Protect your sensitive data with our industry-leading static data masking tool. Mage Data’s Static Data Masking is built to balance performance and security, creating a sanitized copy of your database that can be used safely.
- Choose from 60+ different anonymization methods to protect your sensitive data effectively
- Maintain referential integrity between applications for maximum usability, through anonymization methods that give you consistent results across applications and datastores
- Get the best of both worlds, with anonymization methods that perfectly balance protection and performance
- Choose to encrypt, tokenize, or mask the data according to the use case that suits you
- Anonymize your sensitive data using a range of methods that provide adequate security while maintaining data usability
- Meet data compliance requirements with static masking that is irreversible back to the original data
- Protect sensitive data across data stores and applications
- Choose from a variety of NIST-approved encryption and tokenization algorithms in addition to static data masking
- Maintain minimal reversibility risk, thereby complying with rigorous regulations like HIPAA, GDPR, and CCPA
- Choose how you want to secure your sensitive data with a flexible tool that not only has static data masking, but other anonymization techniques as well
- Secure your data across the spectrum, whether it is in-transit, at-rest, or in-use
- Provide best-in-class security for your sensitive data with NIST approved FIPS 140 algorithm for encryption and tokenization
- Implement masking that integrates easily with your replication process, with a choice of in-app or API based execution of anonymization
- Anonymize your data with context preserving techniques that enable you to retain data usability
- Retain the characteristics of the original data with anonymization techniques that maintain format, length, and context
- Enable adequate anonymization with minimal re-identification risk through the use of Mage Identities (patent pending) masking method
- Generate a fake dataset similar to the original data using fuzzy logic and AI (artificial intelligence)
- Maintain an anonymized datastore that preserves demographics, gender ratios, age distribution, and similar items of interest
- Utilize the power of Artificial Intelligence (AI) and Natural Language Processing (NLP) that can understand the context and discover sensitive data in unstructured fields.
- Discover sensitive data even in log files that could otherwise go undiscovered
