Cybersecurity Risk Scoring in Professional IoT: Building Transparent, Explainable Risk Models for Industry 5.0 Applications

The article discusses the evolution of explainable, transparent cybersecurity risk models of IoT application in Industry 5.0. The more the IoT systems are implemented in manufacturing and healthcare sectors, the more tough and huge security threats can be. Conventional models of risks can be non-transparent and difficult to explain, which a prerequisite to trusting and making reliable decisions is. The article emphasizes the necessity to implement the use of explainable AI methods to increase the level of trust by cybersecurity analysts in automated threat assessment solutions. The research evidences how these risk models may be used to predict weaknesses, enhance real-time evaluations, and enable dynamic security measures through the use of real-world case studies. The evidence demonstrates that transparent models help not only reduce cyber risks but also make AI systems and humans work together, thus increasing security postures. The study highlights the importance of the strong, explainable cybersecurity-based architecture to realize secure, resilient IoT deployment in the dynamic environment of the Industry 5.0.