This is known as " Data Contamination " or " Model Autophagy " . When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as " ground truth, " which causes the model to " Collapse " and lose its ability to accurately reflect the real world. The ISACA AAIA™ manual emphasizes that training data must be " Primary " and " Authentic " to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model ' s logic.