The field of cybersecurity is rapidly evolving with the integration of Large Language Models (LLMs). Recent research has focused on leveraging LLMs to enhance cybersecurity incident investigation, decision support, and threat assessment. The use of LLMs has shown significant potential in detecting adversarial attacks, analyzing complex attack patterns, and predicting threats. Notably, LLMs have been used to investigate cybersecurity incidents in latest-generation wireless networks, with fine-tuning of large language models achieving high precision and recall rates. Additionally, LLMs have been applied to designing reliable lateral movement detectors, network intent management, and cybersecurity compliance verification. The emergence of graph foundation models has also enabled the efficient processing of network traffic captures and binary executables, further expanding the capabilities of LLMs in cybersecurity. Furthermore, research has explored the impact of AI on the cyber offense-defense balance, highlighting the multifaceted nature of the cyber domain and the need for nuanced understanding of AI's effects. Overall, the integration of LLMs in cybersecurity is transforming the field, enabling more effective and efficient threat detection, analysis, and mitigation. Noteworthy papers in this area include: Investigating cybersecurity incidents using large language models in latest-generation wireless networks, which demonstrated the effectiveness of fine-tuning large language models for detecting adversarial attacks. Designing a reliable lateral movement detector using a graph foundation model, which showcased the potential of graph foundation models in cybersecurity. Towards End-to-End Network Intent Management with Large Language Models, which explored the application of LLMs in network intent management and demonstrated their capability in generating network configurations.