Logging is one of the clearest differences between an AI experiment and an AI system that can be governed. Teams need to know what the system saw, what it did, which tools it used, and where human review happened.
Good logging also needs restraint. It should avoid unnecessary personal data capture and focus on the records needed for monitoring, debugging, incident response, and assurance.
DDAI helps organisations define evidence capture that supports governance without turning every AI workflow into an uncontrolled data store.