The field of vector databases is moving towards optimizing query performance and access control. Researchers are exploring innovative approaches to improve the efficiency of vector search systems, such as context-aware query grouping and dynamic partitioning. These advancements aim to reduce latency and increase cache efficiency, making vector databases more scalable and secure. Notable papers in this area include CaGR-RAG, which reduces 99th percentile tail latency by up to 51.55%, and HoneyBee, which achieves up to 6x faster query speeds than row-level security with only 1.4x storage increase. Additionally, the development of subspace aggregation queries and indexing techniques is enhancing the ability to manage and query large-scale resources. The use of elastic index selection and global hash tables is also being revisited to improve the performance of label-hybrid search and group aggregation in vector databases.