This chapter examines data security and privacy concerns in the context of generative artificial intelligence (GenAI) evaluations in higher education. Protecting student data becomes a top priority as educational institutions increasingly use GenAI technology to improve evaluation procedures. The first section of this chapter defines generative artificial intelligence (AI) and discusses its uses in academic assessments, highlighting its advantages and possible privacy hazards. We look at the kinds of information gathered during GenAI tests, the significance of getting students’ informed consent, and the difficulties with anonymization methods. The chapter also covers key foundations for data security, such as risk assessment techniques and recommended practices for protecting private data in learning settings. The ethical and legal ramifications are also covered, emphasizing pertinent regulatory frameworks like FERPA and GDPR as well as the moral obligations of organizations when it comes to data management. We provide useful insights for educators and administrators by demonstrating effective privacy measure installations and lessons learned from data breaches through real-world case studies. To create a safe and reliable environment for the use of generative AI in educational evaluations, the chapter ends by outlining future possibilities for research and innovation in data security and privacy-preserving AI techniques.