Disaggregation is a growing trend in large-scale artificial intelligence (AI) systems to overcome hardware and software resource limitations and improve performance while preserving security and privacy. This paper takes a closer look at different dimensions of the concept, in AI, security and hardware. We identify two key design patterns that may be combined to build optimized disaggregated AI architectures and discuss benefits and limitations for AI and security. Using a large language model use case, we also highlight some key trade-offs between performance, resource allocation and security for different disaggregation strategies in hardware and in software.