International Journal of Emerging Trends in Computer Science and Information Technology
Using Data Mining as a Tool to Enhance Threat Detection and Response
As the nature and scope of cyber threats are growing, signature-based protection mechanisms can no longer protect online resources. Having data mining investigate large volumes of data to uncover hidden patterns has been an essential aspect in the enhancement of threat detection and response. In this paper, we shall examine how data mining strategies can be applied to identify anomalies that can be utilized to predict attacks and even automatically defend the systems. According to the recent findings in machine learning and predictive analytics, information-driven models are more effective in identifying, responding faster, and helping in preemptive cybersecurity procedures. The other issues associated with the deployment of data mining solutions in cybersecurity that have been discussed in the study are data quality, complexity of computation, and privacy
Public Perception and Confidence: How Workforce Attitudes towards AI Influence Willingness to Engage in Upskilling or Reskilling Initiatives
The rapid adoption of artificial intelligence (AI) in industries has made the question of whether the workforce is prepared to respond to the changes through upskilling and reskilling increasingly controversial. The paper discusses the impact of general opinion and trust in AI, as well as trust in the workforce, on readiness to participate in skill development programs. In light of post-pandemic dynamics of digital transformation, the research synthesizes knowledge from organizational psychology, labor economics, and the theory of technology acceptance to investigate the impact of affective and cognitive trust in AI systems on reskilling behavioral intentions. It is suggested that a conceptual framework be used to connect AI perception, confidence, and participation in learning programs as interdependent variables, mediated by organizational communication and national digital maturity. The discussion shows that a positive attitude towards AI and a sense of control over technological change are motivating upskilling. Meanwhile, unemployment and a lack of confidence in AI governance are detrimental to participation, driven by fear of being replaced by machines. The paper ends with policy suggestions that include providing inclusive digital education, communicating clearly about AI, and mechanisms to build employer-capable confidence to enhance societal preparedness for an AI-enhanced economy
Cloud Microservices for Secure Health Data Access
Wellness systems are rapidly adopting cloud-native designs to increase the proportion of scalable, standardized, and high-performance applications. This enables them to quickly deploy applications that allow patients and employees to integrate multiple services or data pipes through well-defined functions easily. Computed using the ideas and principles that emerged as a result of the First World War, life-prolonging electronic medical interventions have been on the rise. However, this might also pose a significant danger to the patient’s private data. For that reason, this paper investigates the several patterns and mechanisms that should be installed in microservices to guarantee that the data is safe from outside manipulations. The regulatory requirements (HIPAA), the most appropriate API software used throughout the industry of cloud management systems (HCR), the service level standards (FHIR /SMART), how the services keep their messages secret (service mesh, mutual TLS), and finally, the security practice quality ideas (in transit and at rest encryption, trust certificate placement) have all been reviewed and compiled in this report. A step-by-step procedure to start implementing this method to secure a health-based cloud service has been included, along with the relevant proposed assessment. Furthermore, regarding the practicality of adhering to the prescribed guidelines, it’s essential to consider additional details alongside container management
Intelligent Network Traffic Identification Based on Advanced Machine Learning Approaches
Network Traffic Analysis (NTA) refers to the process whereby the traffic of the network is logged and analyzed, to identify security risks or performance issues. There are, however, instances when machine learning (ML) is applied in the mechanization of NTA. Network information may be categorized, anomalies detected, and malicious actions identified using machine learning. They could also be employed in foreseeing the same traffic patterns in the future, which can be exploited and utilized to improve network performance. The article gives a recurrent neural network (RNN)-based intrusion detection system (IDS) on the NSL-KDD dataset. The methodology consists of a lengthy preprocessing stage, which involves treating missing values, performing dimensionality reduction, applying one-hot encoding, normalizing the data, selecting features, as well as dividing the training and test sets. The RNN is then trained to identify sequential dependencies in network traffic, enabling it to distinguish between malicious and legitimate activities. These experimental findings show that the suggested model has the following values: F1-score of 99.97%, accuracy of 99.98%, precision of 99.98%, and recall of 99.99% when compared with more traditional ML models like Naive Bayes (NB) and Support Vector Machine (SVM). These findings prove that RNN is efficient and applicable to skewed classes and multidimensional time series patterns in network intrusion detection. The study also identifies the possibility that deep learning (DL) solutions can be used to scale up IDS and enhance its accuracy in real-world network systems
Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen
In the dynamic and ever-evolving realm of computer networks, cloud computing has emerged as a major player. Cloud networks provide on-demand access to pooled resources, but anomalies can compromise their security and integrity. Recognizing anomalies in network traffic is crucial for data security in modern networked systems including the Internet of Things (IoT). This research introduces a novel anomaly detection technique that utilizes the CICIDS-2017 dataset, the Random Forest (RF) classifier, and machine learning (ML). Using the Synthetic Minority Over-Sampling Technique (SMOTE), class balancing is accomplished. Some of the data pre-processing procedures included in the suggested methodology are one-hot encoding, feature selection, handling missing data, and z-score normalization. Two datasets are created from the processed data: one for training and another for testing. Using the Random Forest model, both normal and harmful traffic patterns can be identified. The experimental findings demonstrate that the model achieves a high level of resilience and reliability in detecting anomalies. It obtains an AUC of 1.00, an F1-score of 99.65%, a recall of 99.31%, a precision of 99.99%, and an accuracy of 99.65%. The proposed system offers adequate management of imbalanced and high-dimensional network data, making it applicable in real-world IoT settings. It provides scalability, flexibility, and high detection rates
Goal-Oriented Semantic Communication: A Foundational Enabler for Ultra-Reliable Low-Latency Communication in 6G-Enabled Internet of Things
The sixth-generation (6G) wireless networks aim for a shift towards an AI-driven infrastructure, supporting critical Internet of Things (IoT) applications with extraordinary performance requirements. Ultra-Reliable Low-Latency Communication (URLLC) plays a key role in this vision, but its demanding criteria challenge traditional communication principles. This paper argues that Goal-Oriented Semantic Communication (GOSC), a new approach focused on transmitting only the essential information needed for a specific task, is vital for enabling URLLC in 6G. We examine the primary research challenges associated with processing, knowledge sharing, and resource management. A comprehensive, AI-centered architecture with a dedicated semantic layer is proposed to address these issues. Through case studies in industrial automation, autonomous vehicles, and remote healthcare, we demonstrate GOSC's potential to significantly reduce data loads while improving task success. The paper concludes with future research directions, including standardization, security, and developing a complete theoretical foundation for goal-driven information
Software Testing and Quality Assurance (QA) underpin dependable, secure, and usable software. This paper presents an industry-grounded view of testing processes, from requirements analysis through closure, describes tools and frameworks that enable continuous verification, and reports practical metrics and results observed in agile/DevOps environments. We model a modern automation framework integrated with CI/CD and quantify quality gains using normalized delivery indices. We then examine ethical considerations, including fairness, transparency, and accountability in testing practices. The results indicate that mature automation and disciplined QA governance improve defect discovery, accelerate releases, and stabilize operations. We conclude with a roadmap for AI-assisted, self-healing testing and risk-driven quality budgets
Data Products as APIs: Building Self-Service Access to Trusted Financial Data with GraphQL
Today's financial industry needs to secure, flexible, reliable and immediate access to vast quantities of intricate information. Exposing data products as APIs is a powerful methodology to foster self-service access to data across inter- and intra- organizational perimeters. This paper discusses the architecture, security and governance practices, schema design, performance management and operations of financial data products as GraphQL APIs. The design choices and the implementation processes are supported by real-life examples, describing their advantages and their drawbacks. Future prospects are outlined in the fields of AI-based query understanding and optimization, privacy-preserving computations and other innovative practices that will determine the architectural blueprint of next-generation API-based financial data products
Transforming Insight into Action: The Symbiotic Relationship between Big Data Analytics and Data Visualization
In our digital environment, there are both opportunities and challenges associated with the unprecedented growth of data. We will discuss the two areas of investigation (Big Data Analytics and Data Visualization), which are seldom related fields, and both are essential to achieving insights from Big Data. We will discuss the fundamental traits of Big Data, including volume, velocity, and variety, and how data is transitioned from its initial state to data from analytics. We will then discuss the key component that Data Visualization represents as a cognitive tool to make the complex nature of analytics intuitive, readable, and actionable. This presentation will argue for the complementary nature of the two fields, further supporting the notion that this is not optional, but mandatory, given the nature of Big Data Analytics in relation to science, commerce, and government. We will focus on the potential of the lifecycle of data, from collection to visualization, data use, embedded analytics, and the ethical conceptualization of challenges, as well as a future forecast of system technology that will impact the reestablishment of our knowledge and knowledge practices
The AI-Augmented Data Engineer: How LLMs and Copilots are Redefining the Engineering Workflow
This paper examines how Large Language Models (LLMs) and AI copilots such as GitHub Copilot and ChatGPT are transforming the role of the modern data engineer. By automating routine tasks like code generation, debugging, documentation, and query optimization, these tools allow engineers to focus on higher-level architectural decisions, innovation, and collaboration. The paper explores current impacts, potential risks, and future opportunities of adopting Aico pilots within enterprise data engineering workflows, supported by research insights, productivity studies, and real-world use cases
Beyond Autocomplete: Investigating the Impact of AI Code Generation on Code Quality and Technical Debt
The focus of the study has been on understanding the impact of AI code generation on code quality and technical debt. It has been observed in most cases that the speed of generating codes with the help of artificial intelligence has increased, and it is mostly effective for short works. In larger and complex analyses, there can be a chance of higher redundancy and overhead hosts because of the constant supervision and proper checks that are required. In different areas, the application of AI in generating codes can increase the technical test, the quality of the model, and the architecture is not properly aligned. However, it has also been identified that the proper implementation of AI models and techniques can eventually help in reducing technical debt
Holistic virtualization strategy for balancing risks and costs - Executive and practitioner combined approach
Virtualization technologies have brought various challenges over the past few years. All of IT infrastructure is being scrutinized whether it is for a large company, or, a large Managed Service Provider serving IT needs for multiple customers. While emergence of cloud is one of the reasons which is spread over multiple years, VMWare acquisition followed by licensing reset has turned out to be the biggest change driver. However, cost reduction needs to be carefully approached as security, supportability, and supply chain risks must be considered along with regulatory compliance. This article gives a framework for economic and technology buyers and practitioners to approach the buying and deployment decisions to focus on cost reduction while diversifying risk
Model Evaluation Beyond AUC: A Comparative Study of Somers’ D, Log Loss, Population Stability Index (PSI), and Kolmogorov–Smirnov (KS) Statistic in Credit Risk and Healthcare Prediction Models
The Area Under the Receiver Operating Characteristic Curve (AUC) is the dominant evaluation metric in machine learning classification. However, AUC alone cannot capture important properties such as calibration, stability, and practical separability at thresholds. This paper presents an empirical comparison of AUC with Somers’ D, the Kolmogorov–Smirnov (KS) statistic, Log Loss, and the Population Stability Index (PSI) across three benchmark datasets: (1) the Breast Cancer dataset from scikit-learn, (2) the Heart Failure dataset from Kaggle, and (3) the Lending dataset from Kaggle. Our results show that for the Cancer dataset, Logistic Regression achieves near-perfect discrimination (AUC = 0.999, KS = 0.977) with low log loss and stable PSI, outperforming more complex models. In the Heart dataset, Gradient Boosting offers the best balance between discrimination (AUC = 0.943, KS = 0.784) and stability (PSI = 0.076), while Random Forest, though highly accurate, shows instability (PSI = 0.183). In the Lending dataset, all models show modest discrimination (AUC ≈ 0.70), but Logistic Regression and Gradient Boosting offer the best trade-off between simplicity, interpretability, and stability. These findings emphasize the importance of a multi-metric evaluation framework that goes beyond AUC, integrating discrimination, calibration, and stability metrics for trustworthy machine learning in regulated domains such as finance and healthcare
Observability-Driven Serverless Architectures: Intelligent Monitoring for Distributed Microservices
This paper delves into the incorporation of observability-driven strategies within serverless architectures, emphasizing the role of intelligent monitoring in distributed microservices. It investigates the application of AI-driven tracing, predictive telemetry, and proactive healing mechanisms in cloud-native workloads. The study evaluates how these technologies bolster system reliability, performance, and scalability in serverless settings. It identifies and addresses key challenges in implementing observability in distributed systems. The research proposes a novel framework for real-time monitoring and automated issue resolution in serverless architectures. Experimental findings reveal notable improvements in system uptime, resource utilization, and incident response times. The paper concludes by reflecting on the implications of these findings for the evolution of cloud computing and microservices management
Enhancing Time Series Forecasting using DRL with attention-based neural architectures
Time series forecasting (TSF) is key to decision-making in finance, retail, supply chain management, healthcare, climate among others where accurate predictions inform resource allocation, risk management and strategic planning. While traditional statistical models such as ARIMA handle linear dependencies, they fail to capture the nonlinear and multivariate complexities of modern datasets. Deep learning models such as RNNs, LSTMs, GRUs, CNNs, and Transformers, have advanced forecasting accuracy by capturing temporal patterns and cross-variable interactions. However, these models are static and unable to adapt dynamically to regime shifts, shocks or evolving trends once trained. In addressing this gap, deep reinforcement learning (DRL) offers adaptivity by treating forecasting as sequential decision-making where agents iteratively refine predictions through reward feedback. Attention mechanisms further enhance interpretability and accuracy by highlighting critical time steps and features. This white paper critically reviewed DL and DRL models for multivariate TSF and evaluated their application in finance, retail, supply chains, climate forecasting and healthcare using research studies and datasets. Case studies demonstrate that attention-LSTM and Transformer variants outperform traditional deep models while hybrid DRL–DL approaches achieve greater adaptability. A proposed hybrid architecture integrates attention-based forecasting with DRL agents to combine predictive accuracy, adaptive learning, and interpretability. Although challenges on data, model structure and tasks remain, the approach has the potential to transform TSF into adaptive and decision-support systems across domains
Optimizing Direct Marketing For Burial Insurance With Machine Learning
Burial insurance, a niche but vital segment of life insurance, presents persistent marketing challenges due to low consumer awareness, price sensitivity, and heterogeneous response behavior. Despite widespread use of propensity modeling in broader insurance marketing, there is limited published research applying advanced machine learning approaches to burial insurance acquisition campaigns. This study addresses this gap by developing and validating a predictive modeling framework to improve campaign targeting for burial insurance products. Using a representative sample drawn from a production database of approximately 1.3 million consumer records with historical burial insurance response labels and enriched demographic, lifestyle, psychographic, and financial attributes, we identified key predictors of responseincluding low income, high mobility, limited assets, and behavioral markers such as sweepstakes participation. Ensemble models such as Gradient Boosting and XGBoost delivered a strong performance, achieving 1.8x lift in the top decile and capturing 43% of total responders in the top three deciles. These results demonstrate the value of data-driven segmentation and targeting in reducing acquisition costs and improving ROI for burial insurance marketing, with implications for applying machine learning approaches to other underserved, price-sensitive insurance markets
Vendor Alignment and Governance Models for Large-Scale Cloud Infrastructure Initiatives
As organizations increasingly rely on multi-vendor cloud environments, managing alignment, performance, and compliance across providers has become a critical challenge. Traditional IT-governance frameworks such as COBIT and ITIL were not designed to address the interdependence inherent in hyperscale, distributed ecosystems. This paper proposes an integrated Vendor Alignment and Governance Model (VAGM) that unifies strategic, operational, and compliance dimensions into a single governance framework. The research employs a design-science approach, combining literature synthesis, framework analysis, and expert validation to develop a scalable governance structure. The model introduces three componentsthe Vendor Alignment Lifecycle (VAL), Governance Structure Model (GSM), and Vendor Alignment Matrix (VAM)that collectively provide organizations with measurable alignment, accountability, and performance insights
From Bugs to Breaches: How Agentic AI Protects the Software Supply Chain
Modern software supply chains have become highly sophisticated, prospecting vulnerabilities at all points of the development and deployment lifecycle. From CI/CD pipelines to open-source components, security incidents frequently stem less from insufficient information than from inability to respond quickly and discerningly. Advances in Agentic Artificial Intelligence (AI) like autonomous reasoning, planning, and acting systems are transforming the ways in which software supply chains can be made more secure. Differing from passive anomaly discovery approaches using traditional machine learning, agentic AI systems actively discern, rank, and correct threats at the code, dependencies, and infrastructure configuration levels. This paper provides a systematic review of new Research and industry practice applying agentic AI to software protection. The paper explores primary uses for agentic AI in secure code analysis, open-source risk management, and incident response autonomy, while underscoring the technological, ethical, and governing troubles intrinsic to autonomy within security frameworks. Lastly, the paper sketches out future directions and outlines a conceptual roadmap for infusing agentic AI into DevSecOps ecosystems for the realization of proactive, resilient, self-healing, and robust protection for software
Secure Software Development Lifecycle (SDLC)
Homomorphic Encryption for Privacy-Preserving SQL Query Processing in Financial Databases
In this paper, we introduce a novel framework for processing SQL queries directly over encrypted financial data using homomorphic encryption (HE). Our solution preserves data confidentiality without sacrificing essential SQL operations, including SELECT, WHERE, SUM, and AVG, all performed on encrypted columns. Unlike traditional approaches that rely on trusted execution environments or secure multiparty computation, our method ensures that data remains encrypted throughout the entire query lifecycle at rest, in transit, and during computation. We present a prototype implementation and evaluate its performance using realistic financial datasets. The results demonstrate a practical balance between computational overhead and data privacy, offering a promising foundation for secure, privacy-preserving analytics in cloud-based financial systems
Enhancing Organizational Resilience: Innovations in It Governance and Risk Management
Organizations need to develop resilience as their main survival mechanism in today’s unstable (VUCA) world because it directly depends on modernizing their IT governance (ITG) and risk management (ITRM) systems. The paper demonstrates that COBIT and ITIL traditional static frameworks fail to protect against modern cyber threats and fast technological advancements, so organizations need to adopt adaptive intelligent solutions. The study evaluates outdated systems while studying three transformative governance methods, which include Agile principles, Cloud-Native architecture, and automated compliance systems. The research investigates modern risk management approaches by studying Cyber Resilience as a new paradigm, Zero Trust architecture deployment, and AI-based predictive analytics implementation. The research develops a single framework that combines these solutions to create an integrated strategic plan. The proposed approach achieves validation through KPIs and case studies from financial, healthcare, and manufacturing sectors, which demonstrate measurable advantages
Beyond Automation: Why Human-Centered Decision Making Remains Essential in Construction
The construction industry is undergoing a profound digital transformation as artificial intelligence (AI), robotics, and automation reshape traditional workflows. From AI-driven scheduling to robotic layout and computer vision-based safety monitoring, automation now touches nearly every phase of a project’s lifecycle. Yet despite these advances, construction remains a domain where uncertainty, accountability, and ethical decision-making cannot be fully automated. This paper examines the boundary between machine precision and human judgment through a qualitative framework that integrates professional field experience, regulatory standards, and recent academic findings. Using the Human-in-the-Loop (HITL) governance model, it identifies five domains technical, legal, ethical, managerial, and cultural where human oversight remains indispensable. The analysis demonstrates that while automation enhances speed and data accuracy, only human professionals can interpret intent, manage risk, and uphold safety and compliance. The study concludes that the future of construction lies not in full automation but in hybrid intelligence where digital tools inform and accelerate, and human judgment defines meaning, trust, and accountability. This collaboration between human expertise and AI-driven precision represents not the end of craftsmanship, but its evolution
AI-Driven Enterprise Integration: Leveraging MuleSoft, Micro-services, and vibe coding for a Scalable Cloud Ecosystem
The pace at which digital transformation is proceeding has elevated enterprise integration as an indispensable enabler for attaining agility, scalability, and efficiency in cloud-native environments. The traditional monolithic IT architecture does not meet the needs of dynamic traffic, heterogeneous data flows, and real-time business, which causes scattered systems and higher capital expenditure. To tackle these challenges, this paper presents an AI-based e-enterprise integration framework by integrating uncommon technology trends: (i) MuleSoft API-led connectivity, (ii) microservices architecture, and (iii) Vibe coding new paradigm to create a scalable and reliable cloud environment. On the one hand, a reliable middleware platform like MuleSoft for standardless API management and homogeneous system communication; on the other, microservices to decouple application logic for optimal scalability and fault tolerance. Extensive Video-Based Coding on these, vibe coding provides a new AI-powered development paradigm in which natural language prompts, context-aware learning, and iterative human–AI collaboration enable developers to design, deploy, and optimize integration flows faster. This best-of-both-worlds hybrid model not only saves developers’ time but also allows enterprise teams to move from doing the low-level coding work toward orchestration and governance, which is where you have the most leverage in building your app. The paper details an approach to AI augmented coding agents that generate MuleSoft configurations and microservice templates that can then be iteratively refined by developers, creating an environment for collaborative coding, shortening release cycles, and improving code quality. Experimental verification with a case study proves they are faster in deployment and more robust to integration errors, as well as better adaptive to enterprise systems than other traditional schemes. Results demonstrate that vibe coding in MuleSoft-developed microservices contexts can decrease the time to develop integrations by 40%, reduce operational latency by 25% and provide better maintainability. In addition to technical wins, this work elaborates on the organizational effects, such as cultural change, involved with adjusting to AI-augmented workflows, implications for compliance, and governance approaches. Finally, the research presents AI-powered integration with MuleSoft as a rateable and future responsive model for an enterprise cloud caching etiquette powered by microservices and vibe coding
How Blockchain and IoT Can Come Together and Help a Company to Grow
The merging of blockchain and Internet of Things (IoT) represents a state-of-the-art tech evolution capable of reshaping the modern business landscape. While IoT devices toil away to provide real-time data from the physical environment, the blockchain gives an architecture-a decentralized, incorruptible architecture-to store, share, and verify the data without any interference from middlemen. Together, technology-wise, they treat various crucial business challenges such as data integrity, transparency, security, and operational inefficiency. This study is about how the interfacing of blockchain and IoT can bring growth to sectors through smarter supply-chain management, adding security and trust to M2M communications, enhancing regulatory compliance, and fostering predictive maintenance through transparent sharing of data. In this paper, a robust framework describing the synergistic interplay between blockchain and IoT is presented, with a vast literature review, conceptual modeling, and real-world use cases. Therefrom, the study shows the fine points of business processes improving with such integration-commercially speaking-right from real-time logistics to inventory automation, client personalization down to verifiable financial reports. And further, the study goes on to address the principal technical and organizational barriers toward full-fledged adoption, i.e., scalability, latency, and interoperability. This study ultimately shows that companies able to exploit the twin infrastructure of blockchain and IoT do this in a manner that provides a competitive advantage, reduces fraud and inefficiencies, and paves the way for sustainable scaling in an increasingly data-driven world. The findings lead to the conclusion that for organizations to realize these benefits, a strategic and phased integration approach should be undertaken, bolstered with infrastructure investments, workforce development, and governance models tailored to decentralized ecosystems
Real-Time Fraud Detection Using Graph Neural Networks and Federated Learning
Fraud continues to be one of the most pressing threats to financial systems, costing banks and consumers billions annually. Traditional fraud detection methods rely heavily on rule-based engines and static anomaly detection, which struggle against adaptive adversaries and organized fraud rings. This paper introduces a hybrid framework that leverages Graph Neural Networks (GNNs) for relational fraud detection and Federated Learning (FL) for privacy-preserving model collaboration across financial institutions. GNNs enable the identification of hidden connections between accounts, devices, and transactions that traditional models often overlook, making them highly effective against collusive schemes. Federated learning allows institutions to train models jointly without exposing sensitive customer data, aligning with privacy and regulatory requirements such as GDPR and CCPA. We present a reference architecture for federated GNN-based fraud detection, with components for local graph construction, secure aggregation, and global model dissemination. Case studies demonstrate improvements in fraud detection accuracy, reductions in false negatives, and enhanced resilience to evolving attack patterns. Challenges such as communication overhead, interpretability of GNN decisions, and cross-border regulatory acceptance are discussed. We also highlight future research directions, including quantum-accelerated graph learning and generative adversarial fraud simulations. Our findings suggest that the combination of GNNs and FL is a transformative step in building real-time, scalable, and secure fraud detection systems for global banking
AI-Augmented Software Architecture: Autonomous Refactoring with Design Pattern Awareness
The maintenance of legacy software systems presents a significant and escalating challenge in software engineering, characterized by high costs, technical debt, and resistance to modernization. This paper introduces an innova- tive AI-augmented framework for the autonomous refactoring of these systems. Our approach is uniquely centered on the identification of architectural anti-patterns, or ”code smells,” and the subsequent application of appropriate, well-established design patterns to resolve them. The core of our contribution is a hybrid AI model that synergizes Graph Neural Networks (GNNs) for structural code analysis and Transformer-based language models for semantic understanding and code gener- ation. To facilitate collaborative model improvement without compromising proprietary codebases, we propose a novel fed- erated learning framework. This framework is underpinned by a trust metric system that ensures integrity and accountability by weighting contributions from participating silos based on their performance, data distribution, and historical reliability. Furthermore, we address the critical trade-off between model performance and the need for human-understandable outputs by introducing a methodology to quantify and optimize the balance between refactoring efficacy and explainability. We present a comprehensive experimental design and a discussion of hypothetical results, demonstrating our framework’s potential to significantly reduce cyclomatic complexity and improve software maintainability metrics compared to traditional and baseline automated refactoring tools. Our work charts a course toward more intelligent, secure, and transparent software maintenance paradigms
Data Encryption Strategies for Securing Financial Transactions in Cloud Data Warehouses
The adoption of cloud data warehouses has transformed the financial services industry in the United States, enabling banks to process and analyze petabytes of transactional data in near real time. However, this shift also introduces significant risks related to confidentiality, integrity, and availability of sensitive financial data. Encryption—both at rest and in transit—remains a cornerstone of secure architecture, while virtualization adds an additional layer of protection by isolating workloads and enabling trusted execution environments. This paper presents an in-depth analysis of data encryption strategies specifically tailored for U.S. banks leveraging cloud warehouses such as Amazon Redshift, Snowflake, and Google BigQuery. We examine regulatory drivers including the Gramm-Leach-Bliley Act (GLBA), Federal Financial Institutions Examination Council (FFIEC) guidelines, and PCI DSS standards, and map them to encryption requirements. Our findings integrate theoretical insights with practical results from a case study involving a U.S. commercial bank migrating its Teradata warehouse to Snowflake on AWS. Experimental results demonstrate that encryption overheads remain within acceptable limits (<7% latency increase), while virtualization-based trusted execution environments (Intel SGX enclaves) secure fraud detection models with negligible leakage risk. We argue that the combination of encryption and virtualization forms a dual-pillar strategy for ensuring resilient, compliant, and confidential processing of financial transactions in cloud data warehouses
Generative AI in P&C: Transforming Claims and Customer Service
Generative Artificial Intelligence (GenAI) is rapidly becoming a breakthrough in the sphere of property and casualty (P&C) insurance on two levels: claims and customer support. The current paper discusses how the idea of generative models and other AI-related approaches (large language models, multimodal generation, retrieval-augmented generation, etc.) can complement, automate, and redesign the steps in the claims lifecycle and customer interaction process. We introduce a GenAI infrastructure and approach into main P&C operations, and run emulated deployments of GenAI in claim acquisition, document processing, fraud detection, settlement bargaining, and chat-based customer support. We found that GenAI can save on turnaround time (reduction by approximately 3050) and loss adjustment costs (reduction by approximately 20) and will notice a significant increase in customer satisfaction rates. Risks, regulatory constraints, interpretability of the model and challenges of deployment are also discussed. Lastly, we discuss areas to explore in the future, such as federated learning among insurers, generation that is contract aware, multimodal damage evaluation, and trust architectures. The evidence established that generative AI can revolutionize P&C claims and service on a large scale, though it is important to consider human controls, ethical boundary, and domain adaptation
Real-Time Performance Monitoring with AI
Real-time performance monitoring (RTPM) has become an important element in improving the efficiency of working of modern computing systems and industrial processes. RTPM has undergone dramatic improvements with the introduction of artificial intelligence (AI), since it is now able to do predictive analytics, anomaly detection, and adaptive performance tuning. This essay discusses the concept of AI-based RTPM systems that describe their designs, procedures, and utilization in various fields of application. Our research is on machine learning and deep learning mechanisms to keep track of system performance indicators, such as CPU usage, network delays, memory usage, and process industry variables. AI integration can perform monitoring of the performances which were done manually on the basis of the manual analysis before, so, the response time becomes shorter and the accuracy increases. We also talk about the difficulties with the implementation of AI-based RTPM, including non-homogeneity of the data, related real-time processing limitations, and interpretability of the AI models. In addition, the paper provides a comparative discussion between the standard monitoring systems and AI-based methods and shows that the latter performs significantly better in terms of prediction quality and their adaptability. The use of AI in RTPM systems has been proven to improve operational productivity and minimize downtime by large-scale simulations and case studies. Lastly, future research and development suggestions in AI-enabled performance monitoring are given that require robust, scalable and explainable AI models to support increased needs of dynamic operational environments
Query Optimization Using Machine Learning
Traditional database optimizers rely on hand-crafted heuristics and cost models that assume predicate independence, uniform distributions, and stable runtime conditions. These assumptions are generally flawed on contemporary, heterogeneous workloads deep join trees, correlated attributes, UDFs, and elastic cloud resources that give rise to cascading cardinality errors, ineffective plan decisions, and inflated tail latencies. Machine learning (ML) offers it as an alternative based on the data. Report on and synthesize ML methods that enhance three optimizer layers: (i) predictive (learned cardinality estimators, neural cost models, plan-time latency predictors), (ii) decision (reinforcement learning to join ordering, operator selection, and knob tuning), and (iii) control (bandits to online adaptation, uncertainty-sensitive pruning, and rollback guardrails) in this paper. Present feature representations of SQL/ASTs, logical/physical plan DAGS, and operator-level sketches; contrast offline training on past logs with online continual learning; and study robustness to distribution shift and drift. Analytical and mixed workloads Empirical data using both analytical and mixed workloads indicate steady double-digit decrees in end-to-end execution time and SLO violations with low overheads in optimization. Another set of engineering concerns find are cold start, query log privacy, and interaction with concurrency control, and integration patterns of reproducibility and outline covering advisory scoring to end-to-end learned optimizers. The paper is finalized with a research agenda that is dedicated to uncertainty-calibrated planning, cross-database transfer, explainability, and cross-tail and standardized benchmarks
Cyber Insurance Evolution: Addressing Ransomware and Supply Chain Risks
The market of cyber insurance has considerably changed due to the rise of the prevalence of ransomware and supply chain cyberattacks. Cyber insurance, which was initially a niche financial tool, has since been a necessary risk transfer tool to businesses in the new digital ecosystem. The paper will focus on the history of cyber insurance with regards to two imminent risks, i.e., ransomware and the compromise of the supply chain. Ransomware attacks have become more numerous and sophisticated due to the presence of organized networks of cybercriminals as part of which they are carried out with the help of sophisticated encryption, affiliate business model and payment in cryptocurrency. Equally, the resiliency of supply chain risks has been characterized by the increasing dependency on third-party suppliers, cloud service providers and software dependencies, which weigh the system vulnerability. In this paper, I will begin by discussing the conceptual framework of cyber insurance, its application as a means of financial recovery and as an incentive to be more secure. The cyber insurance (unlike traditional one), will be forced to continuously incorporate itself into the changing technological environment, regulatory forces, and the constantly varied threat environment. In a bid to remain solvent, insurers are heading towards dynamic risk assessment, actuarial models and real time threat intelligence. The paper describes how ransomware has led to reshaping the policy architecture by insurers. Coverage limitations, higher premiums, exclusion, and proactive requirements such as mandatory multi-factor authentication (MFA), endpoint detection and response (EDR) systems have also been covered. Similarly, cases of breaches of the supply chain, the most notable, SolarWinds breach and Kasey ransomware attack, are demonstrations of the devastating character of systemic cyber risk. These incidents underscore the difficulty of the modelling of correlated risks when a single compromise is being experienced by thousands of insured entities. In some respects a literature review points us to how academia, industry and regulators have been in some combination affecting cyber insurance. A literature of scholars has shown a flaw of inefficiency with the actuarial models due to lack of historical information and the uncertainty surrounding threat agents. Reportedly, according to industry readings, claims ratios are on the steep climb, and the application of exclusions is taken in respect of the cyber activities sponsored by the state. The regulators have also focused on resilience, and insurers must change policy language and be financially solvent against accumulated losses. The approach that is advanced in this paper establishes a hybrid framework in responding ransomware and supply chain risks in cyber insurance. It combines actuarial modeling, threat intelligence that is qualitative and systemic risk simulation. Our Bayesian inference models are designed to estimate the probability of ransomware claims and Monte Carlo models are used to model dependencies in the supply chain. There is a multi-layered architecture outlined that connects security controls, design of insurance policies and evaluation of claims. The results are that the application of the proactive security requirements in cyber insurance policies is significant in reducing the overall claim rates. In addition, systemic supply chain risk models indicate that reinsurance system and risk sharing among the insurers should be formed in order to minimize the catastrophic exposures. Flowcharts and mathematical form are provided to show the interaction of insurance risks pools, insured companies and risk threats. As mentioned in the discussion the future of cyber insurance does not lie merely in indemnification but also in active co-operation in cyber risk management. Constant watch is becoming in their direction, and the artificial intelligence and the blockchain-based claims verification will be used. They arrive at the conclusion that in the event of improved integration of technology, harmonization of regulations and systemic resiliency modelling, cyber insurance will be a driving force of digital trust