise resilience through self-healing operations, dynamic risk assessment, and intelligent workload optimization. The study concludes that Cognitive Agentic AI represents a critical evolution in autonomous cloud governance and enterprise reliability engineering. The proposed model contributes to future intelligent enterprise systems capable of achieving resilient, secure, adaptive, and trustworthy cloud operations in increasingly complex digital infrastructures.
Agent-Based Artificial Intelligence Models for Enterprise Cloud Governance and Resilience
The rapid evolution of enterprise cloud ecosystems has significantly transformed organizational digital infrastructures by enabling scalable computing, distributed services, and dynamic operational management. However, the increasing complexity of cloud-native environments has also introduced critical challenges related to governance, cybersecurity, infrastructure resilience, compliance management, workload orchestration, and fault recovery. Traditional governance mechanisms often rely on static rule-based systems that lack adaptive intelligence and autonomous response capabilities. In this context, Agent-Based Artificial Intelligence (ABAI) models have emerged as a transformative paradigm for intelligent cloud governance and resilient infrastructure management. This research article investigates the role of agent-based AI architectures in strengthening enterprise cloud governance frameworks while enhancing operational resilience across distributed cloud ecosystems. The study explores how autonomous AI agents can perform real-time monitoring, predictive analytics, policy enforcement, anomaly detection, adaptive orchestration, and self-healing operations within enterprise cloud environments. The article further evaluates the integration of multi-agent systems with cloud security, compliance automation, and service continuity management. A comparative assessment between traditional governance models and agent-driven intelligent governance architectures is also presented to highlight operational improvements. The research methodology employs a conceptual analytical framework supported by secondary data analysis, literature synthesis, and comparative evaluation models. The findings indicate that agent-based AI systems substantially improve governance efficiency, infrastructure reliability, cyber resilience, and decision automation while reducing operational latency and administrative overhead. The study concludes that ABAI-driven governance frameworks represent the future of intelligent enterprise cloud operations and resilient digital transformation strategies.
Troubleshooting Backup, Restore, and Data Export Failures in Relational Databases
For relational database systems, the accuracy of information and the company's ability to get its information back rely on their reliable backup, reconstruction, and data extraction services. As companies rely more & more on their data-driven mobile apps, even little problems such as processes may lead to huge problems for operations, finances & reputation. This study examines the complex difficulties that often impede these vital operations, including information corruption, problem configuration, network disruptions & storage limitations. These problems usually develop when dependence issues aren't fixed, plans aren't followed through on, or people don't keep track of things well enough. This might cause recovery apps to fail or exports to be missing. This article describes a step-by-step approach for quickly discovering, recognizing, remediating issues. The platform utilizes algorithmic learning for finding unexpected patterns, extensive log diagnostics & predictive analytics to figure out what went wrong before it becomes an enormous issue. It highlights how crucial it is to implement their proactive surveillance, installation confirmation & automatic confirmation strategies to make sure that the archived copies of the database are secure & can be restored. This approach gets rid of the necessity of patients to become many participants by combining smart error detection with guided recuperation methods. It also shortens the time between recovery objectives. This study shows how important ML has become for improving their resilience. Automated verification as well as warning these systems make it easy to run safeguarding & reconstruction cycles while still following information security standards. The desired result is a strong database reconstruction & backup system that can keep working even when things go unexpectedly. The current study enhances the reliability as well as productivity of their relational databases by transforming reactive maintenance into a proactive, insight-based approach that ensures data remains accessible & consistent across many other contexts.
Network Troubleshooting in Financial Systems: TCP/IP, DNS, and Firewalls
Modern financial systems depend on very strong and always available network infrastructures to enable the various real-time transactions, digital payments, and interbank communications that, if disrupted even for a very short time, can lead to financial losses, regulatory issues, and customer distrust. In this paper, we first emphasize the importance of TCP/IP, DNS, and firewall settings in securing financial networks in terms of their availability, integrity, and safety, and then we show how transaction processing and system access can be disrupted by failures such as packet loss, high latency, DNS resolution errors, and wrongly set firewall rules. We identified typical network problems that impact financial platforms and presented a logical troubleshooting methodology, including layered network analysis, monitoring, log review, and configuration validation to quickly find and fix the problems. Additionally, a scenario study illustrates how the combination of a DNS error and a firewall policy modification blocking the necessary port caused transactions to fail and how a methodical troubleshooting approach saved the day by restoring the service and making the system more resilient. Finally, the article highlights that the investment in financial IT operations can be made more fruitful through proactive monitoring, well-documented procedures, and coordinated teamwork thereby ensuring consistent and reliable network ‍​‌‍​‍‌performance.
Performance Tuning Cloud-Hosted Databases: Resource Allocation & Query Optimization
What used to be a relatively simple task of data management has been substantially complicated by the rapid widespread transition to cloud-hosted database technologies such as Microsoft Azure SQL, Google Cloud Spanner, and other managed relational platforms, where the major selling points are scalability, availability, and flexibility. Nevertheless, this development has resulted in recurring performance issues that are inherently due to inefficient resource allocation and poorly optimized queries. Such inefficient practices can cause latency to be increased, throughput to be unpredictable, and inflow of money for operational purposes to be much higher than before, these are the problems cloud environment elasticity users fail to solve. In response to these issues, this work develops a hierarchical improvement framework that combines performance tuning with dynamic resource allocation and AI-guided query optimization. Our compound method uses the proposed model to leverage predictive tools for on-demand distribution of operands and memory together with machine learning to continuously refine each execution plan, user behavior, or workload pattern. Through a series of tests on various cloud platforms, we convincingly demonstrate performance enhancements such as throughput increase by 40%, query latency reduction by 35%, and operational costs decrease by 25%, which are not achievable to the same extent by static tuning methods only. Along with tangible performance enhancements, the paper advocates that in the present-day database world, continuous monitoring, and automation are indispensable gears of the machinery. Cloud database administrators can implement this proposition that delivers them a guide, stepping aside from the empiric way, rather on a data-driven way, which unites performance with cost efficiency and leads to the intelligent tuning.
Maximizing AI Callout Time Using Visualforce Pages in Lightning Components
This research study is about ways to counter the problem of AI callouts in Salesforce Lightning being limited by the Visualforce pages that have been integrated within the Lightning Components to extend the time of execution for the external API interactions. Salesforce limits the callout time strictly by governor limits, which can be a problem in complex AI integrations that require a longer time for processing. In order to solve the problem, the research digs into the technical bottlenecks of the asynchronous framework of Lightning and offers a hybrid architecture that uses the server-side capabilities of Visualforce to keep longer API sessions without a platform constraint breach. A controlled implementation was created to test the performance of AI callouts such as OpenAI and Einstein Language through a custom Lightning-Visualforce integration, latency, reliability, and throughput improvements were measured. The results show that this method increases the average time of callout sustainability by about 40%, thus communication with AI services becomes more efficient without violating Salesforce’s execution policies. These findings are the best practices that can be implemented for Salesforce AI integration which acts as a scalable, compliant way of extending the time of AI interactions in Lightning applications.
Salesforce Flow's native HTTP Callout features cater very well to the need of administrators and developers to create integrations with external systems easily, without the need of Apex code. The article aims to show how Flow automation can handle data exchange in actual time, and can trigger external APIs when they can synchronize business processes just by using Salesforce's declarative interface. By setting up authenticated callouts and using Flow's standard features for data mapping, error handling & conditional logic, departments can now execute complex workflows that were previously custom development tasks. In terms of system design, this strategy is more straightforward as it keeps integrations centralized within Flow, thus allowing less maintenance work and better transparency for both technical and non-technical users. The result is an integration strategy that is more agile; the development cycles are shortened, and the flexibility is enhanced; at the same time, there is less dependency on Apex developers. Additionally, this no-code solution is a step towards long-term scalability as it enables organizations to quickly adapt their integrations to changes in business needs. Moreover, it enhances their security by using Salesforce's native authentication methods and maintains the system in an easy way through visually designed, effortlessly changeable workflows. In a nutshell, the use of external callouts in Salesforce Flows is a significant movement towards low-code integration; thus, it brings together in one single declarative automation framework the aspects of efficiency, reliability & governance.
When Rounding Up Matters: Working with Decimals in Apex
In the area of Salesforce Apex programming, correct decimal precision is not merely a technical issue - it is a fundamental requirement that determines the accuracy, the stability, and the overall business processes' trust. The paper discusses how rounding rules are significant and how even small decimal differences can cause large effects in the downstream areas that are financial transactions, taxation, revenue forecasting, and automated decision-making flows, to mention a few. Developers are often of the view that Apex will take care of decimal operations in a natural way, but this is not the case because Apex performs very strict operations for the Decimal type, and when combination are considered - variations in scale, rounding modes, and division behavior- unexpected challenges are very likely to be there. These problems cause errors to be off-by-one in money calculations; also, there can be situations where totals for aggregated reports are not aligned or even that discrepancies exist between UI-level values and backend computations. In order to come to terms with such difficulties, this research proposes a workable approach that is mainly based on the clear selection of the rounding strategy, the open precision control, and the pattern-based usage of the embedded mathematical functions in Apex. A local example provides an explanation as to how the irregularities in rounding in a tax computation engine caused mismatches in invoice totals in the different integrated systems thus requiring a systematic refactor using deterministic rounding modes such as HALF_UP and FLOOR and standardized helper utilities. The results demonstrate that it is better to define rounding rules explicitly rather than depend on implicit behavior because that not only makes calculations more precise but also raises maintainability, auditability, and cross-system consistency levels. This paper, by discussing the common mistakes, comparing different rounding methods, and showing practical solutions, conveys a far-reaching message that precision is not just a concern of mathematics, rather is it one of the fundamental tenets of business integrity in the Salesforce ​‍​‌‍​‍‌ecosystem.
Power-Bound Storage Design: Architecting Systems for Electrical Scarcity
As computing infrastructures are more and more deployed in edge environments, developing regions, and energy-constrained data centers, power scarcity has become one of the main system design challenges rather than being a minor optimization problem. This paper considers the case of storage systems that need to be designed to work reliably and efficiently when the power supply is limited, intermittent, or very strictly budgeted. We look at the design space of power-limited storage by challenging the traditional storage assumptions of always-on hardware, uniform performance targets, and energy-hidden I/O paths. Using energy-aware architectural principles combined with workload-adaptive control mechanisms, our methodology allows storage components to dynamically tailor their behavior to the available electrical capacity. The design in question includes features such as power-proportional data paths, selective activation of storage tiers, and policy-driven scheduling that explicitly compromises performance and latency for energy sustainability. By means of analytical modeling and system-level experiments, we prove that power-limited storage architectures can drastically cut down the energy consumption while keeping the service guarantees at an acceptable level even under the most difficult conditions. Some of the main takeaways are that a few small but smartly chosen design changes, e.g., energy-aware caching, buffer deferred writes, and power budgeting synchronization across storage layers, can bring a considerable increase in efficiency as compared to standard designs. Besides, it illustrates the wider consequences of power being regarded as a limited resource and thus makes energy awareness a major system abstraction. By partnering electrical realism with storage design, the method we propose lays down a solid practical base for making resilient, sustainable systems that can carry on functioning in power-limited environments, ranging from edge installations to the next generation of energy-constrained data centers.
Availability without Recovery: A Class of Failures Not Captured by SLAs
Availability is usually regarded as the most important reliability goal: if the service stays "up,” then the users should be able to work, the revenue should continue, and their trust should be preserved. But being responsive to this single dimension, a system can still neglect the other crucial attribute of resilience recovery. Thus we highlight the gap between the real meaning of availability and the way it is referred to in the industry. While the SLAs only talk about system uptime or error rates, hardly ever, the capability of a system to quickly, safely, and fully recover from faults is taken into account. So that, even under a scenario of prolonged network degradation, recurring problems, or half-working devices that never seem to return to normal, the contract availability requirements can be considered met by the business. We define such a situation as availability without recovery: a scenario where services thus on paper avail themselves (fulfilling health checks and SLA criteria) but still manifest a constant incapacity to heal performance, correctness, or capacity post-disruptions. Besides setting up a framework for such a phenomenon, we give examples of the failure's fingerprints (e.g., repeatedly attempting an operation, backlog accumulating, reliance on services functioning at a degraded level, and "green dashboards" camouflage loss of functionality), and recovery-specific metrics complementary to conventional availability metrics. In a way through a distributed production system example from real life, we have demonstrated that systems which are inheriting dependency cascades, automations gone wrong, and suffering from a silent shortage of resources can keep high levels of availability at the same time that recovery cycles fail.
Clone Lineage as Technical Debt in Snapshot-Driven Storage Architectures
Storage architectures driven by snapshots have become cornerstone elements in contemporary data management, allowing fast backups, recovery at any given time, and clones of the data that take up very little space. Snapshots and clones bring enormous flexibility to operations, but, at the same time, they create complex clone lineage chains hierarchical relationships between snapshots, clones, and their parent volumes which most of the time are invisible to the system administrators. Hidden chains in this way, if left unattended, can continue to accumulate and thus result in technical debt, which can be interpreted as performance degradation, increased storage overhead, and operational risks during recovery or migration. In this paper, we delve into clone lineage as a form of technical debt in snapshot-driven storage systems and what composes it. Specifically, we investigate how clone lineage, operational inefficiencies and technical debt that arise due to long and complex clone lineages can be quantified, characterized, and targeted for reduction. We have thoroughly studied enterprise-scale storage deployments based on empirical data and we have also simulated operational workloads and their impact on lineage evolution in order to better understand it. We invent metrics to ascertain lineage complexity, discover trends that can be linked to performance issues, and experiment with methods for lineage pruning and reorganization. One of the main revelations that our study brought to light is that uncontrolled clone creation can lead to a significant increase in storage usage levels of up to 35% in the environment that we observed, with a very noticeable delay in the time required to restore a snapshot. Besides that, some lineage configurations harm the write amplification and deduplication efficiency, thus producing higher overall operational costs in the absence of clone management.
When Healthcare Lags, Banking Leaks: A Generative AI Framework to Stop Time‑Based Data Spills in Cross‑Sector Federated Learning
In a cross-sector federated learning setup where hospitals and banks jointly train a cybersecurity AI, a quiet but dangerous problem emerges: hospitals often take weeks to report and remediate cyber threats, while banks must act within days or hours. This timing mismatch creates a subtle data leak, delayed model updates from healthcare nodes can unintentionally reveal patterns that expose sensitive banking data to an adversary. This paper introduces a human-centered generative AI framework that simulates and blocks such “time-based data spills.” Using a time-conditioned generative adversarial network (GAN), we first mimic how delayed healthcare updates distort the shared learning process. Then, a second defensive AI. a differentially private federated learner with adaptive noise learns to mask sector-specific footprints without forcing banks to wait for slow partners. We test the framework on an AWS cloud testbed using synthetic but realistic healthcare and banking transaction logs. Results show that our approach reduces cross-sector information leakage by over 70% while preserving detection accuracy above 90% for both sectors. Instead of demanding perfect synchronization, this work embraces real-world regulatory delays and offers a practical, privacy-respecting path for collaborative cybersecurity across industries that do not march to the same clock.
AI-Enabled Phishing, Deepfakes, and Social Engineering: Emerging Threats and Countermeasure Strategies
Generative artificial intelligence has introduced a new phase in the evolution of cyber deception, particularly in phishing, deepfake impersonation, and social engineering attacks. Traditional phishing campaigns often depended on poorly written emails, generic fraudulent messages, and visible technical weaknesses that users and security systems could detect with reasonable accuracy. However, the emergence of advanced language models, synthetic media tools, and automated content generation has changed this pattern. Attackers can now create highly personalized phishing emails, realistic fake identities, cloned voices, manipulated videos, and convin cing social engineering messages that imitate trusted individuals, organizations, and communication styles. This shift has made cyberattacks more scalable, persuasive, and difficult to identify, especially when human trust and organizational routines are exploited. This article reviews emerging AI-enabled cyber threats with specific attention to phishing, deepfakes, and social engineering. It examines how generative AI supports the production of realistic scam messages, fake websites, synthetic profiles, deepfake audio and video, and targeted deception campaigns. Existing studies show that AI-driven social engineering increases the quality, speed, and personalization of attacks, making it harder for users to distinguish between legitimate and fraudulent communication (Schmitt & Flechais, 2024; Jabir et al., 2025). The article also discusses how deepfake technologies create new cybersecurity risks by enabling impersonation, fraud, misinformation, and identity abuse. These risks are particularly serious in organizational environments where attackers may imitate executives, employees, vendors, customers, or public figures to manipulate decision-making and gain unauthorized access (Mirsky & Lee, 2021). In addition, the article evaluates current detection and prevention methods, including machine learning-based phishing detection, deep learning models, transformer-based email analysis, URL classification, multimedia forensics, and deepfake detection techniques. Machine learning and artificial intelligence have become important tools for identifying suspicious patterns in emails, websites, messages, images, audio, and videos. However, these methods still face limitations, including dataset bias, model generalization challenges, adversarial manipulation, and the rapid improvement of AI-generated deceptive content (Gupta et al., 2023; Jaffal et al., 2025). The article therefore argues that technical detection alone is not enough to address AI-enabled deception. The study proposes a multi-layered countermeasure strategy for organizations. This strategy combines automated detection systems, deepfake forensic analysis, identity verification, multi-factor authentication, human-centered cybersecurity awareness, incident reporting procedures, and AI governance policies. It emphasizes that employees remain a critical point of defense because many AI-enabled attacks rely on psychological manipulation rather than purely technical intrusion. Organizations must therefore strengthen both technological and human defenses by improving training, verification culture, access control, and responsible AI management. Overall, the article concludes that AI-enabled phishing, deepfakes, and social engineering represent a growing cybersecurity challenge that requires integrated, adaptive, and governance-driven defense strategies.
Privacy-By-Design Engineering Under GDPR and CCPA: Practical Patterns for Cross-Border Data Handling In Cloud-Based Applications
Article 25 of the European Union General Data Protection Regulation imposes a legal obligation on data controllers to implement data protection by design and by default. The California Consumer Privacy Act, with its 2020 amendment by the California Privacy Rights Act, reaches a similar outcome through a different mechanism, requiring clear notice, opt-out controls for sale or sharing of personal information, and limits on the processing of sensitive personal information. Together with the wave of comparable regulations enacted in other US states and other jurisdictions since 2020, these requirements have transformed privacy from a compliance footnote into a first-order engineering concern. This paper presents practical patterns for implementing privacy-by-design in cloud-based applications that handle personal data across jurisdictions. The patterns cover data minimization at the schema and API level, purpose limitation through tag-based access control, storage limitation through automated retention enforcement, consent capture and propagation, data subject access request fulfillment, breach notification readiness, and the architectural choices that determine whether cross-border data flows are tractable to govern. Each pattern is presented with the legal rationale that motivates it, the engineering work required to implement it, and the residual risks that remain. The patterns are intended to be tractable for engineering teams without specialized privacy expertise, while preserving the rigor that the regulations require.
Serverless Architecture Patterns for Enterprise AI Agents: ECS Fargate, OpenSearch k-NN, and DynamoDB for Knowledge-Grounded LLM Workflows
Production deployment of enterprise AI agents that combine large language models with proprietary knowledge bases presents specific architectural challenges. State has to be managed across asynchronous workflows. Retrieval latency has to be kept low enough to support interactive use. The knowledge base has to stay fresh as the underlying source systems change. Costs have to be predictable. The agent has to be observable and recoverable when individual steps fail. This paper describes a next-generation serverless reference architecture for a domain-specific AI agent. The architecture is built on AWS ECS Fargate for compute, Amazon OpenSearch with k-Nearest Neighbor search using Hierarchical Navigable Small World indexing for vector search, Amazon Titan embeddings for semantic representation, Amazon DynamoDB for workflow and conversation state, and an automated ingestion pipeline that synchronizes issue trackers, architecture documentation, and source code repositories into the knowledge base. The paper describes the end-to-end data flow from ingestion through embedding, retrieval, language model invocation, and response persistence. It discusses the design choices around HNSW parameter tuning, embedding refresh strategy, Fargate cold-start mitigation, isolation between tenant workloads, and incremental knowledge base updates without full reindexing. The architecture supports a graphics engineering bug triage agent that builds on the Retrieval-Augmented Generation approach described in earlier work and extends it with the operational properties that production deployment at scale requires. The contribution is a deployment-tested blueprint for teams building RAG-based agents on AWS, with attention to operational concerns that frequently receive less coverage in prototype-focused literature.