AI-Driven Predictive Threat Detection and Cyber Risk Mitigation: A Survey

Abstract

Predictive analytics is revolutionizing cybersecurity and various industries by leveraging artificial intelligence (AI) and machine learning (ML) to enhance threat detection, risk mitigation, and decision-making processes. By enabling a shift from reactive to proactive security strategies, AI-driven predictive models improve the accuracy of cyber threat detection, reduce response times, and strengthen overall resilience against evolving attack vectors. Advanced techniques such as deep learning, anomaly detection, and natural language processing (NLP) enhance the adaptability and precision of these systems. A comprehensive review of existing research highlights key advancements, challenges including data integrity, algorithmic bias, and scalability and ethical concerns related to privacy, fairness, and transparency. Beyond cybersecurity, predictive analytics optimizes efficiency across sectors such as healthcare, finance, manufacturing, and energy, supporting smarter resource allocation and operational improvements. The integration of emerging technologies, including quantum computing, federated learning, and blockchain, further enhances predictive capabilities while ensuring security and compliance. By addressing these aspects, this research provides valuable insights to advance AI-driven predictive analytics, guiding the development of intelligent, ethical, and scalable solutions for a rapidly evolving digital landscape.