AI-Driven Auditing and Technical Architecture refers to the integration of artificial intelligence technologies into auditing processes and the underlying system design that supports these capabilities. AI-driven auditing leverages machine learning, natural language processing, and data analytics to automate and enhance the examination of financial records, compliance checks, and risk assessments. This approach improves accuracy, efficiency, and the ability to detect anomalies or fraud by analyzing large datasets in real-time. The technical architecture supporting AI-driven auditing typically includes data ingestion layers, AI models, processing engines, and visualization tools. It involves secure data pipelines that collect and preprocess diverse data sources, cloud or on-premise computational infrastructure for model training and inference, and user interfaces for auditors to interact with insights generated by AI. This architecture must ensure data integrity, privacy, and compliance with regulatory standards. Together, AI-driven auditing and its technical architecture enable organizations to conduct more thorough, timely, and cost-effective audits, enhancing decision-making and regulatory adherence in complex environments.