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As data privacy regulations tighten and the risk of data exposure grows, enterprises are rethinking their approach to test data management (TDM). In particular, software engineering and quality assurance (QA) teams are facing new pressure from Chief Information Security Officers (CISOs) and compliance officers to stop using masked production data in lower environments. Increasingly, organizations are turning to synthetic data as a secure and scalable alternative. At GenRocket, we’re seeing this shift accelerate across enterprise quality engineering teams with both new and existing customers.

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Executive Summary

Enterprise software engineering and quality assurance face an ongoing challenge: the lack of reliable, scalable, and compliant test data. Recent industry reports highlight that most enterprises struggle with insufficient, inconsistent, or privacy-compromised test data, hampering the effectiveness of software testing, automation, and overall quality engineering efforts.
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In the realm of software quality assurance and engineering, there exists a wide array of testing categories, each tailored to ensure different aspects of system functionality, performance, and compliance. These range from unit testing, which examines individual components, to complex integrations, performance benchmarking, and regulatory compliance checks. For each testing category, having precise and relevant test data is crucial to accurately assess system behavior and ensure quality.

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Introduction

In the rapidly evolving landscape of insurance technology, Guidewire stands as a cornerstone solution, offering a comprehensive suite for managing policies, claims, and billing. As insurance companies increasingly adopt and customize Guidewire’s platform, the need for efficient, secure, and comprehensive test data automation solutions becomes paramount. GenRocket presents an advanced test data solution to address the unique challenges faced by Guidewire users in generating, automating, and securing test data throughout the software development lifecycle.
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In the fast-evolving world of technology, the demand for high-quality software has never been greater. Companies are under constant pressure to release new product features that not only provide a competitive edge but also enhance the customer’s digital experience. Amid this backdrop, the need for a more secure, automated, and agile approach to Test Data Management (TDM) has become paramount. Enter Test Data Automation (TDA), a fresh new approach that promises to transform the way organizations handle test data, ensuring security, compliance, and software testing efficiency.

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Part 3: How a Prominent Legal Firm is Deploying the Technology

In this third part of our series on leveraging GenAI for controlled and accurate synthetic data generation, we delve into a real-world application of GenRocket's platform, integrating the principles discussed in the previous two parts of the series. We will explore how GenRocket, combined with generative AI (GenAI), can address complex data provisioning needs, delivering robust synthetic data solutions at an enterprise scale by presenting a comprehensive use case deployed by a prominent legal firm.
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Part 2: Scalability Requirements for Managing the Full Data Provisioning Life Cycle

In Part 1 of this series, we focused on how to leverage generative AI (GenAI) tools for provisioning synthetic data to ensure data quality in a complex enterprise environment. It described the limitations and risk factors presented by GenAI tools and their Large Language Models (LLMs). In Part 2, the essential factors for provisioning data on a global scale are examined along with strategies for leveraging GenAI using a single data platform at enterprise scale.
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