## 1 Introduction
### Sensitivity and Potential Vulnerabilities  
After examining contemporary web frameworks, we move on to a crucial topic: data sensitivity and potential vulnerabilities. In the domain of web applications, especially those developed with frameworks such as Angular, the management of sensitive data is of the utmost importance. The distinction between varying degrees of sensitivity and the potential impact of data leakage is essential for the development of secure and robust software. We encountered a notable obstacle: Is it possible to classify sensitive data and measure its level of sensitivity? It is indisputable that the technological advancements of the past decade, not to mention the various scandalous violations of personal data by large technology companies against their users, have raised an entirely new set of concerns regarding how we comprehend, handle, and expect our personal data to be handled. In the past few years, numerous studies have examined issues such as how we understand the concept of sensitive data in different domains [12], how the concept of personal sensitive data has evolved in the first place, and its various degrees [13]. Data collection parsimony, which sets a metric for machine learning algorithms on how little data they require for efficient learning and operation [14], has become a dominant trend, as have categorizing and quantifying the sensitivity of data [15; 16] and the automated recognition of sensitive data [17]. In our proposed categorization, detailed further in the Methodology section, we divided sensitive data into three categories based on the potential damage from disclosure. While at the bottom of the scale we can only speak of potential damage through the accumulation of a large volume of leaked data, the data at the top of the scale is vital information of its own. Although the categorization of such a method set can be arbitrary for a given development team, we wanted to create a scale for large language models that is easily defined and can be described using the chain-of-thought principle. The primary findings of this paper are as follows:  
* Definition of a proprietary classification to quantify the sensitivity and immunity of the data and evaluating it on a test set of 200 variable names using the GPT API. This classification provides a robust framework for comprehending and classifying data according to its sensitivity. By evaluating it using the GPT API, we ensure that the model recognizes and processes diverse data categories appropriately, thereby making our approach more applicable to real-world scenarios. * Demonstration of prompts generated using prompt engineering for the GPT API for static analysis of the code of a complex web application, taking the larger context and deeper context into account. We maximize the potential of the GPT models through prompt engineering, ensuring that the static analysis is not only accurate but also context-aware. This method bridges the divide between generic AI analysis and specific software vulnerabilities, yielding both broad and profound insights. * Evaluation of the efficacy of the GPT-3.5 and GPT-4 models in detecting sensitive data in an application via static code analysis. By comparing the performance of various variants of the GPT models, we seek to discern the evolution and enhancements in these models over time. This evaluation sheds light on how advancements in LLMs can further strengthen software application security. * Evaluation of the ability of the GPT-3.5 and GPT-4 models to determine the protection levels of front-end application elements. In addition to detecting sensitive data, it is essential to ensure that it is adequately protected. By assessing the GPT models' ability to estimate protection levels, we can better comprehend their potential role in security assessments and potential interventions.