AI-Driven Risk Scoring System for Dynamic Cyber Insurance

Today, business processes are going digital at a faster pace. Cyber attacks have become more advanced, causing a tremendous increasing number of financial and operational risks for organizations. Under this situation, the constant cyber insurance underwriting method-static, expert driven and rule-based-always fails to look at how the threat has gotten very dynamic and fast evolving. The mispricing of premiums leads to a reduced transparency and inconsistency in the visibility of risk across insurers and insured entities. In this respect, the paper presents innovative AI-driven risk scoring frameworks adequately tailored for automating and upgrading the assessment of cyber risk in insurance. The framework uses machine learning (ML) techniques to analyze structured cybersecurity attributes such as patch management frequency, authentication strength, incident count, and vulnerability exposure. A Gradient Boosting Regressor (GBR) model, trained on synthesized organizational security data, predicts risk scores on a continuous scale (0-100) and estimates corresponding insurance premiums based on industry category, annual revenue, and security posture. The whole system will be implemented through a Flask based web application enabling real-time interaction, user authentication and automated reporting. Experiments corroborate considerable advances in accuracy and processing speed when comparing with established assessment methods. As expected, these improve performance over most latency issues. The outcome of this research will be in taking insurance frameworks toward becoming data driven, adaptive and transparent in their pricing of premiums in relation to actual risks incurred and costly management strategies in security for the modern enterprise.