ABSTRACT 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. IMPORTANCE Transcription factors (TFs) play a crucial role in deciphering biological information from the DNA of living organisms. Improper regulation of their functions can disrupt cellular physiology and lead to diseases in humans. As one of the key regulatory mechanisms, some TFs control their own expression levels through autogenous regulation. However, identifying autogenous regulation events of TFs has been a tedious task. In this study, we present a straightforward approach that provides a reliable means to identify TF autogenous regulation events. Our method provides a valuable means for understanding the function of this important class of proteins in cells.