Mage™ Static Data Masking

What is Mage™ Static Data Masking?

Mage™ Static Data Masking (formerly iScramble, MENTIS' Data Anonymization module) protects critical sensitive data in non-product and pre-production environments. iScramble offers users the flexibility to choose the anonymization method per requirements - including encryption, tokenization, and masking techniques - to protect sensitive data in a delicate balance of performance and security.

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Data Masking

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Screenshot of Choose from 60+ different anonymization methods that protect your sensitive data effectively. Maintain referential integrity between applications through anonymization methods that give you consistent results across applications and datastores. Anonymization methods that offer you the best of both worlds in terms of protection and performance. Choose to encrypt, tokenize, or mask the data as per the use case that suits you.

Screenshot of Anonymize your sensitive data using a range of methods that provide adequate security while maintaining data usability. Protect sensitive data across data stores and applications and maintain referential integrity between them. Choose from a variety of NIST-approved encryption and tokenization algorithms in addition to masking to secure your sensitive data. Maintain minimal reversibility risk, thereby complying with rigorous regulations like HIPAA, GDPR, and CCPA.

Screenshot of Choose how you want to secure your sensitive data with a data classification centric anonymization technique. Secure your data across the spectrum, whether it is in-transit, at-rest, or in-use. Provide the best in class security for your sensitive data with NIST approved fips140 algorithm for encryption and tokenization.

Screenshot of 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 the data’s usability. Retain the characteristics of the original data with anonymization techniques that maintain format, length, and context.

Screenshot of Enable adequate anonymization with minimal re-identification risk through the use of MENTIS Identities (patent pending) masking method. Generate a fake dataset similar in characteristics to the original data through fuzzy logic and artificial intelligence, with MENTIS identities. Maintain an anonymized datastore that preserves demographics, gender ratios, age distribution, and the like.

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Screenshot of Choose from 60+ different anonymization methods that protect your sensitive data effectively. Maintain referential integrity between applications through anonymization methods that give you consistent results across applications and datastores. Anonymization methods that offer you the best of both worlds in terms of protection and performance. Choose to encrypt, tokenize, or mask the data as per the use case that suits you.

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Mage™ Static Data Masking

What is Mage™ Static Data Masking?

Categories & Use Cases

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