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.