AI-Based Cybersecurity Risk Prediction and Management

Abstract

The sophistication & prevalence of cyber attacks has highlighted the ineffectiveness of traditional reactive cyber security strategies and the need for predictive and intelligent security solutions has grown. This research paper aims to examine the potential of artificial intelligence (AI) in cybersecurity risk prediction and management, and proposes an overall framework of machine learning, threat intelligence, anomaly detection, and automated decision support. The model aims to combine various cybersecurity data sources, such as network traffic, system logs, endpoint telemetry, and threat intelligence feeds, to analyze and detect new attack patterns, anticipate risks, and prioritize security reactions. It integrates data preprocessing, feature engineering, predictive modeling, risk scoring and continuous monitoring to bolster organizations’ cyber resilience, cut response time and boost operational efficiency. The study further explores the importance of explainable AI, enterprise governance and ongoing evaluation of models for transparent, reliable and trustworthy cybersecurity operations. In addition, it explores the implementation issues, including data quality, adversarial attack, model bias, privacy, and scalability, and how these issues can be mitigated. The results validate the potential of AI-enhanced cybersecurity risk prediction, showing how it can shift the focus from reacting to threats to proactively managing risk, enhancing security practices, and providing intelligent insights for proactive measures in today’s digital landscape.