In today’s technology-centric era, digital transformation has made accessibility and cybersecurity critical components for developing inclusive web applications that are essential in daily operations. However, current development models often treat accessibility and cybersecurity separately, leading to solutions that may excel in one area while lacking in the other. This paper addresses this gap by proposing the Holistic Security and Accessibility Layered Architecture (HSALA), a novel architectural model that integrates accessibility and cybersecurity throughout the development process. HSALA consists of five interconnected layers, each paired with a corresponding security layer, ensuring that both accessibility and security are embedded at every level. We conduct a theoretical and analytical case study comparing HSALA with the traditional three-layered architecture, demonstrating HSALA’s superiority in addressing contemporary challenges related to accessibility, cybersecurity, scalability, and user experience. The findings indicate that HSALA provides a robust framework for developing secure and inclusive web applications, promoting a more equitable digital ecosystem. We outline a roadmap for future work, including empirical validation and practical implementation strategies. HSALA represents a significant advancement in web application development, fostering inclusivity and trust in an increasingly digital society.

The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions.