Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in their ability to understand natural languages w.r.t the context and nuances. The aforementioned challenges are overcome with the arrival of Gen-AI techniques, along with their inherent ability w.r.t translation between languages, fine-tuning between various tasks and domains. In this manuscript, we provide a comprehensive overview of the various work done while using Gen-AI techniques w.r.t user safety. In particular, we first provide the various domains (e.g. phishing, malware, content moderation, counterfeit, physical safety) across which Gen-AI techniques have been applied. Next, we provide how Gen-AI techniques can be used in conjunction with various data modalities i.e. text, images, videos, audio, executable binaries to detect violations of user-safety. Further, also provide an overview of how Gen-AI techniques can be used in an adversarial setting. We believe that this work represents the first summarization of Gen-AI techniques for user-safety.

翻译：机器学习与数据挖掘技术（即监督式与非监督式技术）在各领域中被用于检测用户安全违规行为。例如，分类器可用于检测电子邮件是否为垃圾邮件，或网页是否在索取银行登录信息。然而，现有的机器学习/数据挖掘分类器在理解自然语言的上下文与细微差别方面存在局限。随着生成式人工智能技术的出现，上述挑战得以克服，该技术具备跨语言翻译、跨任务与跨领域微调的内在能力。本文系统综述了利用生成式人工智能技术保障用户安全的相关研究工作。具体而言，我们首先阐述了生成式人工智能技术已应用的多个领域（如网络钓鱼、恶意软件、内容审核、仿冒欺诈、物理安全）。其次，我们分析了如何结合文本、图像、视频、音频、可执行二进制文件等多种数据模态，运用生成式人工智能技术检测用户安全违规行为。此外，本文还概述了生成式人工智能技术在对抗性场景中的应用方式。我们认为本工作是首次针对用户安全领域的生成式人工智能技术进行的系统性总结。