Abstract :

Cybersecurity risk assessment is critical for modern organizations as cyber threats continue to evolve in scale, sophistication, and impact. Traditional risk assessment techniques are often manual, time consuming, and unable to respond dynamically to real-time vulnerabilities. This work proposes an AI-driven cybersecurity risk assessment platform integrated with a responsive full-stack web application. The system automates data collection, analyzes vulnerabilities, predicts risk severity, and generates mitigation strategies using machine learning techniques. A scalable backend architecture supports continuous monitoring and intelligent decision making. The system provides dashboards, automated alerts, and visual insights for administrators to make informed security decisions. Experimental validation demonstrates improved detection accuracy, reduced assessment time, and enhanced usability in practical environments.