Supply Chain Security for Tools and Prompts

It’s very easy to talk about secure GenAI. But did you ever think about whether your agents are running only the prompts, tool schemas, router rules, and semantic models you intended — especially after many weeks of rapid iteration? It is very hard to prove this. Most teams freeze application code and container images, but they leave one thing open: the fast-moving agent configuration supply chain.

Prompts and tool definitions change more often in production. Router rules are updated daily. Tool schemas get expanded just to add one field. A semantic model gets tweaked to fix a metric edge case. These types of edits can silently weaken policy controls, enable data-exfiltration paths, or simply reduce faithfulness until you are shipping wrong answers with confidence. For governed, trustworthy AI data systems, you must treat agent configuration like top-tier software artifacts, such as reviewing, signing, versioning, promoting, and auditing, and not like loose text files in a repository.