Challenges in Cyber Risk Analysis Using Big Data and AI Models

The fast pace of the cyber threat landscape requires deploying advanced cyber threat modeling techniques based on big data and artificial intelligence (AI) for detection, prediction, and mitigation. With big data analytics, businesses can process large volumes of structured and unstructured data collected from various sources, such as network logs, threat intelligence feeds, and user behavior patterns, to detect anomalies and potential vulnerabilities. On the other hand, AI models, like ML and DL, are making a huge impact by automating threat detection, classifying risks, predicting attack vectors, etc., with a myriad of highly accurate methods. The integration of big data and AI has led to significant advancements in cyber risk analysis and assessment. We consider the difficulties with data quality, model interpretability, and adversarial attacks that exploit AI systems. Moreover, the chapter discusses ongoing implementations, such as intrusion detection systems (IDS), fraud prevention, and automated incident response, showcasing how AI-driven analysis enhances decision-making capability and shortens response times. Though they hold transformative potential, ethical and regulatory considerations like data privacy and algorithmic bias need to be addressed to enable responsible deployment. The study wraps up with future directions, calling for explainable AI (XAI), and federated learning to address collaborative threat analysis and quantum defensive cybersecurity frameworks. Big data and AI, as a force multiplier for proactive cyber risk management, enable organizations to leverage these technologies after immediate crises have passed to better address the increasingly complex threat landscape.