Redefining Test Data Strategy in the Gen AI Era [Testμ 2026]

At Testμ Conf 2026, Equinox SDET leader Manideep Singireddy presented a scenario-first approach to test data that measures delivery time from test intent to usable data rather than test-suite runtime. The approach begins by defining the business situation to test, such as a high-risk payment failure, then uses enterprise metadata, rules, API contracts, and controlled services to derive and create only the required data, rather than searching production copies or generating large volumes of synthetic records. He argued that large language models should plan and reason over governed context rather than directly query or modify databases, while deterministic generators, approved APIs, and validation tools execute data creation and enforce schemas, integrity, business rules, masking, permissions, and auditing. The proposed model aims to reduce provisioning time and reliance on production data while increasing scenario coverage, reuse, and defect detection, with adoption beginning with metadata and progressing through synthetic data, scenario generation, governance, and eventually agentic workflows. The presentation described an architecture and hypothetical examples but included no live demonstration, named product, measured results, or implementation evidence, and its brief Q&A acknowledged that AI-generated data does not fully solve privacy risks.

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