LLAMA Wrangler: An Intelligent and Secure Data Wrangling Platform Using LLM

In the modern era of data-driven decision-making, organizations rely heavily on clean, structured, and reliable data to achieve accurate analytical insights and machine learning outcomes. However, data wrangling, the process of cleaning, transforming, and enriching raw data, remains one of the most time-consuming and error-prone stages of data science. This research introduces LLama Wrangler, an intelligent and secure platform for automated data wrangling powered by Large Language Models (LLMs). The system simplifies the data preparation process by integrating artificial intelligence and cybersecurity to ensure both automation and data privacy. LLama Wrangler automates tasks such as data cleaning, feature type inference, data enrichment, and transformation using intelligent LLM-based algorithms. Furthermore, it embeds security mechanisms like encryption, access control, and privacy-preserving computation to handle sensitive data securely. By automating the wrangling pipeline, the system reduces human intervention by over 70%, minimizes errors, and enhances data integrity. Experimental results show that LLama Wrangler significantly improves data quality and model performance. This paper explores the motivation, methodology, architecture, evaluation, and future prospects of this innovative solution.