STATISTICAL MODELING TO ASSESS THE POTENTIAL FOR EXPOSURE OF CONFIDENTIAL DATA PROCESSED BY ROBOTS

Abstract

This article presents research that suggests a statistical model focused on analyzing the risks associated with the exposure of sensitive data in systems that use robotic process automation. The study is dedicated to understanding the obstacles related to the protection and privacy of information in automated operations. To achieve this, the model is based on the assessment of critical variables, such as authorized access levels, the nature of the data manipulated and possible technological weaknesses. The approach used involved the collection and analysis of practical data, in addition to the application of advanced statistical techniques, such as multiple linear regression, histograms and time series, to estimate the probability of security incidents occurring. The findings indicate that the lack of effective access control policies and flaws in security systems are among the main points of vulnerability. Furthermore, environments that handle large amounts of sensitive information or that have widely distributed access are more susceptible to risks. The model developed is intended to function as a valuable tool to help managers implement preventive strategies and improve security measures, thus contributing to the compliance and protection of systems. The research emphasizes the importance of constant updates to security practices and solutions that are in tune with technological advances.