The field of AI privacy and regulation is rapidly evolving, with a growing focus on developing innovative solutions to address the unique challenges posed by AI systems. Recent research has highlighted the need for holistic approaches that integrate technical, legal, and social dimensions to ensure trustworthy AI development. Notably, there is a shift towards prioritizing human factors and accountability in AI privacy, recognizing that human error and intentional actions can have significant impacts on data protection. Furthermore, the development of new regulatory frameworks and standards, such as the GDPR, is influencing the way companies approach data governance and privacy. Researchers are also exploring the concept of Facial Privacy and the need for sector-specific rules to regulate algorithmic decision-making. Overall, the field is moving towards a more nuanced understanding of the complex interplay between AI, privacy, and regulation. Noteworthy papers include: The paper introducing Agentic-AI Healthcare presents a promising approach to combining agentic orchestration, multilingual accessibility, and compliance-aware architecture in healthcare applications. The paper on Accountability Capture provides valuable insights into the effects of record-keeping practices on algorithmic oversight and highlights the need for greater attention to the implications of transparency and accountability regimes.