Generative AI and privacy-preserving big data analytic in cloud environments with AI agents

While generative artificial intelligence (GenAI) technologies are revolutionizing content production, they pose serious privacy and data security issues. The potential for privacy violations, biases, and cyberattacks rises as these models process large datasets containing sensitive or private data. This book examines these issues, especially cybersecurity, healthcare, and finance. The potential for GenAI models to reproduce or infer sensitive data from training datasets is a significant problem that raises ethical and intellectual property issues. Data protection techniques like encryption, tokenization, and anonymization are crucial to reducing these dangers. This study assesses the efficacy of these techniques by looking at how they affect the functional performance and privacy risk reduction of GenAI systems. Through experimental analysis, it evaluates the impact of tokenization and anonymization on a state-of-the-art large language model (LLM). Empirical results offer insights into the trade-offs between protecting model performance and data privacy using open-source tools such as Microsoft Presidio. The research aims to help create safe and morally sound GenAI applications, ensuring that AI advancements align with data security guidelines while preserving accuracy and efficiency in practical applications.