AI-Driven Threat Intelligence: A Predictive Analytics Framework for Enhancing Cyber Défense Capabilities

Abstract

The rising frequency, sophistication, and automation of cyberattacks have created an urgent need for advanced security mechanisms capable of anticipating threats rather than merely reacting to them. This study proposes a predictive analytics framework for AI-driven threat intelligence that enhances cyber defense capabilities by leveraging machine learning, deep learning, and behavioral modeling. The framework integrates heterogeneous data sources—such as network telemetry, endpoint logs, malware signatures, and open-source intelligence—to generate actionable insights and early-warning indicators. By utilizing predictive algorithms, anomaly detection techniques, and temporal analysis, the model seeks to identify latent patterns associated with emerging threats, zero-day vulnerabilities, and multi-stage attack campaigns. The study also evaluates architectural components necessary for operationalizing predictive threat intelligence, including data preprocessing pipelines, feature engineering, model training workflows, and automated alerting mechanisms. Furthermore, the paper examines common implementation challenges such as data imbalance, adversarial manipulation, scalability constraints, and the need for real-time processing. The findings underscore the transformative potential of predictive analytics in enabling proactive cybersecurity strategies and strengthening organizational resilience. This framework serves as a conceptual foundation for future research aimed at creating autonomous, adaptive, and trustworthy cyber defense ecosystems capable of evolving alongside the dynamic threat landscape.