An effective and important technique, Deep Learning has been found to be recurrently used in different fields, including computer vision, machine vision, and natural language processing. As a result of this technology, deepfakes generate visual media of a particular person that are nearly similar to the original material. Over the past few years, a variety of studies have examined how deepfakes operate, and numerous methods utilizing deep learning have come forward to detect both deepfakes videos and images. In this work, the dangers deepfakes present to corporate cybersecurity are investigated, underscoring the need to understand the technology related to deepfakes and the importance of constructing effective detection and mitigation methods. The article includes coverage of the merits and shortcomings of several methods for locating deepfakes, including deepfake detection algorithms along with human review and contextual analysis. A technique that unites automated detection with human judgment is critical for discovering deepfakes. The article brings to attention the necessity for businesses and individuals to strengthen their awareness and evasion capabilities against deepfake deception, decrease the risk that deepfakes can get, and take advantage of the potential opportunities deepfakes might create. Key takeaways reveal that facial landmark analysis, lip synchronization detection, and micro-expression analysis play key roles in deepfake detection and that a zero-trust and multi-factor authentication protocol is necessary to address the risks associated with deepfakes.