For brands and government agencies, regulating access to SQL databases is essential. Organizations often have legal and professional duties to ensure they protect sensitive data.
That’s where DBHawk’s dynamic data masking (DDM) from Datasparc can help. This technique obscures or hides sensitive information while enabling authorized users to view data required to perform their jobs.
This blog post explains what DBHawk’s dynamic data masking is and the benefits it offers.
What is DBHawk Dynamic Data Masking?
Datasparc’s DBHawk dynamic data masking (DDM) is an database security feature that stops unauthorized users from accessing sensitive data in the database. Organizations and permissions managers in IT security teams can dictate how much access employees or contractors have on a granular level.
SQL Server dynamic data masking arrived in 2016. From that point onwards, users could define masking rules for table columns containing sensitive data, preventing unauthorized users from seeing them.
Conveniently, enabling dynamic data masking has no effects on database entries. Therefore, organizations generally find it easy to use in existing applications. Coding experience is not required, and teams only need to apply new rules to query results.
Today, DBHawk dynamic data masking is available on various databases. Data and IT professionals can configure it using DBHawk’s patented object access control policy. DBAs can choose tables and apply dynamic data masking.
The Benefits of DBHawk Dynamic Data Masking Versus Static Data Masking
Static Data Masking (SDM) is a different technique from DDM. The former approach applies a mask to the data “at rest” and creates a copy of the target database with masked data for training and development purposes. By contrast, DDM replaces sensitive data “in transit.”
The benefits of DBHawk’s DDM against SDM are considerable. Here’s what you can expect.
Adds A Layer of Security and Streamlines Policy Management
While DBHawk’s DDM does not replace existing SQL security features, it does add an additional layer of protection. For instance, it prevents access to sensitive data by enabling administrators to specify how much sensitive data they want to reveal without affecting the application layer. The tool also lets users configure their DDM to hide sensitive data from probing search queries.
Includes Predefined Policies
DBHawk’s DDM also features predefined policies, giving firms a granular and flexible solution to SQL data security. That’s because teams can apply different masks to users based on roles, access rights, and privileges. DBHawk’s central policy management lets administrators control data access and limit what certain users can see
At an even more granular level, DBHawk’s DDM lets administrators apply masks to data columns and rows, depending on their sensitivity or the search query, meaning the solution combines rights to access with this specificity, offering unrivaled control.
More Scalable and Efficient
In addition, DBHawk’s DDM may be more scalable and efficient than SDM. SDM requires copies of existing SQL databases, necessitating additional storage space and hardware requirements. (Organizations relying on cloud services may also need to pay for more storage).
By contrast, DBHawk’s DDM doesn’t require purchasing new server racks or hard drives because it doesn’t change storage requirements. Additional processing overheads are minimal.
Reduced Risk of Data Leakage
Another DBHawk DDM benefit is reducing the risk of data leakage or inconsistencies between original and masked data. Datasparc’s solution achieves GDPR, PCI, and HIPAA compliance “out of the box” since there’s no need to change your existing database.
Under a DBHawk DDM scheme, SQL simply hides data in an existing database based on permissions and configuration. Administrators can then apply masking rules in query result sets, making it easier to use in existing applications. By contrast, SDM requires the overt transfer of data, which is riskier in some settings.
Choose DBHawk’s Dynamic Data Masking to Protect Sensitive Data
As we have seen, SDM makes it harder to maintain a single source of truth in your organization. Multiple copies of the same data can result in data silos, human error, and general confusion. Staff may not use the same information to draw conclusions.
Under a DBHawk DDM scheme, SQL simply hides data in an existing database based on permissions and configuration. Administrators can then apply masking rules in query result sets, making it easier to use in existing applications. By contrast, SDM requires the overt transfer of data, which is riskier in some settings.
SDMs also require you to store data in secondary environments, whereas DBHawk’s DDM lets you stream data from the original location, including development or testing environments.
To improve your database’s security and remain compliant with data protection laws, consider using DBHawk today. To learn more contact us or request a demo.
