Enterprise Test Data Management: Practical Approach for Testing & Development

16 Nov, 2023
5 min read

Your Test Data Determines the Quality of Testing

Your testing processes are important, but they are useless if the test data you use is not right or of adequate quality. Effective testing requires high-quality (production-like) data. Traditional Test Data Management methods cannot keep up with the ever-changing enterprise needs, resulting in issues impacting efficiency, quality, speed, compliance, privacy, and security, to name a few.

Traditional Approached & Challenges

As organizations go through digital transformation, agile methodologies are becoming mainstream in development and operations, requiring efficient and continuous testing execution. This transition into a dynamic development process requires availability and access to high-quality test data across the testing cycles. The use of personal data in development and testing environments is a persistent concern for software engineering leaders and organizations, especially in view of regulatory policies such as GDPR, CPRA, and others (Source: Gartner: Steps to Improving Test Data Management). Production data frequently contains confidential and private information that may be subject to regulation, causing delays for both internal and contracted team members. Low-quality data and poor Test Data Management directly impact development and innovation cycles, creating process bottlenecks, resulting in higher costs, poor products, and unhappy development teams.

Synthetic Data vs. Production Data

There are two types of solutions when it comes to Test Data Management:

  • Use of Anonymized Production Data: Data that has a similar structure to the real data, while anonymizing sensitive data and PII from the original data. It could be as simple as changing the variable name. In contrast, with synthetic data, using high-quality production data allows maintaining original data, referential integrity, and consistency.
  • Synthetic Data Generation: Data that is artificially manufactured, having the same statistical properties. Creating synthetic data requires various algorithms to map the synthetic data to the original data. The main point is that not everything that works on synthetic data will work on the original data—meaning that you will have to validate and test your product (again) against the production data.

Enabling Automated, High-Quality Test Data Operations

SecuPi’s Test Data Management offers IT leaders a way to leverage production data while automating the protection of sensitive and PII data, preventing it from being misused in lower-end environments such as development, unit-testing, and system-testing. It makes software engineering frictionless by enabling the use of high-quality datasets, mitigating concerns regarding privacy, security, and compliance.

Integrating Test Data Management into the organization’s CI/CD, delivery pipelines, and data governance framework allows efficient testing operations, enabling data self-services and auto-provisioning of datasets of users to efficiently perform testing with high-quality data.

Coupled with data anonymization and masking, the use of production data allows for the elimination of risks associated with sensitive and personal information. This approach makes the data usable for testing purposes while ensuring compliance with data privacy laws such as GDPR and CPRA, which mandate the use of anonymized data to minimize damage in the event of a breach.

SecuPi’s centralized platform provides IT teams with an efficient tool for controlling and provisioning test data across multiple projects, users, and applications, while enforcing much-needed security controls, no matter where the data is coming from. Efficient, high-quality testing requires organizations to adopt a data-centric approach to Test Data Management. This requires moving from a silo-based, dataset-specific approach into a data-centric state of mind. Data security, privacy, governance, and sovereignty must be consistently enforced across the organization’s operations, users, partners, and vendors, supporting business processes and testing operations.

  • Protect Sensitive Data, Both Dynamically and At-Rest:
    • Ensuring end-to-end data security over test data: using protection at-rest and physical masking, sensitive data is anonymized before it is made available for testing. Data is always protected while stored in and accessed for testing purposes, maintaining full data consistency and integrity and ensuring high-quality testing.
    • Dynamically apply masking, filtering, blurring, scrambling, and other privacy and security-enhancing technologies over data stored in clear-text. This preserves data’s utility while keeping it beyond reach of non-authorized, testing and administrative users.


Adopting Data-Centric Security for Data Leakage Prevention

Traditional Test Data Management solutions do not offer the capabilities to address the full scope of business and technical requirements. Such tools further require extensive implementation effort, alongside high CAPEX and OPEX to maintain multiple technologies. Access control is fundamental to securing data, but the complexity of on-premises, hybrid, and cloud operations, the volume of data, technologies, and number of data users can cause it to quickly get out of hand as policies need to be defined and validated to be consistently enforced across each and every user, different data-stores, classifications, technologies, and more. Adopting a data-centric security approach enables the externalization of data protection and control functions from various applications, data platforms, and other technologies. As a result, this approach offers several key benefits to the organization:

-Single Pane-of-Glass Across Your Data Landscape: Consolidating telematics from multiple sources, correlating and aggregating it for a single, holistic view for all data activities is critical for a real-time response to emerging risks.

-Consistent Data-Protection, Everywhere: Using a data-centric platform allows the organization to define data access and data security policies that support the organization’s business strategy. Enabling data democracy while ensuring access on a need-to-know basis, regardless of how data is being accessed or processed. Consistent policies will always apply.

-From Classification to Remediation: Implementing a data-centric security platform offers a superset of capabilities, traditionally procured from multiple vendors to address a specific need. Such an overarching set of capabilities enables ensuring that nothing falls between the cracks:

Data Discovery and Classification: Enabling identification, classification, and defining the risk level for sensitive data such as PII, PHI, PCI, etc.

-Application-centric data discovery & classification

-Datastore level discovery & classification

-External source (discovery and data catalog integration)

Real-Time Monitoring: Gain real-time visibility into every data access and processing activity with full classification, user context & risk level.

– Who is doing what from where and when

-Full user context

-Full forensics across the data session (Header, Body, User Request, Data Request, Data Response, User Response)

-User behavior analysis

-Data accessed, its classification, and any data-protection activity enforced on the session

-Tools used to access/process the data

-User Behavior Analytics

-Anomalies detection, alerting, and actions

Access Control: Applying access control requires understanding modern architecture, volume of data, technologies, regulations, and the number of data users

-Attribute-based Access Control (ABAC) automatically applies policies at query time, considering traits of the user, data, environment, and intended action.

-Control data operations (allowed to search, allowed to modify, etc.)

-Correlating data attributes, classifications, roles, and users’ traits, enabling multi-facet Purpose Based Access Control (PBAC)

Data Anonymization & De-identification: Protecting data dynamically and at-Rest

-Reversible and non-reversible methods

-Deterministic and non-deterministic methods

Effective Test Data Management plays a crucial role in ensuring the quality, efficiency, and security of testing and development processes. Traditional approaches face challenges related to compliance, privacy, and security, prompting the exploration of innovative solutions. SecuPi’s solution for Test Data Management provides a strategic method to utilize production data while safeguarding sensitive information in lower-end environments. By adopting a data-centric security approach, organizations can address the dynamic nature of modern development processes, efficiently manage high-quality test data, and ensure compliance with regulatory frameworks.

SecuPi’s centralized platform serves as a comprehensive tool for controlling and provisioning test data across diverse projects, users, and applications, reinforcing security measures consistently. Embracing a data-centric mindset is crucial for organizations to overcome process bottlenecks, achieve high-quality testing, and navigate the evolving landscape of data security and privacy.

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