Optimizing security in the Metaverse using DLP – Data Loss Prevention and Paraconsistent Logic

Considering the growing technological innovation with the use of the Metaverse as an environment for educational, corporate, and governmental interaction, in contrast to the risk of cyberattacks, there is an urgent need to strengthen its security, especially when there is the possibility of transacting assets with NFTs—Non-Fungible Tokens—which are high-value objects acquired and traded through blockchain technology. The objective of this article is to propose a research framework to optimize the security of these NFT assets using DLP—Data Loss Prevention—and Paraconsistent Logic to identify threats not only preventively but also by actively detecting loss, theft, misuse, and leakage of these types of assets during the use of the Metaverse. With a literature review on the Metaverse, DLP—Data Loss Prevention, Evidential Annotated Paraconsistent Logic Eτ, Artificial Intelligence techniques, NFTs—Non-Fungible Tokens, and data protection, the study will employ a Python program to conduct applied research using data from a transportation company, which shows a 37% data loss rate in its analysis. Through this Artificial Intelligence process and statistical concepts, compared to the minimization of data loss in the analysis using Evidential Annotated Paraconsistent Logic, Eτ resulted in 23%, indicating a significant difference of 15%, complemented as a tool to improve decision-making accuracy