Paper Contents
Abstract
In todays digital era, personal data leakage through textual communication has emerged as a critical cybersecurity concern. Sensitive information, such as identification numbers, financial details, and private contact information, can be unintentionally exposed via emails, documents, and chat platforms. This paper proposes an AI-based detection system that efficiently identifies such leakage risks in textual data using a combination of Regular Expressions (RegEx) for pattern matching and a Multinomial Naive Bayes classifier for contextual classification. The hybrid approach ensures both precision in pattern recognition and adaptability in handling diverse text formats. Experimental results demonstrate the systems high accuracy, validating its potential as a practical tool for personal data protection. Keywords : Data Leakage Detection, Personal Text Data, Regular Expressions, Naive Bayes Classifier, Data Privacy, AI-based Security, Textual Data Protection, Information Leakage.
Copyright
Copyright © 2025 Chitoor Venkat Rao Ajay Kumar. This is an open access article distributed under the Creative Commons Attribution License.
