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Abstract: The sixth generation of wireless networks (6G) will require network automation to meet the rapidly increasing demands for high data rate services, ultra-low latency, massive connectivity, and seamless integration with emerging technologies, while effectively reducing operating costs. To address these demands, the concept of Zero-Touch Networks (ZTNs) has been proposed, where Artificial Intelligence (AI) and Machine Learning (ML) play crucial roles in optimizing network performance, enabling intelligent decision-making, and ensuring efficient resource allocation. However, the implementation of ZTNs is subject to security challenges that may hinder their development and deployment. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of specific attacks targeting AI/ML models. In this survey paper, a comprehensive review of the security vulnerabilities and issues with ZTNs is conducted. Additionally, potential automated solutions to ZTN security concerns, with a specific focus on leveraging Automated ML (AutoML) technologies, are investigated. Two case studies are conducted to address security issues in ZTNs and further corroborate our findings: the development of autonomous intrusion detection systems and the creation of defense mechanisms against Adversarial ML (AML) attacks. Finally, some of the challenges and future research directions for the development of ZTN security approaches are discussed.