Risk Analysis on Cognitive Behavioral Theory using Paraconsistent Logic

Currently, many patients in psychological therapy clinics have phobias, noting that this personal information is sensitive, both from the company’s headquarters and its branches, and is recorded in a cloud environment, leaving it exposed and subject to data theft by hackers or data leaks by malicious users. Companies around the world are subject to this type of risk and, therefore, seek not only to protect their customers’ data when dealing with sensitive health data, but also preventive monitoring to avoid the disclosure of information. This study presents the use of SIEM - Security Information Event Monitoring, that is, a tool for collecting information on risk analysis of events that occur with patients using Evidential Annotated Paraconsistent Logic Eτ. This identifies threats that exploit their vulnerabilities, in case of leaking of this data, through the dissemination of malware, or phishing through spam. Automated market tools could have been used, but in them, the risk analysis is only at the basic or initial level, with no secondary levels. However, when a customized SIEM is used, suspicious situations or situations that require further evaluation are detected, at which point an alert is automatically triggered for the DPO – Data Protection Officer who calls on a psychologist to understand the impact of the leaked data. Thus, the article presents exploratory research using SIEM tool with paraconsistent logic, focusing on risk analysis based on artificial intelligence on vulnerabilities related to sensitive data of clinic patients, with a focus on the protection and privacy of their sensitive personal data. Therefore, compared to security regarding data loss minimization in the analysis using SIEM with the aid of Evidential Annotated Paraconsistent Logic, Eτ resulted in 27%, indicating a significant difference as a complementary tool in improving assertiveness at the time of decision making.