P&C insurers are increasingly pressured to identify and effectively predict risk. While traditional methods, such as actuarial models and manual assessments, are effective for identifying patterns in large-scale policy and claims data, they struggle to capture complex patterns, like resistance curves. This paper examines how predictive risk modelling can be implemented in practice using Guidewire DataHub and Power BI Embedded Analytics. Power BI is used for interactive visualization and real-time decision support, whereas Guidewire Data Hub is utilized as a centralized platform for storing and managing structured insurance data. It utilized structured data from claim history, underwriting attributes, policy details, and customer profiles to build a predictive model. Machine learning algorithms, such as Random Forest and Logistic Regression, were then applied to classify policyholders as High, Medium, or Low risk after preprocessing and feature selection. Standard metrics (accuracy, precision, recall, ROC-AUC) were used to evaluate model performance. The Random Forest classifier achieves an accuracy of 84% and identifies high-risk profiles most effectively. It then integrated these predictions with Power BI dashboards, allowing underwriters and analysts to explore risk at both the individual and portfolio levels. The study illustrates how building data platforms that integrate machine learning and embedded analytics facilitates more innovative underwriting, fraud detection and pricing. In a competitive, data-driven insurance environment, the ability to turn raw insurance data into actionable insights provides significant operational and strategic value.

The integration of large language models (LLMs) into mobile applications introduces new vectors for security and privacy vulnerabilities. This study proposes an automated framework for systematically testing LLM-enabled mobile apps, focusing on identifying potential threats such as prompt injection, data leakage, unauthorized access, and adversarial manipulation. The approach combines dynamic analysis, static code inspection, and machine learning-based anomaly detection to evaluate app behaviors in real-time. Our method ensures scalability and efficiency across diverse mobile platforms and LLM configurations. Results demonstrate significant improvements in detection rates and response times compared to conventional manual testing. This work aims to bridge the gap between AI innovation and secure mobile deployment, promoting trust in AI-integrated ecosystems.

Cloud platforms are now central to the management of healthcare supply chains, offering faster information exchange, scalability, and improved coordination. Yet, the very features that make them attractive also bring new privacy and security risks. Sensitive data such as patient records, procurement transactions, and shipment details move across a web of hospitals, suppliers, distributors, and service providers. This complexity raises concerns over unauthorized access, data leaks, and compliance with regulations like HIPAA and GDPR, particularly when information crosses national borders.

Common problems remain stubbornly persistent misconfigured storage systems, uneven use of encryption, and overly broad access privileges have been implicated in several breaches. Traditional safeguards, while helpful, are often too rigid for rapidly changing supply chain environments. More advanced ideas, including blockchain audit logs and federated identity systems, show potential but still face barriers in scale, integration, and oversight.

The pandemic made these tensions visible: cloud tools helped organizations track protective equipment and medicines in real time, but incidents such as the ransomware attack on Ireland’s Health Service Executive also exposed how vulnerable these systems can be.

In response, this paper puts forward a multi-layer privacy framework tailored to cloud- based healthcare supply chains. It combines encryption, adaptive access controls, blockchain-enabled transparency, and automated compliance monitoring. Findings from the study indicate that this layered approach reduces exposure to breaches, improves accountability across stakeholders, and strengthens alignment with regulatory standards. Taken together, the framework balances operational efficiency with the trust and confidentiality that healthcare delivery depends upon.

Purpose: The migration to microservices has substantially enhanced agility but simultaneously introduced critical complexities in managing inter-service communication and maintaining system-wide reliability. Traditional integration and End-to-End (E2E) testing are often too slow, brittle, and resource-intensive for continuous delivery pipelines. This article systematically examines Consumer-Driven Contract (CDC) testing, specifically through the implementation of the PACT framework, as a paradigm-shifting solution to assure inter-service reliability in distributed architectures.

Methodology: This paper develops a comprehensive theoretical framework for CDC and analyzes its technical workflow, focusing on artifact generation, mock service behavior, and provider verification. A conceptual comparative analysis is employed to contrast the PACT-based CDC approach against conventional testing strategies, utilizing key performance indicators such as pipeline execution time, integration defect leakage, and deployment frequency as metrics for success. We conduct a deep dive into the systemic role of the Pact Broker in governing contract evolution and enabling organizational autonomy.

Findings: CDC testing, particularly when formalized with PACT, is associated with a significant shift-left of integration defect discovery, leading to demonstrably faster and more stable CI/CD pipelines. The mechanism facilitates the independent evolution of services by establishing a machine-readable, shared understanding of the API interface. Crucially, the approach mitigates the coordination overhead and infrastructural cost associated with E2E environments.

Originality: This work contributes an academic synthesis of PACT's integration into the full development lifecycle, highlighting its capacity to enable high-velocity, high-assurance distributed systems, and identifying key future research avenues, including the integration of AI-driven tools.

Purpose: This article examines the architectural paradigm shift initiated by the service mesh in cloud-native microservices environments, focusing on its efficacy in enhancing system performance, security, and operational capability. The study provides a comparative analysis of prominent mesh implementations, specifically addressing literature gaps concerning resource overhead mitigation and operational complexity in advanced topologies.

Methodology: A conceptual and technical comparative analysis framework is employed, detailing the separation of concerns between the Data Plane (sidecar proxy) and the Control Plane (policy and configuration). The methodology conceptually evaluates the trade-offs introduced by leading meshes (e.g., Istio and Linkerd) across key functional areas: traffic management, mTLS-based Zero Trust security, and distributed observability.

Findings: The service mesh is demonstrated to be a foundational enabler of sophisticated resilience and security, particularly in implementing Zero Trust principles, which is associated with a reported reduction in successful lateral movement attacks. However, this infrastructure layer introduces significant performance overhead and operational complexity. Future architectural evolution is strongly associated with mitigation strategies, including kernel-level optimization (eBPF) and the movement toward multi-mesh federation to support large-scale, heterogeneous, and geographically distributed deployments. The increase in control plane development focusing on multi-cluster features confirms this trajectory.

Originality: This work synthesizes current technological trends and academic gaps, identifying the critical need for formal security verification and AI/ML-driven solutions to manage the cognitive load of mesh telemetry.