A comparative evaluation of integrating Differential Privacy (DP) into Extract–Transform–Load (ETL) pipelines for sensitive domains is presented. Laplace noise of epsilon = 0.1, 1 and 5 was applied on the MIMIC-III clinical data and Fannie Mae loan archive on which the privacy-utility trade-off was evaluated using the Logistic Regression (LR) and Random Forest (RF) models. These findings suggest that healthcare data are extremely sensitive to DP perturbation. In MIMIC-III, AUC decreased from 0.60 (raw) to 0.50 (ε = 0.1) for LR, with F1 falling from 0.31 to 0.26. Conversely, financial data were also more resilient, and the Fannie Mae dataset showed better results, AUC = 0.91 and F1 = 0.82 even at ε = 5. These results indicate that privacy-sensitive ETL pipelines are necessarily application-specific as healthcare will experience stiffer utility falls whereas finance will retain predictive capacity. The research offers the first cross-domain findings of the DP-enhanced ETL performance and has practical recommendations about the process of the secure data integration in the sensitive AI.