AI-Driven Threat Intelligence: A Comprehensive Review of Predictive Analytics for Modern Cyber Défense

Abstract

The rapid expansion of digital ecosystems and the increasing sophistication of cyberattacks have pushed organizations to adopt advanced methods for anticipating, identifying, and mitigating threats. Artificial Intelligence (AI)-driven threat intelligence has emerged as a transformative approach for enhancing cyber defense by leveraging machine learning, deep learning, and predictive analytics to extract actionable insights from vast and complex security datasets. This review examines the current landscape of AI-based threat intelligence systems, focusing on their capacity to analyze patterns, forecast attack vectors, identify anomalies, and generate real-time alerts. The study evaluates the evolution of threat intelligence frameworks, the integration of AI in threat detection and response, and the performance of predictive analytics techniques such as supervised classification, clustering, neural networks, and probabilistic modeling. Furthermore, the review discusses key challenges, including data quality, adversarial attacks, automation biases, interpretability limitations, and the need for standardized evaluation protocols. The paper highlights the growing significance of AI-enhanced threat intelligence in proactive cybersecurity and emphasizes the role of predictive analytics in building resilient defense architectures. The findings suggest that future cyber defense models will increasingly rely on hybrid AI systems capable of continuous learning, adaptive decision-making, and context-aware threat prediction.