The migration of Business Intelligence (BI) systems to the cloud offers substantial advantages in terms of scalability, flexibility, and efficiency. However, this transition raises significant concerns regarding data privacy, especially when sensitive information, such as personal, financial, and confidential business data, is involved. This paper presents an automated solution designed to anonymize sensitive data during cloud migration to ensure compliance with privacy regulations, including GDPR, while maintaining data integrity for BI analysis. We developed a Python-based script that automates the anonymization process, applying techniques such as pseudonymization and Data masking to protect sensitive identifiers such as names, credit card numbers, etc. Through our validation process, enhanced by AI techniques, we confirmed that the anonymized data retains its analytical value, with minimal impact on performance and accuracy and The results confirmed the utility of automated anonymization in ensuring both privacy protection and the effectiveness of cloud-based BI systems which makes this approach reduces the risks associated with manual anonymization while offering a scalable and efficient solution for organizations migrating BI to the cloud.