Using AI-Generated Fake Data to Mitigate Online Scams: A Cybersecurity Approach; Empowering Users in Digital Privacy: AI-Generated Synthetic Data as a User-Centric Cybersecurity Solution

This research addresses the disadvantages of traditional data protection models and data breaches by using AI-generated synthetic data to protect real user information while embedding invisible cryptographic markers for breach attribution. The GAN-VAE system is able to create realistic, unrecognizable profiles that function in account and transaction workflows. In simulations, credential stuffing, phishing, brute force, and synthetic identities are worthless in the real world, and the markers are able to identify compromised platforms with 80% accuracy. These datasets have a statistical similarity of over 91% to real data, allowing for seamless integration without privacy breaches. Under the framework of actor-network theory, this approach shifts control to users, forcing businesses to adapt and urging regulators to regulate the ethics of synthetic data. As a result, AI-driven synthetic identities provide a scalable, user-centric privacy solution that mitigates digital threats and enhances individual autonomy.