Summary: As enterprises transition from synthetic to real-time production for software testing, the data privacy and security landscape must refocus to ensure all the identified personal and sensitive content is masked or removed from test data. Let us learn how.
Why is the security of Test Data important?
Development and Test teams are keen on using test data which closely mimics live production data to ensure the efficiency of any application/software leading to multiple data breaches. In 2018, Uber suffered multiple data breaches in its cloud storage infrastructure due to weak access controls to the production data. Test data privacy is important because it helps to protect sensitive and confidential information from unauthorized access, use, or disclosure during testing.
As one understands the importance of securing test data, let’s look at some types of threats to test data.
Types of Threats to Test Data
Primarily, all applications/software thrive on real-time data, and enterprises must comply with government regulations stipulating that the data must be masked, de-identified, or encrypted.
In addition to government regulations, various security threats could leak valuable and personal information and be used maliciously. These threats can cost a bomb to businesses. Thus, let’s look at some of the common security threats that testers encounter during the test data management process:-
SQL Injection
This is a cyber-attack used by hackers to put dangerous input within a web application i.e., SQL commands use the entry field for execution. It is a significant threat to test data, as it can be used to exploit vulnerabilities in an application’s database and gain unauthorized access to sensitive information. Resulting in data loss, corruption, exposure to unauthorized parties, loss of accountability, access denial, or even to a complete server database. The developers/testing teams should validate input options, ensure adequately formatted inputs in fields like text boxes, comments, etc., and use parameterized queries.
Accidental Data Exposure
Often data breaches are not caused due to any malicious attack but are caused by negligent or accidental exposure of confidential information or intellectual property protected under data protection laws like personally identifiable information (PII) or healthcare data. Accidental exposure might happen during sharing, granting access to, or not complying with security policies. Thus, the concerned teams should know how to check sensitive and valuable data-sharing methods. For example, sharing confidential information with the wrong email address increases the chances of data breaches.
Phishing and social engineering
Phishing is nowadays a common trick attack using communications means, such as an email, to infiltrate a network or spread malware by bluffing authentic-looking email with infected links or attachments or asking for personal information, such as providing a credit card number to offer unexpected prizes from sought-after brands. This security trick compromises sensitive data like credit card details and virtual access to the user’s machine. Thus, understanding social engineering is essential to identify and prevent suspicious requests.
Data sharing with Third-Party
Often the data protection norms discourage the use of portable devices like USBs, which are vulnerable to loss of data and has led to infamous data breaches example such as the Heathrow Airport security incident, which compromised over 1,000 confidential files, including susceptible security and personal information as an employee lost a USB with over 1,000 confidential files. Additionally, third-party vendors may not be careful regarding the security standards while using the Test data, thus, compromising the security of data accessed by them.
Improper Data Disposal
When test data is exhausted or no longer required for testing, the data should be disposed of properly, avoiding any chances of exposing the data to unauthorized access or reach to the data. Proper configuration of access controls on sensitive data avoids any data leakage, thus, in a TDM process, the process of data disposal and authorization required for such disposal should be in place.
Methods of Securing Test Data
Here are some options for securing test data in test data management.
Masking or obfuscating sensitive data: One of the most common methods for securing test data is to mask or obfuscate sensitive data in the test data. This method replaces sensitive information such as Personally Identifiable Information (PII) – names, addresses, and social security numbers with fake or anonymized data.
Data subsetting: This method creates a subset of the production data from different production databases, containing only the necessary data for testing (usually of a much smaller size as a whole) and later generates synthetic data. This method helps in test data distribution and makes the testing much faster than a complete database clone.
Data encryption: Data encryption is another method of securing test data, where the data is encrypted using encryption algorithms and restricts access to the encryption keys to approved individuals.
Access Control and Audit: This method manages the access to test data and ensures it is limited only to authorized personnel through user authentication, access permissions, and role-based access control. A regular audit or review of the test data helps identify unauthorized access and potential security breaches.
Data virtualization: The creation of virtual copies of As data subsetting, data virtualization also efficiently distributes and tests complete production databases. This method creates a virtual layer that extracts the data from its physical storage location, allowing testers to query, manipulate and analyze it in real time. Thus, creating test scenarios and executing tests become efficient without waiting for physical copies of the data to be created.
Securing Test Data Privacy and Breach through Platforms
Several platforms offer secure test data in the test data management process. Here are a few examples I have tried, tested and recommend for most enterprises and startups:
K2View
K2View Test Data Management is designed to create, manage, and secure test data for software testing and development purposes. It creates subsets of test data and masking or obfuscates sensitive data while still maintaining the integrity and relevance of the test data. Their platform also provides data virtualization features, allowing testers to access and manipulate data without needing direct access to the underlying data sources. This helps improve data security by reducing the risk of data breaches or unauthorized access. The K2View TDM platform provides a comprehensive solution for managing test data privacy and security, thus, enabling organizations to maintain data privacy and confidentiality while meeting the standings of high-quality software testing.
Informatica
Informatica Test Data Management platform provides a range of features to help secure test data. The Data Discovery feature helps to automatically identify sensitive data/information, followed by masking where sensitive data is hidden by replacing it with fictitious or randomized data. It creates subsets of test data and categorizes and profiles the structure and relationships of the data. a. This helps ensure that data masking and subsetting policies are properly applied. Additionally, features such as audit trails, role-based access controls, and compliance reporting also help maintain data privacy and security.
Delphix
Delphix offers secure test data management using data masking and data virtualization techniques to hide sensitive data by creating virtual or fictitious copies of production data. Thus, the teams can work with production or real-time data without exposing the data to any possible breach. It ensures consistent masking across different environments by allowing enterprises to create data masking policies that can be applied to multiple data sources.
Conclusion
In summary, during software testing, it is crucial to check the security and privacy of the test data to protect sensitive data, comply with data protection regulations, maintain customer trust, and avoid legal and financial penalties, including fines and damage to the reputation.
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