This research demonstrates how Large Language Models can be effectively engineered to automate privacy policy analysis through prompt engineering, fine-tuning, and explainability techniques.

• Enables automated detection of different concepts within privacy policies
• Facilitates creation of reader-friendly summaries from complex legal documents
• Helps identify potential legal compliance issues efficiently
• Provides explainable results for better transparency in legal AI applications

This innovation matters for legal professionals by reducing manual review time, improving consistency in privacy policy analysis, and enabling more efficient compliance monitoring across multiple documents.

Using LLMs for Automated Privacy Policy Analysis: Prompt Engineering, Fine-Tuning and Explainability