In the digital era, organizations routinely process large volumes of confidential information, includingpersonalidentifiers, financial credentials, and healthcare data. Safeguarding this information without compromising its analytical valuehas become a crucial challenge. Conventional anonymization methods such as redaction or suppression often reduce datautilityor fail to guarantee sufficient privacy. This paper presents a Policy-Driven Smart Data Anonymization Frameworkintegratedwith a Secure Credential Vault that ensures both privacy protection and operational usability. The proposed frameworkappliesflexible masking strategies—full, partial, format-preserving, and noise-injection—to protect structured and unstructureddatasets.It features a configurable policy engine for defining reusable anonymization rules adaptable across multiple business domains.An integrated PII Detection Module employs pattern recognition and context-aware text analysis to identify sensitive datasuchasemails, phone numbers, IP addresses, and financial details. The Credential Vault, secured using AES-256 encryption, managespasswords and API keys with encrypted backups and password-strength validation. Designed for offline use, the systemsupportsCSV, Excel, JSON, and log files, offering real-time anonymization previews and compliance-ready audit reports. Theoverallsolution demonstrates an effective balance between data confidentiality, usability, and regulatory compliance fordiverseorganizational and academic environments. Keywords:Data Anonymization, Privacy Preservation, Smart Masking, PII Detection, Encryption, Credential Vault, DataSecurity,Compliance, Policy Engine, Differential Privacy.