Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.

翻译：隐私与安全是设计具备健全数据保护能力和网络弹性能力的信息系统的核心要素。然而，开发人员在将这两项属性融入软件项目时常常面临困难，原因在于他们要么缺乏适当的网络安全培训，要么并未将其视为优先事项。先前的研究试图通过威胁建模方法来支持隐私与安全工程活动，以审查系统架构中的缺陷。此外，现有文献也提出了多种用于自动识别漏洞和生成安全代码实现的技术。相比之下，此类“即代码”方法在隐私领域的研究似乎尚不充分，鲜有工作深入探讨（i）源代码中隐私属性的自动检测，或（ii）隐私友好型代码的生成。在本工作中，我们通过开展一项快速文献综述，旨在刻画当前“隐私即代码”方法与工具的研究图景。我们的结果表明，PaC研究尚处于起步阶段，尤其是在现有方法的性能评估与可用性评估方面。基于这些发现，我们概述并讨论了未来可能的研究方向，包括与软件从业者合作的实证研究、基准数据集的构建，以及生成式AI技术在其中可能扮演的角色。