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BizDataX vs. Mage™ Static Data Masking

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
BizDataX
Score 0.0 out of 10
Enterprise companies (1,001+ employees)
BizDataX is a tool specifically designed to mask sensitive user data. It allows IT departments to create databases for non-production purposes, such as testing, product development, marketing, training, and other secondary environments. Thanks to the functions within the tool, users can create secondary data that is compliant with data protection policies such as GDPR, PCI DSS, HIPAA, etc. The tool itself provides complete data masking support and various core functionalities for different…N/A
Mage™ Static Data Masking
Score 0.0 out of 10
N/A
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.N/A
Pricing
BizDataXMage™ Static Data Masking
Editions & Modules
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Offerings
Pricing Offerings
BizDataXMage™ Static Data Masking
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup Fee$7,000 per databaseOptional
Additional DetailsThe BizDataX currently charges licenses depending on the database size - small (up to 500 Gb), medium (up to 1Tb), large (more than 1Tb) and corporate (unlimited database size and quantity). All features are included in the prices of license. The exact price of the implementation depends on the additional services and other factors that are defined directly with our team.—
More Pricing Information
Best Alternatives
BizDataXMage™ Static Data Masking
Small Businesses

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Medium-sized Companies

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Enterprises
Delphix
Delphix
Score 9.2 out of 10
Delphix
Delphix
Score 9.2 out of 10
All AlternativesView all alternativesView all alternatives
User Testimonials
BizDataXMage™ Static Data Masking
ScreenShots

BizDataX Screenshots

Screenshot of Data specification panelScreenshot of Sensitive data discovery screenScreenshot of Example of an execution report

Mage™ Static Data Masking Screenshots

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.