This paper aims at understanding the critical issues with the DAST tools and the main one is the high-end false positives that affect the effectiveness of the security evaluations. Although DAST is a significant asset in discovering weaknesses in applications while they are in use, the challenge of large numbers of false positives presents challenges for security specialists, which results in time and monetary waste and the possible failure to recognize actual risks. This paper outlines the main causes of false positives in DAST which include improper scanning settings, dynamic content changes and the general nature of heuristic based detection. Furthermore, this paper also presents recommendations on how to prevent these problems, some of which are consideration of the use of the combined DAST and SAST testing strategies and the integration of machine learning techniques to help in the improvement of detection rate. The results outlined above point to the necessity of expanding the range of methodological tools and developing new technologies in DAST to form more stable security environment. Therefore, by correcting these challenges, it will be easier for organizations to fashion better security strategies and maximize resource utilization on application security.