As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.

Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.

This study addresses the urgent challenge of safeguarding sensitive health data in today’s digital age by proposing a novel, integrated data protection framework that synthesises six critical pillars—technology, policy, cybersecurity, legal frameworks, governance, and risk assessment—into a unified socio-technical model. Unlike existing piecemeal approaches, this framework is designed to bridge the gap between regulatory requirements and practical implementation through measurable, engineering-based solutions. Healthcare organisations face persistent difficulties in aligning innovation with secure and compliant practices due to fragmented governance and reactive cybersecurity measures. This paper aims to empirically validate the effectiveness of the proposed framework by quantitatively analysing causal relationships between its components (such as between governance and compliance) using advanced statistical methods, including exploratory factor analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). A survey of healthcare professionals across multiple countries revealed significant gaps between regulatory expectations and operational realities, underscoring the need for harmonised strategies. The results demonstrate strong causal linkages between governance, cybersecurity practices, and compliance, validating the framework’s robustness. This research contributes to the fields of digital health, information systems, industrial engineering, and electronic governance by offering a scalable, empirically tested model for socio-technical data protection. The findings provide actionable strategies for policymakers, system architects, and digital infrastructure designers.