In the digital era, organizations continuously handle large volumes of sensitive data such as personal identifiers, financial records, healthcare details, and login credentials. Protecting this information is critical, not only to preserveusertrust but also to comply with global privacy regulations including GDPR, HIPAA, and the Indian DPDP Act. Traditionalanonymization techniques, such as simple redaction, often destroy the utility of data and fail to meet the dual requirement ofprivacy and usability. This project proposes the development of a Smart Data Anonymization Tool that uses policy-drivenmasking techniques and an integrated Secure Password Vault to achieve both privacy protection and practical usability. Thesystem supports multiple anonymization strategies, including full masking, partial masking, format-preserving substitution, noise injection for numeric values, and complete field removal. It features a configurable policy engine where userscandefine and reuse rules across diverse datasets, enabling flexible adaptation to different business domains. Thetool isdesigned to work entirely offline and supports multi-format data sources such as CSV, Excel, JSON, and log files. Abuilt-inEnhanced PII Detection System employs pattern recognition and context-aware text analysis to identify emails, phonenumbers, IP addresses, credit cards, bank details, and other personally identifiable information. Sensitive credentialsaresecurely stored in an encrypted Password Vault using AES-256 encryption, ensuring an additional layer of protection. Toenhance usability, the application includes a modern web interface built with Flask and Bootstrap, offering drag-and-dropfile uploads, real-time anonymization previews, and interactive dashboards. The system also generates analyticsandcompliance-ready reports, including before/after anonymization comparisons and detailed audit logs. Designedtorunseamlessly on student laptops without internet dependency, this project demonstrates a complete privacy preservingframework that balances data confidentiality, compliance needs, and operational utility. Keywords: Data Anonymization, Smart Masking, Privacy Preservation, Personally Identifiable Information (PII), DataSecurity,Compliance, Encryption, Password Vault, Policy-Driven Anonymization, Dif erential Privacy