Generative AI and Privacy-Preserving Big Data Analytic in Cloud Environments with AI Agents

While generative artificial intelligence (GenAI) technologies are revolutionising content production, they also pose serious privacy and data security issues. The potential of privacy violations, biases, and cyberattacks rises as these models process large datasets, many of which contain sensitive or private data. These issues are examined in this book, especially in important fields like cybersecurity, healthcare, and finance. The potential for GenAI models to reproduce or infer sensitive data from training datasets is a major problem that raises ethical and intellectual property issues. Data protection techniques like encryption, tokenisation, and anonymisation 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. It evaluates the impact of tokenisation and anonymisation on a state-of-the-art large language model (LLM) through experimental analysis. Empirical results offer insights into the trade-offs between protecting model performance and data privacy using open-source tools such as Microsoft Presidio. The goal of the research is to help create safe and morally sound GenAI applications, making sure that advancements in AI are in line with data security guidelines while preserving accuracy and efficiency in practical applications.