In this age of technological superiority, where no sector is immune from its influence, privacy becomes more important than ever before. A technology as old as Generative Artificial Intelligence (Generative AI) which has been doing wonders after applications such as ChatGPT, Bard, Bing AI have started summoning their popularity. It is built on Generative Adversarial Networks (GANs), a brand of neural nets, which has been utilized to generate competitive network in order to create more and more fake images. This generator then creates data that teaches the discriminator by creating content that it can’t differentiate as real or fake each time. Generative AI, though extremely promising, it is experiencing fundamental ethical dilemmas. There are concerns that such power could be abused to create misinformation or deepfakes. Such privacy concerns, however, are just the tip of the iceberg when it comes to the potential real-world consequences of generate synthetic data. In this paper, we unveil the basis of synthetic data generation as well as the inner functioning of Generative AI, its challenges and probable solutions to overcome them.

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Privacy Data Security Personal Information Artificial Intelligence Generative AI GANs Neural Networks AI Ethics Technological Advancements Technology Ethics Ethical AI Development Generative AI Applications Synthetic Data Ethical Dilemmas.