A Framework for Secure Data Processing Using AI Agents in Business Intelligence Applications

Abstract

Increased usage of Business Intelligence (BI) systems to make data-driven decisions has made data privacy and security a bigger concern. Due to the complexity and scale of BI environments today, traditional security mechanisms are not adequate. In this paper we introduce a new framework for using AI agents in BI applications for secure processing of data. Using a risk-based approach, the framework combines elements of machine learning and artificial intelligence to detect anomalous behavior, protect sensitive data, and automate compliance with privacy regulations — all in an integrated manner in real-time. The framework uses AI agents to adaptively modify security policies by analyzing contextual data ensuring that pertinent data protection is achieved across the entire stack of BI systems. We describe the details of our proposed framework and show how it can be used in large-scale business scenarios. Additionally, empirical outcomes confirm the effectiveness of the framework in ensuring confidentiality, integrity, and availability of data, as well as enhancing performance of the system. The results are significant and reveal new insights into buffer location reflection data that can be used to improve BI system security, suggesting a potent solution to the increasing demands for current data processing in business intelligence applications.