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Auditing GenAI: what "audit-ready" actually means
This is a sample post so the blog section isn't lonely on launch day. Swap it with your own writing — the structure below (short paragraphs, one blockquote, a list) shows off everything the post template can render.
Every organization deploying GenAI eventually says the same sentence: "we need it to be audit-ready." It sounds concrete. It rarely is. Audit-ready is not a state a system reaches; it's a question the system can answer: when someone challenges an output, can you reconstruct why it happened?
Governance frameworks are not evidence
A policy document says what should happen. Evidence shows what did happen. The gap between the two is where audit findings live. For GenAI systems, that gap tends to appear in three places:
- Prompt and version lineage — which prompt template, which model version, which retrieval corpus produced this output?
- Data quality at the boundary — event-driven inputs drift silently; assessments need to run where the data enters, not where it lands.
- Human override trails — the moments people corrected the model are the most informative records you have, and usually the least captured.
If your GenAI system can't explain itself, your audit team ends up doing it — at deposition speed, after the fact.
Start with the question, not the framework
The practical move is to write the challenge questions first — the five things a regulator, a customer, or your own board will ask — and engineer the evidence backwards from there. Frameworks are useful scaffolding, but scaffolding is not the building.