Unlock the value of data without increasing risk, while also minimizing storage cost. Oracle Data Masking and Subsetting helps organizations achieve secure and cost-effective data provisioning for a variety of scenarios, including test, development, and partner environments.
透過 LiveLabs 引導式研討會使用 Oracle 安全解決方案以獲得實務體驗。
Discover and classify sensitive data to understand risk and aid in designing data protection policies.
Determine referential relationships and create models to protect data integrity during data masking and subsetting operations.
Remove sensitive data from test, development, analytics, and other non-production environments.
Flexible masking techniques preserve data characteristics to support continued application functionality after data masking.
Further reduce the effort required and maximize time to value with pre-built masking templates for Oracle E-Business Suite and Oracle Fusion Applications.
Create smaller data sets to save time and storage costs. Improve efficiency while reducing risk.
Multi-factor subsetting techniques create representative, relevant, and functionally intact data sets.
Eliminate overhead on production by masking in-database, or use in-export masking to eliminate the need for staging environments.
Leverage the power of Oracle Database to minimize the time and resources required to create non-production databases.
Mask and subset data in non-Oracle databases using Oracle Database Gateways, which is always included.
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Thanks to this tool, our cost of creating test data has decreased considerably.
Senior Application Analyst in the Finance Industry
The Gartner Peer Insights reviews constitute the subjective opinions of the individual end users based on their own experiences and do not represent the views of Gartner or its affiliates.
Minimize data exposure in non-production environments by discovering and masking sensitive data and sharing only relevant data.
Leverage data masking and subsetting policies to demonstrate data protection by design and default to auditors.
Safely share realistic, representative, and functionally intact data with relevant stakeholders to fuel test, development, and other initiatives.
Reduce time, storage, and infrastructure costs by extracting and sharing only relevant data.