Secure data-in-use with the help of our context and format preserving anonymization methods that ensures security of the data while retaining its usability and preserving the data value.
- Anonymize sensitive data using context and format preserving anonymization techniques.
- Retain the value of your data while mitigating risk of sensitive data exposure
- Secure your data using robust anonymization methods that minimizes the risk of re-identification
- Use differential privacy techniques to secure sensitive data while retaining data usability
- Mimic production data in your non-production and testing environments without exposing sensitive data
- Synthetically generate realistic data that is identical to your production data specific to security leaders responsible for maintaining regulatory compliance
- Audit-ready reporting that displays the presence of sensitive data across the enterprise
- Determine the likelihood of the discovered data classification being sensitive data with a scorecard approach, that assigns confidence scores
- Choose from three scan types, namely Sample Scan, Full Scan, and Incremental Scan, with flexible scanning methods
- Scan only the newly added tables/rows/columns after a datastore refresh through incremental scanning
- Anonymize your data through mechanisms that not only preserve the format of the original data, but also retains the context
- Generate realistic data for analytics and testing that replicates production data without the inherent risk of sensitive data exposure
- 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.
- Enable adequate anonymization with minimal re-identification risk using Mage Data’s (patent pending) masking method.
- Generate a fake dataset similar in characteristics to the original data through fuzzy logic and artificial intelligence, with Mage Data’s identities.
- Maintain an anonymized datastore that preserves demographics, gender ratios, age distribution, etc.
