The field of cloud-based AI models is moving towards a greater emphasis on privacy and security. Researchers are exploring innovative solutions to protect user data and prevent malicious attacks. One notable direction is the development of gatekeeper models that filter out sensitive information from user queries before they are sent to cloud-based AI models. Another area of focus is the creation of dynamic risk assessment and collaborative defense frameworks to identify and mitigate potential security threats. Additionally, there is a growing interest in rethinking traditional security concepts, such as denial-of-service attacks, to better address the unique challenges of cloud-native and serverless environments. Noteworthy papers in this area include: Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models, which proposes a lightweight, locally run model to filter out sensitive information from user queries. Risk Assessment and Security Analysis of Large Language Models, which describes a system for dynamic risk assessment and a hierarchical defense system to protect against security threats. Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats, which proposes a unified framework for classifying denial-of-service attacks. A Comprehensive Review of Denial of Wallet Attacks in Serverless Architectures, which provides an in-depth analysis of denial-of-wallet attacks and their financial impacts, attack techniques, mitigation strategies, and detection mechanisms.