Artificial intelligence (AI) is transforming software testing by enabling large-scale test data generation and analysis. However, the lack of traceability between large-scale test libraries and system requirements undermines confidence in the testing process. The paper addresses this challenge by proposing a traceability solution tailored to an industrial setting characterized by a data-driven approach. Building on an existing model-based testing framework, the design extends its annotation capabilities through a multilayer taxonomy. The suggested architecture leverages AI techniques for bidirectional mapping: linking requirements to test scripts for coverage analysis and tracing test scripts back to requirements to understand the tested functionality. The approach addresses industrial-scale challenges, improving efficiency and transparency in managing large volumes of test data.