Cloud-native technologies, which include microservices, containers, Kubernetes, and serverless computing, have been growing rapidly in recent times and have revolutionized the world of software development. Although the emergence of these technologies is beneficial in making software more scalable, flexible, and deployable, there exist certain difficulties that come about with them in software testing and security. Traditional methods of testing have found themselves ill-equipped to deal with such complex and dynamic environments, where artificial intelligence emerges as an ideal solution. The survey aims to study the role of AI-enabled test engineering strategies in creating secure cloud-native systems. This includes investigating the use of Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning in planning, testing, and deploying software. This survey will focus on various AI-enabled strategies in automated test case creation, test data creation, defect detection, test priority analysis, and deployment. Security issues such as malware detection, unauthorized access, runtime security threats, and decentralized access control are some other aspects to be covered in this survey. Some of the advantages of using AI-driven strategies for testing include greater automation, accuracy, and quicker deployment. On the downside, there are certain challenges such as the need for high-quality data, explainability, and computational complexity.