The integration of Large Language Models (LLMs) into various domains is driving significant advancements in privacy management and policy analysis. Researchers are increasingly focusing on leveraging LLMs to automate and enhance privacy threat modeling, policy comprehension, and user-led data minimization. These innovations aim to address the complexities and ethical challenges associated with handling personal data in AI-driven applications. Notably, the development of tools that integrate LLMs with existing frameworks, such as LINDDUN, is streamlining privacy risk identification and prioritization, making the process more efficient and accurate. Additionally, interactive LLM-based agents are being employed to empower users in understanding and managing their privacy, leading to more informed consent and reduced cognitive load. The field is also witnessing advancements in user-facing privacy controls, which offer tangible solutions for navigating privacy trade-offs in conversational agents. These developments collectively underscore a shift towards more user-centric and automated privacy management systems, enhancing transparency and user control in the digital economy.