Risk Classification as an Architecture Input
Understand how risk classification serves as a critical architecture input for governing enterprise generative AI systems. Explore how to map risk tiers, oversight steps, and evidence requirements to use cases, data scope, and autonomy levels. Learn to transform classification into operational controls that meet regulatory and production review standards.
The cost model closed with a handoff to governance mapping. It established the system’s cost constraints but did not define what oversight is required for the people and processes the system can affect. Risk classification is a core input to governance for an enterprise GenAI or agentic system. Classification defines the minimum oversight requirements and the evidence artifacts required for deployment and review, regardless of which model vendor is selected, while still accounting for vendor-specific risks and controls.
A use case description explains what the system does and why it is valuable. A risk classification explains who may be affected, the pathways through which harm could occur, and what oversight and auditability controls are required to keep risk within defined tolerance. For this regulated-enterprise agentic assistant, classification is an architecture input. Map the classification directly to the business ...