A recent guide has outlined a practical approach to securing artificial intelligence systems, including AI agents, MCP servers, and large language model applications, in production environments. The framework involves a five-layer map of potential attack surfaces, a checklist for identifying misconfigurations, and tools for triaging and hardening systems. Additionally, the guide includes a self-assessment tool to help organizations evaluate their maturity in securing AI systems. This framework matters because it provides a structured approach to addressing the unique security challenges posed by AI systems.