The emergence of Large Language Models (LLMs) has opened new frontiers in artificial intelligence applications across multiple domains, including cybersecurity. This paper presents a comprehensive review of the role of LLMs in enhancing cyber defense mechanisms, with a particular focus on their effectiveness in identifying, mitigating, and responding to Advanced Persistent Threats (APTs) and other sophisticated cyber-attacks. We explore the integration of LLMs in threat intelligence, anomaly detection, automated incident response, and adversarial behavior analysis. By examining recent advancements, case studies, and state-of-the-art implementations, we highlight the strengths and limitations of current LLM-based approaches. Furthermore, we assess the challenges related to scalability, adversarial robustness, and ethical considerations inherent in deploying LLMs within cybersecurity infrastructures. The review concludes with future research directions, emphasizing the need for hybrid AI systems that combine LLMs with traditional rule-based and statistical methods to provide resilient and adaptive cybersecurity solutions in the face of evolving digital threats.