Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The insurance sector faces mounting challenges from silent cyber risk—hidden exposures in traditional policies where cyber coverage remains neither explicitly granted nor excluded. This article presents an AI-powered framework for quantifying these elusive risks across global insurance portfolios. Advanced natural language processing analyzes unstructured policy documents to extract cyber-relevant clauses, while generative modeling creates realistic silent cyber scenarios mapped to specific exposures. Monte Carlo simulation techniques map frequency distributions, severity patterns, and risk concentrations across business sectors and regions. Insurance professionals apply this framework for regulatory stress testing, treaty-level aggregation analysis, and evidence-based underwriting. Its value lies in transforming ambiguous policy language into measurable risk parameters. This contribution addresses the urgent need for structured methodologies in cyber insurance analytics, giving insurers tools to identify blind spots, strengthen solvency planning, and meet regulatory requirements. As digital transformation accelerates, the framework advances the management of this challenging aspect of contemporary insurance risk. This framework has been referenced by regulatory agencies and adopted within reinsurance modeling workflows, demonstrating its practical impact and significance in addressing an unresolved global insurance challenge.