This project aims to transform how we protect computer systems against sophisticated targeted cyberattacks known as Advanced Persistent Threats (APTs). APTs are stealthy, multi-step attacks that can cause severe data breaches, undermining trust and security in both private and public sectors. Current defenses often suffer from alert fatigue and high false positives, struggle to adapt to constantly evolving threats, and cannot effectively harness rich threat intelligence information and the unique insights of human investigators. This project will harness the emerging power of large language models (LLMs), the critical technology behind recent advances in generative AI, to enhance various stages of cyber threat protection. By leveraging LLMs' ability to understand and generate human language, the project will enable faster and more accurate detection and investigation of threats, better integration of external knowledge, and smarter collaboration between human experts and automated defenses. The outcomes of this work promise to safeguard individual privacy, strengthen national security, and reduce the massive costs of data breaches and cyber incidents. To achieve this, this research will develop an LLM-powered framework for intelligent, knowledge-enhanced, context-aware, and human-inspired cyber threat protection across the full cyber defense lifecycle. The work is organized into three integrated thrusts. Thrust 1 focuses on extracting dynamic threat knowledge from cyber t