The hidden gap in AI audit trails: reasoning changes, but records stay flat.
One thing I've noticed with AI audit trails is that they tend to do a good job of recording events but not always the reasoning behind them. Take a simple example: An AI-generated report goes through an internal review before being shared with customers or stakeholders. Someone makes edits, approves the final version, and the workflow moves forward. Months later, if you look back, the audit...
Would you trust an AI output if you could not see who approved it?
Been thinking about this after something that came up recently. Imagine an AI agent makes a recommendation that ends up influencing a customer workflow. The recommendation gets reviewed, approved, and eventually becomes part of how the team operates. Fast forward a few months and someone wants to understand why that decision was made. The interesting part is that the technical history is...
Should AI governance include decision ownership?
Something I've been thinking about recently: Let's say an AI agent makes a recommendation and that recommendation ends up influencing a real business decision. A few months later, someone wants to understand why that decision was made. In most cases, it's not that hard to find the model, the prompt, or the output. Teams are getting much better at tracking those things. What feels harder is...
