Following a rigorous methodology is key to delivering customer satisfaction and expanding analytics use cases across the business.
During a Data Security/Data Masking project, rules and policies are established as an essential part of the planning process. If there are several tables (and hence several sensitive columns) involved, browsing through the objects could be troublesome. A wrong assignment of a rule or policy could seriously compromise the security of the data.
A proper naming convention is very important for effectively browsing, using and tagging sensitive fields.
Rules play a key role in masking sensitive data. The following syntax can be used for naming a rule:
Syntax
rul_[Mask]_<EntityName>_[AttributeName]
Where EntityName is the name of an entity. An entity can typically be a single table or a group of related tables and AttributeName is the name of the column that is being masked.
Examples
Note: In the above examples, Last Name (Last_Name) and Address Line 1 (Addr_Line_1) might be attributes in two different tables belonging to “Customer” and are hence named similarly.
A policy is typically a collection of rules. The following syntax can be used for naming a Policy:
Syntax
pol_[Mask]_<EntityName>
Where EntityName is name of an entity. An entity can typically be a single table or a group of related tables.
Examples
A plan is an executable unit. The following syntax can be used for naming a plan:
Syntax
pln_[Mask]_<EntityName>
Where EntityName is name of an entity. An entity can typically be a single table or a group of related tables.
Examples
A project is a workspace where metadata is imported into, and where masking, subset, and data generation artifacts can be built. The following syntax can be used for naming a Project:
Syntax
Proj_<Name of the Application>
Examples
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