AI-driven auditing leverages artificial intelligence technologies to enhance the efficiency, accuracy, and scope of auditing processes. By utilizing machine learning, natural language processing, and data analytics, AI can automate routine tasks such as data extraction, transaction matching, and anomaly detection. This enables auditors to focus on higher-value activities like risk assessment and decision-making. Recent technology trends include the integration of AI with blockchain for immutable audit trails, real-time continuous auditing through advanced sensors and IoT devices, and the use of predictive analytics to identify potential compliance issues before they arise. Additionally, cloud computing facilitates scalable data storage and processing, while advanced visualization tools improve the interpretation of complex audit data. In environmental regulation, AI-driven auditing aids in monitoring compliance with environmental standards by analyzing vast datasets from emissions, waste management, and resource usage. This technological evolution supports more proactive regulatory enforcement, reduces human error, and promotes transparency. Overall, AI-driven auditing represents a transformative shift towards smarter, faster, and more reliable auditing practices aligned with contemporary technological advancements.