Ever feel overwhelmed by endless privacy policies? You're not alone. Most of us blindly click "Agree," unsure of what data we're sharing. But what if AI could help us decipher these complex documents? New research explores how Large Language Models (LLMs), like the ones powering ChatGPT, can classify the dense paragraphs of privacy policies into user-friendly labels. The challenge? LLMs sometimes "hallucinate," making up facts or misinterpreting information. This new framework tackles that problem with an "entailment" process. Imagine the AI double-checking its work, like a careful editor. It identifies key phrases and then masks them out, challenging itself to regenerate the missing information based on context. This verification step ensures the AI's understanding is accurate and consistent. Tested on a real dataset of privacy policies, this approach outperforms traditional methods, achieving higher accuracy in labeling policy sections. It's a significant leap towards making privacy policies more accessible, empowering users to make truly informed decisions about their data. However, some hurdles remain: while the new approach offers insights into its "reasoning," embedding based methods such as "PrivBERT" remain black-box, and researchers acknowledge that these models need improvement for more nuanced comprehension, but this innovative framework offers a promising glimpse into a future where AI can empower us to navigate the complexities of online privacy.