Privacy-Aware AI Logging Framework for Omni-commerce Applications

Abstract

The rapid growth of omni-commerce applications has led to increased reliance on artificial intelligence systems for personalised services, demand forecasting, and customer behaviour analysis. However, a huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed paper is a Privacy-Aware AI Logging Framework that is aimed at providing secure, compliant and efficient management of log data within omni-commerce settings. A huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed system adopts a modular architecture that supports real-time data processing while minimising privacy risks without compromising system performance. Experimental analysis shows that the framework has a good balance between data utility and privacy protection, as it has a high level of accuracy in data analytics and minimal exposure to sensitive data. This approach enhances trust in AI-driven omni-commerce platforms and provides a scalable solution for privacy-aware data management in modern digital ecosystems.