This paper presents a comprehensive survey on the application of Generative Artificial Intelligence (GAI) in enhancing physical layer security in communication networks. GAI, which includes models like Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), offers significant potential to address security challenges in physical layer communications. Traditional AI methods often struggle with dynamic channel conditions and complex cyber threats, making GAI a promising solution. The paper discusses GAI's roles in communication confidentiality, authentication, availability, resilience, and integrity. It highlights how GAI can improve data reconstruction, detect anomalies, and adapt to changing environments. Future research directions include model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. The survey emphasizes GAI's ability to enhance security through its capacity to learn complex data distributions and generate realistic content. It also addresses the challenges of data sparsity and incompleteness in physical layer security, showcasing GAI's effectiveness in overcoming these limitations. The paper concludes with a detailed analysis of GAI's applications in secure physical layer communications, emphasizing its potential to address emerging challenges in this field.This paper presents a comprehensive survey on the application of Generative Artificial Intelligence (GAI) in enhancing physical layer security in communication networks. GAI, which includes models like Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), offers significant potential to address security challenges in physical layer communications. Traditional AI methods often struggle with dynamic channel conditions and complex cyber threats, making GAI a promising solution. The paper discusses GAI's roles in communication confidentiality, authentication, availability, resilience, and integrity. It highlights how GAI can improve data reconstruction, detect anomalies, and adapt to changing environments. Future research directions include model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. The survey emphasizes GAI's ability to enhance security through its capacity to learn complex data distributions and generate realistic content. It also addresses the challenges of data sparsity and incompleteness in physical layer security, showcasing GAI's effectiveness in overcoming these limitations. The paper concludes with a detailed analysis of GAI's applications in secure physical layer communications, emphasizing its potential to address emerging challenges in this field.