AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions

Abstract

AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide AI implementation in security operations. By addressing the increasing sophistication of cyber threats, AI-driven approaches aim to create a proactive, dynamic cybersecurity posture that can keep up with evolving cyber adversaries.