This paper develops and validates an architectural approach for continuous auditing and real-time assurance using deep learning methods, with emphasis on practical implementation in corporate environments. Employing design science research methodology combined with action research, the study examines limitations of traditional sample-based auditing in high-volume digital environments and substantiates the transition to full-population monitoring. Central attention is given to deep learning as a structural element enabling anomaly detection, risk-event ranking, and manageable expert review. Implementation across a diversified holding company processing 2.4 million annual transactions demonstrates that practical effectiveness is determined not by maximizing individual model accuracy, but by architectural integration into a unified audit loop comprising data, analytical, and assurance layers with mandatory auditor involvement. Results show cycle time reduction from 45 days to under 72 hours and 60% reallocation of audit resources from manual testing to exception investigation. Institutional constraints including trust, interpretability, and regulatory clarity are examined. The article contributes to internal audit practice and research on AI-enabled assurance systems.