Integrating generative AI with cloud computing has revolutionized educational systems to achieve personalized and adaptive content by harnessing big data for different learners’ needs. At the same time, this development poses enormous challenges regarding data protection, integrity, security in computation, and compliance issues related to regulatory requirements. A study investigated data protection schemes and security protocols of specific cloud-enhanced generative AI systems, specifically in education. This research investigates, by using a mixed-methods approach, the privacy risks, ethical implications, and effectiveness of applied security measures around cloud-based AI in educational contexts. Key takeaways from the study include that, while relevant platforms widely adopt encryption and access control security measures, a gap still exists in regulations, particularly for the GDPR/FERPA requirements. Also, specific ethical considerations fall at risk, such as algorithmic bias or transparency, which is supposed to be fair and equable in AI-driven education. The present study, therefore, develops enhanced privacy-by-design principles, robust encryption protocols, and ethical AI frameworks that could mitigate risks identified herein and provide guidelines on how educational institutions, policymakers, and developers can foster safe, legally compliant, and ethically sound AI-enhanced educational environments.