Answers
Continuous monitoring detects anomalies; a continuous audit loop learns from them and retrains the models based on human feedback
It reduces repetitive testing and focuses human effort on interpretation, escalation, and governance
Yes. Start by automating one process - e.g., journal entry anomaly detection - and expand incrementally with feedback mechanisms
Audit cycles used to end when reports were signed. In AI-driven assurance, that’s where learning begins. Every engagement, journal entry, and review comment becomes an input that sharpens the next cycle’s accuracy.
This maturity model shows how each loop compresses the delay between *data → insight → action*
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