The integration of Artificial Intelligence (AI) and Information Technologies (IT) is increasingly transforming Corporate Social Responsibility (CSR) auditing by improving transparency, efficiency, and analytical capabilities in sustainability reporting. Traditional CSR auditing methods, which rely on manual processes and limited data analysis, often fail to provide timely and accurate insights into organizational social and environmental performance. The adoption of IT systems enables automated data collection, real-time monitoring, and enhanced reporting accuracy, thereby strengthening accountability mechanisms in CSR practices. AI technologies, including machine learning and predictive analytics, allow auditors to detect anomalies, identify hidden patterns, and assess ESG-related risks more effectively. Furthermore, AI-driven auditing tools contribute to improved decision-making and stakeholder engagement by analyzing large volumes of structured and unstructured data. Despite these advancements, significant challenges remain, including ethical concerns, algorithmic bias, and limitations in AI auditability and transparency. The need for continuous auditing frameworks and robust governance systems has become critical to ensure the responsible implementation of AI in CSR auditing processes. Overall, the integration of AI and IT into CSR auditing enhances the reliability, effectiveness, and strategic value of sustainability assessments, contributing to more responsible and sustainable business practices.