Transcription factors (TFs) play a crucial role in regulating gene expression in living organisms, and any malfunction in their functions can lead to severe physiological consequences. The regulation of TFs occurs at multiple levels, with autogenous regulation being a common mode of regulation where the expression of a TF gene is dependent on its own function. Although bioinformatics analysis of promoter sequences is frequently used to predict autogenous regulation events, it is not entirely reliable. In this study, we present a simple yet effective strategy to identify the autogenous regulation of a TF. We have demonstrated the effectiveness of this method on three fungal TFs (the Carbon Catabolite Repressor CreA and two uncharacterized TFs of secondary metabolism) and have generated a set of plasmids to facilitate the construction of experimental strains in fungi. Our method can be applied to both positive and negative-acting TFs and generalized for other organisms. Hence, this work provides a reliable and straightforward method for identifying autogenous regulation events, which is useful for understanding TFs’ functions.