When working in a production setting, it can be challenging to restrict users’ access to sensitive information while still providing them with the access they need to accomplish their tasks efficiently and effectively.
However, for the safety and security of the company, your employees, and your customers, you must maintain strict permissions access. The more people with access, the more vulnerable your company is to a cyber attack.
The dichotomy exists because while programmers need access to production datasets they should not be able to view any of the personally identifying information (PII) such as names, emails, or IP addresses within the data. Similarly, analysts require access to analytic datasets but also should not have access to PII. One way to solve this issue is to provide access to the data but obfuscate sensitive information.
This article will further discuss data masking, particularly within Elasticsearch.
What is Elasticsearch Data Masking?
Elasticsearch Data Masking tools are intended to protect sensitive information that is stored in database systems by altering the real data point values. This prevents users without approved access from viewing the real values of a data set. These types of users instead find false data that, despite its appearance, does not contain any PII or sensitive data. In this manner, private and sensitive information is shielded from the risk of being stolen or made public.
When a database is accessible to employees of an organization or when a database is released to third parties, the Data Masking tools for Elasticsearch enable an organization to secure the sensitive fields and data that the firm controls. The data masking tools randomize or obscure the information, rendering it meaningless while giving the impression that it is genuine, allowing data users to carry out activities without violating its security or secrecy. Further, the Elasticsearch Data Masking tools are helpful when transferring information to third parties or when it is necessary to limit the availability of corporate information to unauthorized staff, such as researchers, developers, and engineers.
Using Data Masking in Elasticsearch
The practice of data masking helps reduce the likelihood of information leaks as well as breaches involving large amounts of data. In addition, data masking frequently necessitates the application of data transformations to accommodate users with varying degrees of data access.
Processing Data Masked in Elasticsearch
To utilize data masking in Elasticsearch, companies must first unmask the data or make the data representative of the original data set. To do this, organizations use an ingestion pipeline with data unmasking tools and then divide the data into fields that can be recognized without difficulty. Screening by field is one of the most effective tools when it comes to Elasticsearch Data Masking since it allows users to quickly divide data into recognizable fields.
Now that an individual can quickly determine what data they have, it is time to consult with the data protection officer to decide who should receive access to the data. The next step would be to determine and assign user roles.
Establish User Roles and Define Field-Level Protections
Within the security settings, you can define roles that can be assigned to various groups of users. These settings have several advantages, one of which is that they give the administrator more control over user access to data.
In this section, you can grant access to whole indexes except for a certain field using row-level security practices. This access is useful for utilizing wildcards or combining these permissions in various ways. So, it is possible to grant a programmer exposure to the database even if it contains sensitive data by restricting the elements that the programmer can view within the database.
Masking of Data Through the Utilization of the Log Stash Fingerprint Filter
The Log Stash fingerprint filter offers a method for transforming a field in which only one direction is preserved. The best feature of this transition is that it always produces the same output based on the same input, but it is not simple to reverse the process.
One can construct a new field by using the contents of a sensitive field as inputs. Even if a field is no longer accessible, it is a good habit to check in with the data protection officer at regular intervals. There is no mistaking that this is a one-of-a-kind identifier, which may call for additional scrutiny.
Data Security in Elasticsearch Data Masking
Modern businesses face various challenges regarding data management, including the collection, distribution, analysis, and processing of actual data. On top of these essential data management tasks, organizations must maintain the data’s confidentiality and adhere to the most recent legislation around data protection.
Sensitive Information as well as Personally Identifiable Information or PII
A data masking solution virtually guarantees data protection and adherence to data duplicity by restricting access and exposure to sensitive data values while assisting various data source materials, configurations, and types.
Benefits of Elasticsearch Data Masking
- Data masking is an integral component of many rules and compliance programs in which data relating to PII must be safeguarded and must, under no circumstances, be made public. The data’s integrity and structural format are also preserved in masked data.
- You can grant access to the data to the testers and the programmers without exposing the information in any manner.
- Data masking reduces the potential threat to the system’s security while allowing for the display of data analytics and results.
- Data masking is the workable answer to problems such as security breaches, loss of data, and account or security weaknesses resulting in fraud, unsecured interfaces, and unethical use of data.
Elasticsearch Data Masking with Satori
Satori provides dynamic data masking capabilities that work in tandem with Elasticsearch’s native masking capabilities. The ability to search out sensitive data, including semi-structured data, wherever it is located and anonymize data dynamically ensures that data can be easily shared increasing the time-to-value.
Using Satori you can create, maintain, and replicate masking policies easily reducing the burden on DevOps and data engineering teams and reducing security risks. Satori’s integration with Elasticsearch is seamless and easily scalable and does not require any additional coding.
To learn more about how to integrate Satori with Elasticsearch book a technical call with one of our experts.
