This research investigates the feasibility of using Large Language Models to enrich anonymized data while maintaining privacy protections.

• Demonstrates how LLMs can add value to anonymized datasets without compromising sensitive information
• Evaluates the practical limitations of using AI for data enrichment in privacy-sensitive contexts
• Explores the balance between data utility and privacy protection in AI-augmented datasets
• Establishes frameworks for responsibly applying LLMs to sensitive data environments

For security professionals, this research provides crucial insights into maintaining data privacy while still extracting business value from sensitive datasets through AI augmentation techniques.

Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment