  We introduce a state-of-the-art approach for URL categorization that
leverages the power of Large Language Models (LLMs) to address the primary
objectives of web content filtering: safeguarding organizations from legal and
ethical risks, limiting access to high-risk or suspicious websites, and
fostering a secure and professional work environment. Our method utilizes LLMs
to generate accurate classifications and then employs established knowledge
distillation techniques to create smaller, more specialized student models
tailored for web content filtering. Distillation results in a student model
with a 9% accuracy rate improvement in classifying websites, sourced from
customer telemetry data collected by a large security vendor, into 30 distinct
content categories based on their URLs, surpassing the current state-of-the-art
approach. Our student model matches the performance of the teacher LLM with 175
times less parameters, allowing the model to be used for in-line scanning of
large volumes of URLs, and requires 3 orders of magnitude less manually labeled
training data than the current state-of-the-art approach. Depending on the
specific use case, the output generated by our approach can either be directly
returned or employed as a pre-filter for more resource-intensive operations
involving website images or HTML.
