iScramble Description
You have the freedom to choose the anonymization method that best suits your needs - including tokenization, encryption, and masking techniques. This will ensure that sensitive data is protected in a delicate balance between security and performance. You can choose from over 60 different anonymization methods to protect sensitive data. Anonymization methods that provide consistent results across datastores and applications will help you maintain referential integrity. Anonymization methods that provide both performance and protection. You can choose to encrypt, tokenize or mask data depending on the use case. There are many ways to anonymize sensitive data. Each method provides adequate security and data usability. Protect sensitive data across data storage and applications, and maintain referential integrity. You can choose from a range of NIST-approved encryption or tokenization algorithms.
iScramble Alternatives
iMask
iMask protects sensitive data at the Application Layer as well as the Database Layer. It offers flexible solutions that can be used for all types of use and all users, small, medium and large. Mask sensitive data at both the application and database levels to ensure your data's protection in production. You can set up rules in the product UI to allow role-based and user-based access controls that control who can access your sensitive information. You have the option to choose from more than 40 anonymization methods to maintain data consistency between production and nonproduction instances. You can set authorization rules to restrict who can see sensitive data based on geography, roles, departments, and other factors. Secure anonymization protocols can be enabled without affecting performance. Database embedded approach allows data to be deidentified without any changes in the application architecture or security protocols.
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Informatica Persistent Data Masking
While protecting privacy, context, form, as well as integrity, must be retained. Data protection can be enhanced by de-sensitizing sensitive data and de-identifying it. Pseudonymize data for privacy compliance, analytics, and analytics. Obscured data preserves context and referential integrity, so that the masked data is usable in testing, analytics, and support environments. Informatica Persistent data masking is a high-performance, scalable data masking solution that protects confidential data such as credit card numbers and addresses. It creates realistic, de-identified data that can then be shared internally or externally. It can also be used to reduce the risk of data breaches occurring in nonproduction environments, create higher-quality test data, streamline development projects, and comply with data privacy regulations.
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DATPROF
Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.
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IRI Voracity
IRI Voracity is an end-to-end software platform for fast, affordable, and ergonomic data lifecycle management. Voracity speeds, consolidates, and often combines the key activities of data discovery, integration, migration, governance, and analytics in a single pane of glass, built on Eclipse™.
Through its revolutionary convergence of capability and its wide range of job design and runtime options, Voracity bends the multi-tool cost, difficulty, and risk curves away from megavendor ETL packages, disjointed Apache projects, and specialized software. Voracity uniquely delivers the ability to perform data:
* profiling and classification
* searching and risk-scoring
* integration and federation
* migration and replication
* cleansing and enrichment
* validation and unification
* masking and encryption
* reporting and wrangling
* subsetting and testing
Voracity runs on-premise, or in the cloud, on physical or virtual machines, and its runtimes can also be containerized or called from real-time applications or batch jobs.
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Integrations
Company Details
Company:
Mage Data
Year Founded:
2014
Headquarters:
United States
Website:
magedata.ai/platform/mage-static-data-masking/
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Product Details
Platforms
SaaS
Type of Training
Documentation
Live Online
In Person
Customer Support
Phone Support
Online
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