Securing Data Examples

Data management systems often need to apply filtering and masking on data and apply strict access control rules to maintain data security, privacy, and regulatory compliance. Organizations need to protect sensitive information from unauthorized access. In this chapter, you will discover ways to use aggregation pipelines to help secure the data in a MongoDB database to reduce the risk of a data breach.

This chapter will cover the following:

• Providing views that filter out sensitive records and fields
• Masking and obfuscating the data of sensitive fields
• Applying programmatic access control rules over which users can access specific subsets of data

Redacted view

A typical data security requirement is to expose views of data to users, omitting specific sensitive records and fields. For instance, a personnel database might hide salary details while showing employee names. Due to a confidentiality clause, the system may even need to omit some employees from query results entirely. In this example, you will discover how to build an aggregation pipeline to apply such filtering rules for a view.

Scenario

You have a user management system containing data about various people in a database, and you need to ensure a particular client application cannot view the sensitive parts of the data relating to each person.

Consequently, you will provide a read-only view of each person's data. You will use the view, named adults, to redact personal data and expose this view to the client application as the only way it can access personal information. The view will apply the following two rules to restrict data access:

• Only...

Mask sensitive fields

Data masking, or data obfuscation, is a technique that allows an organization to alter sensitive data, maintaining its structure but removing the actual sensitive content. This ensures developers and testers can work with realistic datasets without risking data misuse and allowing the organization to protect sensitive information. In this example, we will discover how to use a pipeline to mask the fields of a document.

Note

This example requires MongoDB version 4.4 or above. This is because you'll be using the $rand operator introduced in version 4.4.

Scenario

You want to perform irreversible masking on the sensitive fields in a collection of credit card payments, ready to provide the output dataset to a third party for analysis, without exposing sensitive information to that third party.

The specific changes that you need to make to the payment fields include the following:

• Partially obfuscate the cardholder's name
• Obfuscate...

Role programmatic restricted view

MongoDB provides robust RBAC solutions, allowing administrators to specify user access to resources declaratively. However, there are instances when you need to programmatically apply detailed logic to limit data access based on roles. In this example, you will explore how to regulate data access using programmatic RBAC within an aggregation pipeline.

Note

This example requires MongoDB version 7.0 or above. This is because you'll be using the USER_ROLES system variable introduced in version 7.0.

Scenario

At a medical establishment, the central IT system holds patient data that you need to share with different applications (and their users) according to the application's user role: receptionist, nurse, or doctor. Consequently, you will provide a read-only view of patient data, but the view will filter out specific sensitive fields depending on the application user's role. For example, the receptionist's application should...

Summary

In this chapter, you have learned techniques to apply filtering and masking on data and apply strict access control rules to maintain data security.

In the next chapter, you will explore how aggregations can be used to extract summarized information from time-series data.