Mandiant has put out an interesting blog post on how AI can be used to enhance adversarial emulation. The article focuses on using large language models (LLMs) to analyze unstructured data obtained during adversarial emulation engagements. The article presents several case studies that illustrate how AI can be used to analyze network, user, and domain data to identify potential attack paths. The article also provides examples of how AI can be used to analyze files for credentials, cluster users, and correlate users to their machines. Overall, the article provides valuable insights into how AI can be used to improve both red team and blue team operations. I'm particularly impressed with how the authors used AI to analyze unstructured data. This is a challenging problem that cybersecurity teams have long struggled with, and it seems that AI has the potential to make a big difference in this area. I think this research is important because it shows how AI can be used to improve adversarial emulation. By using AI to analyze unstructured data, cybersecurity teams can more effectively identify potential attack paths. This can help organizations improve their defenses and prevent attacks.