Redefining Test Data Strategy in the Era of Generative AI

For years, test automation has focused on better frameworks, faster execution, and expanding CI/CD pipelines. One challenge still slows delivery: test data.

Modern applications depend on datasets that are complex, interconnected, and often privacy sensitive. Traditional approaches — copying production databases, hand-building records, maintaining static datasets — don't scale well and are slow and costly to maintain.

Generative AI changes what's possible here. Instead of treating test data as a one-time setup task, teams can build intelligent workflows that help discover data requirements, generate realistic datasets, mask sensitive information, surface edge cases, and maintain reusable data assets across the testing lifecycle.

This session introduces an AI-Driven Test Data Strategy Framework that combines generative AI with established test data management practices to improve both automation quality and delivery speed. You'll see where AI adds value, where human expertise remains essential, and how to adopt AI responsibly while maintaining data privacy, regulatory compliance, and trust.

Through practical examples and enterprise scenarios, you'll see how AI can cut test data bottlenecks, improve coverage, and support quality engineering at scale without loosening governance.

Whether you're a test automation engineer, QA lead, SDET, or quality architect, you'll leave with practical ideas for modernizing your organization's test data strategy using AI.