IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 4, December 2024, pp. 3727~3738 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i4.pp3727-3738
3727
Methodology applied to computer audit with artificial intelligence: a systematic review Sheyla Reymundez Suarez, Bryan Martínez Huamani, María Acuña Meléndez, Christian Ovalle Departamento de Ingeniería, Facultad de Ingeniería, Universidad Tecnológica del Perú, Lima, Perú
Article Info
ABSTRACT
Article history:
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 metaanalyses (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.
Received Jan 7, 2024 Revised Mar 3, 2024 Accepted Mar 21, 2024 Keywords: Anomalies detection CAESAR8 Information technology audit Machine learning operations Predictive model
This is an open access article under the CC BY-SA license.
Corresponding Author: Christian Ovalle Departmento de Ingenieria, Universidad Tecnológica del Perú Lima, Perú Email: dovalle@utp.edu.pe
1.
INTRODUCTION In the current business landscape, where digitization is omnipresent, ensuring the integrity and security of information emerges as a critical pillar to ensure the continuity and efficiency of organizations. According to Goes [1], he reveal that 12% of companies have experienced significant security incidents in the last 12 months, generating economic losses that reach an average of 5% of their annual revenue. Furthermore, they suggest that the reputational impact of these incidents can be even more damaging, affecting the trust of customers and business partners. In the face of the increasing complexity of information systems, internal information technology auditing is emerging as an indispensable tool for assessing the effectiveness of the security management in place. Haller [2] indicate that 80% of companies consider internal auditing as an essential component of their risk management strategy. However, challenges remain, with 35% of organizations reporting difficulties in adapting their audit procedures to the rapid evolution of technology [3]. In this context, early anomaly detection and the implementation of machine learning operations (MLOps) practices are presented as innovative strategies that strengthen information security and improve the efficiency of auditing processes. Alsagheer et al. [4] indicate that organizations that adopt advanced approaches, such as MLOps integration, experience up to 45% fewer security incidents, reducing associated Journal homepage: http://ijai.iaescore.com