AI-Driven Threat Intelligence Platforms for Real-Time Detection and Mitigation of Advanced Persistent Threats (APTs): A Comprehensive Review

Abstract

Advanced Persistent Threats (APTs) represent a formidable, evolving challenge in contemporary enterprise cybersecurity. Characterized by state sponsored backing, multi-stage attack lifecycles, low-and-slow execution, and Living-off-the-Land (LotL) evasion techniques, APTs consistently bypass signature-based Intrusion Detection Systems (IDS) and traditional Security Information and Event Management (SIEM) tools. To counter these adversaries, AI-driven Threat Intelligence Platforms (TIPs) have emerged as an essential defense framework. This review paper provides a rigorous, journal-level evaluation of AI-driven TIPs engineered for real-time detection, correlation, and automated mitigation of APTs. We examine the integration of machine learning (ML), deep learning (DL), graph neural networks (GNNs), natural language processing (NLP), and cyber threat intelligence (CTI) standards such as STIX/TAXII 2.1. Furthermore, we analyze provenance graph processing, dynamic behavioral feature extraction, and automated Security Orchestration, Automation, and Response (SOAR) playbooks. Synthesizing benchmark evaluations on standard datasets (DARPA Transparent Computing and CIC-APT2020), this paper demonstrates how hybrid GNN-BiLSTM architectures achieve up to 98.4% detection precision while reducing Mean Time to Respond (MTTR) from days to minutes. Finally, we address open research challenges—including adversarial machine learning, explainability in security operations, and privacy-preserving intelligence sharing and propose strategic future directions.

KEYWORDS: Advanced Persistent Threats (APTs), Threat Intelligence Platforms (TIP), Artificial Intelligence, Graph Neural Networks (GNN), Cyber Threat Intelligence (CTI), Real-Time Mitigation, SOAR, Machine Learning.