Methodology applied to computer audit with artificial intelligence: a systematic review

• Autores: Reymundez Suarez, Sheyla, Martínez Huamani, Bryan, Acuña Meléndez, María, Ovalle, Christian
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/14511
• Enlace del recurso: https://hdl.handle.net/20.500.12867/14511; https://doi.org/10.11591/ijai.v13.i4.pp3727-3738
• Nivel de acceso: acceso abierto
• Materia: Anomalies detection; Machine learning operations; Predictive model; https://purl.org/pe-repo/ocde/ford#2.02.04

This systematic review focused on evaluating the impact of the machine learning operations (MLOps) methodology on anomaly detection and the integration of artificial intelligence (AI) projects in computer auditing. Data collection was carried out by searching for articles in databases, such as Scopus and PubMed, covering the period from 2018 to 2024. The rigorous application of the preferred reporting items for systematic reviews and meta analyses (PRISMA) methodology allowed 88 significant records to be selected from an initial set of 1,389, highlighting the completeness of the selection phase. Both quantitative and qualitative analysis of the data obtained revealed emerging trends in the research and provided key insights into the implementation of MLOps in AI projects, especially in response to increasing complexity, whereby the adoption of the MLOps methodology stands out as a crucial component to optimize anomaly detection and improve integration in the context of information technology auditing. This systematic approach not only consolidates current knowledge but also stands as an essential guide for researchers and practitioners, and the information derived from this systematic review provides valuable guidance for future practices and decisions at the intersection of AI and information technology auditing.