There is a widespread problem of cookie banners not being compliant with the General Data Protection Regulation (GDPR), which negatively impacts user experience and violates personal data rights. To mitigate this issue, strides need to be made in violation detection to assist developers, designers, lawyers, organizations, and authorities in designing and enforcing GDPR-compliant cookie banners. In this thesis, we present a novel method and an open-source tool for automatically analyzing the GDPR compliance of cookie banners. The tool uniquely leverages large language models together with static code analysis to locate and analyze any cookie banner, using only the website address as input. Informed by the Design Science Research methodology, our research process involved interviews with GDPR legal experts and a thorough review of current literature in order to understand the problem context and define the objectives for our solution. After an initial version of the tool was created, an evaluation was performed by a GDPR legal expert. The feedback revealed that even at this early development stage, the tool approaches the capabilities of a trained eye, which illustrates its potential. Furthermore, our proposed method is generalizable and can be used under many domains to solve various problems (e.g., more generalized web scraping). However, further development and testing with the help of legal experts is required to enhance the tool's accuracy and validity.