Using AI to Quantify and Continuously Update Cyber Risk Scores across Digital Assets

Abstract

ML models and data-oriented methods are used to ingest huge quantities of system logs, user activities, and threat intelligence in real-time to identify emerging threats and vulnerabilities in systems. AI models are capable of dynamically combining certain factors in a rule-based context, in a deep-learning approach, to produce risk scores that adapt to the environment. Automated systems help mitigate the burden faced by security teams by making them make decisions faster and provide better resource allocation, as well as minimise human mistakes and enhance the resilience of organisations in real-time. This study showcases the effectiveness of AI and machine learning in automating and updating cyber risk scores in real-time. The study results indicated high accuracy in prediction with dynamic changes in response to the vulnerabilities, as well as insights into the sector. These findings show the role of AI in establishing cyber risk management in a proactive, adaptive, data-driven process.