  The number and dynamic nature of web and mobile applications presents
significant challenges for assessing their compliance with data protection
laws. In this context, symbolic and statistical Natural Language Processing
(NLP) techniques have been employed for the automated analysis of these
systems' privacy policies. However, these techniques typically require
labor-intensive and potentially error-prone manually annotated datasets for
training and validation. This research proposes the application of Large
Language Models (LLMs) as an alternative for effectively and efficiently
extracting privacy practices from privacy policies at scale. Particularly, we
leverage well-known LLMs such as ChatGPT and Llama 2, and offer guidance on the
optimal design of prompts, parameters, and models, incorporating advanced
strategies such as few-shot learning. We further illustrate its capability to
detect detailed and varied privacy practices accurately. Using several renowned
datasets in the domain as a benchmark, our evaluation validates its exceptional
performance, achieving an F1 score exceeding 93%. Besides, it does so with
reduced costs, faster processing times, and fewer technical knowledge
requirements. Consequently, we advocate for LLM-based solutions as a sound
alternative to traditional NLP techniques for the automated analysis of privacy
policies at scale.
