Industrial information systems leveraging web technologies, ISOWT, face escalating security challenges, particularly in critical sectors like energy. Traditional qualitative assessments often fail to provide actionable, real-time insights for managing complex, dynamic threats. This paper introduces a novel security index for evaluating ISOWT in industrial organizations, integrating fuzzy logic, metric-based evaluations, fuzzy Markov chains, and multi-agent systems. The index quantifies deviations from an ideal "center of safety," enabling early risk detection and proactive mitigation. Validated through case studies on Syria’s energy sector systems—namely, the Ministry of Electricity website and Mahrukat fuel management system—the methodology achieved significant improvements, including a 45.9–58.5% increase in the security index, 56.9–60.3% reduction in page load times, and 78.3–82.4% decrease in vulnerabilities. Compared to existing methods, this approach offers superior quantitative precision, real-time monitoring, and predictive capabilities. This scalable, automated framework addresses critical gaps in ISOWT security assessment, providing a robust tool for enhancing system resilience. Its adaptability makes it applicable across diverse industrial contexts, contributing to advanced cybersecurity practices for critical infrastructure. Future work will focus on integrating advanced technologies, expanding applications to other sectors, developing adaptive fuzzy models, addressing human factors, and enhancing visualization capabilities. These advancements will further strengthen the methodology's impact and address the evolving security challenges faced by industrial organizations in an increasingly connected world.