DataStealth Data Protection is a data security platform designed to protect sensitive data across enterprise environments using configurable, policy-driven rules applied at the individual data-element level. The platform provides three core data protection methodologies: **Tokenization:** Replaces sensitive data with tokens that can be deterministic (same input yields same token) or randomized, reversible or irreversible, and scoped globally, per application, per tenant, or per geography. Tokens are format- and length-preserving for schema compatibility, and support partial tokenization, time-bound expiry, and governed detokenization with audit trails. **Dynamic Data Masking:** Supports static masking for permanent obfuscation, dynamic role-based masking applied on the fly, redaction, partial reveal, generalization/banding, date shifting, hashing, and realistic pseudonym generation. Masking policies can be applied by role, region, device, risk score, or data sensitivity, and work across structured and unstructured sources including nested JSON. **Encryption:** Covers data at rest and in transit with field-level, file/object, and fragment-aware encryption. Supported algorithms include AES-GCM, AES-CBC with HMAC, and format-preserving encryption (FF1/FF3) for PANs and IDs. Envelope encryption, TLS 1.2+, mTLS, and certificate pinning are also supported. The platform operates without requiring application code changes, as tokens and encrypted values preserve the format and behavior of original data. Protection can be permanent, reversible, or time-bound, and continuous monitoring and real-time classification are included. The solution supports on-premises and cloud environments.