Maintaining today’s data-driven world requires well-organized, simple access and rigorously protected information. In other words, a database is necessary. The term “database” can describe any collection of related records that have been formatted in a specific way and stored for easy access and analysis. Here is when MongoDB comes in handy.
To store data in a format other than SQL, many organizations have turned to MongoDB. MongoDB includes native capabilities to keep your data safe from encryption to authentication to access control to audits.
This article will discuss the essentials of MongoDB data masking including:
What is MongoDB Data Masking?
Data masking is a tried and true method of keeping sensitive data hidden from unwanted access yet making it accessible to authorized individuals. Data masking is a technique for protecting sensitive data while still making it usable by your technical staff.
Accordingly, MongoDB is a distributed document database capable of handling massive amounts of data. You can encrypt sensitive data saved in MongoDB databases while it is at rest in the database, during backups, and while it is in transit over the network by the database administrator. Users working with data on a MongoDB server have the option of field-level encryption to safeguard sensitive data.
With its reputation as a highly adaptable database, MongoDB is now the backend data storage of choice for many well-known companies and organizations, including Facebook, Google, IBM, Twitter, and many more.
Learn more about some Common Struggles Data Teams Face With Data Masking Projects and Satori’s Data Masking Capabilities
Examples Of MongoDB Data Masking
A wide spectrum of methods exist for hiding identities in data. The best way of anonymizing data depends on the specific circumstances. Three of the most prominent data masking functionalities in MongoDB include the following:
If the data is intended for reporting, establishing an aggregation pipeline will offer a significant amount of protection. However, if any groupings have only a few members, it may be possible to re-identify aggregated personal data. Unfortunately, both public medical records and criminal histories have experienced this phenomenon.
Data masking techniques like pseudonymization is often used when the original subjects to be re-identified.
The report which comprises personally identifiable details is masked in a way that the sensitive information can be re-identified. For instance, a hospital needs to have an analysis done of the likelihood of a given pathology, or the ideal treatment, based on medical history.
If private data “leaks” into patient records, this protection is nullified. This practice is not legally recognized as equal to anonymization.
3. Data Generation
When an app has not been made public yet, it is impossible to put it through its paces without generating encrypted data and using it for testing and training purposes. If not for the fact that data across disciplines tend to be interconnected, the task of data generation for masking would be trivial.
MongoDB Data Masking Challenges
MongoDB efficiently scales programs and uses a flexible storage system that stores massive volumes of data across clusters defined in millions of nodes. Nonetheless, this document-based storage system faces substantial obstacles to de-identifying and masking data.
The lack of data structure poses the first difficulty. Since there are no predefined schemas in MongoDB, any field in a collection can store any data type. In addition, this classification can shift at different depths of a document. This inconsistency hampers the ability to effectively conceal data and simulate production environments in testing environments.
Data masking is further complicated by MongoDB’s JSON storage format. Names, license plate numbers, and other information that are harder to quantify are all stored in JSON. It isn’t easy to get down to the granularity required by these deeply nested document fields at the top level to generate test data that accurately reflects reality.
Length of Time to Build
Building an infrastructure that can generate test data replicas of production data is a time- and resource-intensive endeavor, even for a relational database management system (RDBMS). Since there are numerous MongoDB formats and versions, this process takes much longer. Generators that can find and mask any document version and format are a necessary part of your de-identification infrastructure.
3 Best Practices for MongoDB Data Masking
To overcome the challenges that MongoDB data masking poses, and to keep data secure, here are three best practices to follow:
1. Differentiate your Security Credentials
You can enable authentication by generating user and process-specific login credentials for use with MongoDB. Instead of having several people share the same credentials, you should offer each user their credentials and provide them access based on their role, as explained below.
2. Reduce the Number of Database Connections
A data breach may occur if an unauthorized third party accesses the database. To mitigate this threat, you should restrict the number of users who can access the database remotely. The recommended approach is to limit connections to only those from a whitelist of approved IP addresses.
Each project using MongoDB’s fully managed service, Atlas, has its virtual private cloud. Virtual Private Cloud (VPC) peering allows users to restrict access to their applications from outside their private network.
3. Establish Additional Encryption for Sensitive Data
The server performs the bulk of the encryption. This encryption raises the possibility that whoever gains access to the server can only access the specified information. Data is protected at the client end using Field-level encryption, which requires a decryption key known only to the intended recipients.
As a result, the encrypted data can only get deciphered by the intended recipient. Only the database native driver needs to be updated to enable FLE.
As a database, MongoDB is at the forefront of protecting user data. Security professionals will recognize and appreciate the engineering effort put into technologies like client-side field-level encryption in MongoDB.
Satori enables the anonymization of data based on policies, according to users, roles, and datasets.
To learn more:
- Book a demo with one of our experts
- Satori’s Dynamic Masking Capabilities
- Blog: Common Struggles Data Teams Face With Data Masking Projects