data masking वाक्य
उदाहरण वाक्य
मोबाइल
- It is also commonly known as a dynamic data masking method.
- Static Data Masking is done on the golden copy of the database.
- Having on-the-fly data masking in place becomes essential.
- In 2016 the company introduced a data masking solution called Imperva Camouflage.
- Dynamic data masking capabilities prevents sensitive data from being visible by unauthorized users.
- The same technique is applied to " Dynamic Data Masking " but one record at a time.
- In December 2015, Gartner recognized Informatica in its December 2015 Magic Quadrant for Data Masking Technology report.
- This type of data masking is most useful for environments that do continuous deployments as well as for heavily integrated applications.
- On-the-Fly Data Masking happens in the process of transferring data from environment to environment without data touching the disk on its way.
- It also highlights to anyone that wishes to reverse engineer any of the identity data that data masking has been applied to some degree on the data set.
- The overall practice of Data Masking at an organisational level should be tightly coupled with the Test Management Practice and underlying Methodology and should incorporate processes for the distribution of masked test data subsets.
- "' Privacy Analytics Inc . "'is a Canadian technology firm that claims to offer the only commercially available integrated data masking and de-identification product on the market today.
- In most cases the substitution files will need to be fairly extensive so having large substitution datasets as well the ability to apply customised data substitution sets should be a key element of the evaluation criteria for any data masking solution.
- Data masking invariably becomes the part of these processes in SDLC as the development environments'SLAs are usually not as stringent as the production environments'SLAs regardless of whether application is hosted in the cloud or on-premises.
- Production DBAs load the backup in a separate environment, reduce the data set to a subset that holds the data necessary for a particular round of testing ( a technique called " subsetting " ), apply data masking rules while data is in stasis, apply necessary code changes from source control and push data to desired environment.
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