Embedding geometry refers to the mathematical structure and spatial arrangement of data representations - called embeddings - within high-dimensional vector spaces, as used in artificial intelligence and machine learning models. These embeddings capture complex relationships and similarities between data points, such as words, images, or user behaviors, by mapping them into geometric spaces where distances and directions reflect meaningful relationships. In cybersecurity, understanding embedding geometry can help analyze how models detect threats, cluster malicious behaviors, or identify anomalies. Misuse or vulnerabilities in embedding geometry may lead to adversarial attacks, where attackers exploit the model's geometric structure to evade detection or manipulate outcomes.