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This is a master's thesis study that explores how to use large language models to automatically identify key tasks related to GDPR compliance in business processes, providing new ideas for the automation of data protection compliance.
Section 01
This is a master's thesis study that explores how to use large language models to automatically identify key tasks related to GDPR compliance in business processes, providing new ideas for the automation of data protection compliance.
Section 02
Section 03
Since the General Data Protection Regulation (GDPR) came into effect in 2018, it has imposed strict requirements on how enterprises handle personal data. However, many organizations face a core challenge in practice: how to systematically identify which links in their business processes involve GDPR compliance obligations. Traditional methods rely on manual audits, which are both time-consuming and prone to omissions. This study explores the use of large language models (LLMs) to automate this process, representing an innovative application of AI in the field of regulatory technology (RegTech).
Section 04
The business processes of modern enterprises often involve a large number of data processing activities, from customer registration and order processing to after-sales service—almost every link may involve personal data. Manually sorting through these processes is not only labor-intensive but also requires professional knowledge to determine which activities fall within the scope of GDPR regulation.
Section 05
GDPR includes numerous clauses covering multiple dimensions such as the legal basis for data processing, data subject rights, the principle of data minimization, and storage period restrictions. Accurately identifying which business process links touch on these requirements requires dual professional knowledge in law and technology.
Section 06
Business processes are not static; they evolve continuously with business development. Manually maintaining compliance lists is difficult to keep up with this pace of change, which can easily lead to compliance blind spots.
Section 07
Large language models, through pre-training on massive amounts of text, have strong semantic understanding capabilities. They can:
Section 08
The study may adopt the following technical path:
Collect and organize business process documents, including flowcharts, operation manuals, system descriptions, etc. These documents form the input for LLM analysis.
Design specialized prompt templates to guide LLMs in identifying GDPR-related tasks. Prompts may include:
Compare the identification results of LLMs with manual expert annotations, and calculate metrics such as accuracy and recall. Cross-validation may be used to ensure the reliability of the results.
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