Frequent itemset mining is a foundational technique in data mining, used extensively for discovering associations within large datasets. However, the presence of sensitive itemsets poses a significant challenge, necessitating data sanitization to protect privacy without compromising the utility of the mined itemsets. This paper introduces an AI-empowered approach to border sanitization that effectively hides sensitive itemsets while preserving the usability of non-sensitive itemsets. Our method involves generating frequent itemsets, identifying sensitive and non-sensitive borders, and applying optimization techniques such as safe sets and linear programming. The approach leverages advanced AI techniques, including Markov Random Fields, to optimize the sanitization process. Experimental results demonstrate the method’s effectiveness in maintaining data utility and privacy, highlighting the potential of AI in enhancing privacy-preserving data mining techniques.