Episode 3 — System Architecture & Trust Boundaries
This episode explains the architecture of AI systems, breaking down their stages and components to show how trust boundaries shift across the lifecycle. Training, inference, retrieval-augmented generation (RAG), and agent frameworks are introduced as discrete but interconnected environments, each with distinct risks. For exam relevance, learners are expected to identify these architectural elements, describe where threats occur, and understand how adversaries exploit them. The discussion highlights how traditional security boundaries—such as network segmentation or user authentication—must be re-evaluated when applied to AI. Understanding these system dynamics is crucial for answering exam questions and for analyzing risks in real deployments.
The applied discussion explores how architecture decisions affect overall system resilience. Examples include how training pipelines depend on secure data provenance, how inference APIs expose models to prompt injection or extraction attacks, and how agents connected to tools introduce risks of privilege escalation. The episode emphasizes practical considerations such as monitoring trust boundaries, enforcing least privilege, and mapping dependencies across cloud and on-premises environments. Troubleshooting scenarios illustrate how gaps in architecture create opportunities for attackers, reinforcing why governance of system design is as important as technical controls. By mastering these architectural concepts, learners gain both exam readiness and practical insight into AI security. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
