From guarantees to quality margins: the governance shift AI requires

This article is part of Framna's Trust by Design series, exploring what it takes to build trustworthy AI systems beyond the model itself. If you haven't read the previous articles yet, you can find the first one on grounding here, and the second on evaluation here.

The previous articles in this series explored two important layers for reducing AI risk. Grounding connects a model to an organization's own sources, reducing the likelihood of invented information. Evaluation tests outputs through deterministic checks, model-based evaluation and human review.

Together, these layers make quality more visible. They provide signals about where a system performs well, where it falls short and where stronger controls may be needed. Because even with both layers in place, some uncertainty remains.