The Role of Artificial Intelligence and Machine Learning in Revolutionizing Identity Theft Protection: A Generative AI Approach to Predictive Security Models

Abstract

Artificial intelligence (AI) and machine learning have immense potential to revolutionize identity theft protection. Advances in data science and AI have led to unprecedented opportunities for organizations seeking stronger defenses against ever-evolving cybercrime. Identity theft is one of the fastest-growing criminal activities in the world, and the need for identity theft protection at scale has never been greater. This paper addresses the challenges in developing new models of security, which are driven by technology and influenced by AI and machine learning. We demonstrate how generative AI technologies can help in constructing predictive models for a range of identity theft scenarios. We particularly focus on a GAN-based architecture for predicting Social Security Numbers and offer a conceptual overview of our model. By offering a precise analysis of this architecture, we aim to help the reader understand key trends, innovations, and opportunities in developing security solutions, particularly in predicting and adapting to new forms of cybercrime.

While identity theft has driven a need for advancements in security, crime analytic tools to counteract burglars, drug dealers, and other forms of criminal activity in the non-cyber world have been developed much earlier. Generating potential harm scenarios has long been an aspect of the criminal analytical battle. Such tools are now adapted in cyber, fighting off fraudulent actors. The theoretical model we explore may prove beneficial for those seeking innovative methods to protect organizations from potential harm. In further research, we plan on testing the model on other datasets to gain empirical evidence to back up the theoretical model. It will also be essential to conduct further research to break down the different architectural components of the model in a more practical manner.