DBSec DBMasker-EnCloud

DBMasker masking products enable automatic identification and management of sensitive data, provide flexible strategies and masking scheme configurations, help enterprises quickly implement sensitive data masking processing, and ensure the effectiveness and availability of data, so that the desensitized data can be safely used in testing, development, analysis, and third-party usage environments. DBMasker supports mainstream databases such as MySQL, Oracle, PostgreSQL, MSSQL, and Informix. It supports multiple mainstream database application platforms such as Windows, Linux, AIX, and Solaris, and provides flexible masking rule configurations and masking rule extensions.

1. Resolve data sharing and exchange security Through DBMasker masking products, sensitive data exported from the production environment to development, testing, BI analysis and other environments is all desensitized, effectively preventing private data from flowing out of the enterprise without masking. Satisfying enterprises must not only protect private data, but also maintain regulatory compliance to meet corporate compliance.
2. Ensure reliable operation of the business DBMasker masking products provide the ability to follow the “guarantee that data can be applied after masking” rules. This is reflected in the product's support in the following six areas: Intelligence. As enterprise data grows and data content continues to be enriched, sensitive data may be scattered across hundreds or thousands of tables and fields, and the management of metadata becomes more burdensome; at the same time, sensitive data extracted from business systems is highly arbitrary in format and organization; therefore, masking products should have a good ability to identify sensitive data, not rely on the definitions of tables and fields in the metadata, but can automatically identify sensitive data and effectively desensitize them based on data characteristics. For example, ID card, address, phone, email, bank card number, license plate number, company name, etc. Reversibility. With the popularity and popularity of data analysis, third-party analysis agencies and internal management teams need to restore analysis results to business data based on desensitized data to form real analysis results. Therefore, it is necessary to provide the ability to restore the data after desensitization in a reversible manner. Repeatability and irreproducibility. Data masking requires the ability to repeatedly desensitize the same data. In different rounds of masking, ensure that the same private data is also the same after masking, so that the data can be effectively correlated in an incremental environment. On the other hand, sometimes due to safety considerations, it is necessary to provide unrepeatable masking capabilities to ensure that the data generated by the same data in different rounds of desensitization is different, thus preventing reverse engineering from restoring data. Data validity. In order to ensure the normal operation of the business test system and analysis system, it is necessary to ensure that the data after desensitization can accurately reflect the business attributes and data distribution characteristics of the original data; information such as name, address, condition, and enterprise name in the original data must still be legible after desensitization; the desensitized data meets the data rules of the business system and can be correctly verified by the data validity of the business system, such as ID number, bank card number verification, birthday data range, valid card issuer information, age and date of birth, etc.; Data integrity. The data needs to be guaranteed to be complete after desensitization, and provides the ability to not change the size of the original data or include invalid information to prevent sensitive data from not meeting the definition of target data, causing it to be stored smoothly. Data correlation. The data after desensitization should meet the data relationship characteristics of the business system and strictly preserve the original data relationships; for example, ID numbers appear in multiple tables, and it is necessary to ensure that these data are the same after being desensitized. Also, for data with a time series relationship, it is necessary to ensure that the original time series can be maintained even after each date is desensitized. Through support for the above six aspects, DBMasker effectively guarantees that the data after desensitization can meet the same business rules as the original data, is fictional data that can represent actual business attributes, and can make users of the desensitized data feel that the data is real from an experiential point of view, thus ultimately ensuring that the use of the desensitized data can guarantee the reliable operation of the business.
3. Automated processing improves desensitization efficiency Through the automatic discovery function of sensitive data, it is possible not only to avoid the extensive work of manually defining sensitive data, but also to ensure that private information is not missed, and new sensitive data fields can be continuously discovered. The system has built-in rich masking algorithms, and suitable masking algorithms are recommended for discovered sensitive data fields. Staff can make minor adjustments or use them directly. The system supports scheduled tasks and can automatically execute masking tasks at a set time. Through the above automated processing, it is possible to reduce a large amount of manual processing, reduce the error rate, and provide technical guarantees for safe desensitization. This product supports BYOL and provides a seven-day free trial license. You can call our technical support hotline at 400 923 1376 for help.