Enhancing Data Security Resilience in AI-Driven Digital Transformation: Exploring Industry Challenges and Solutions Through ALCOA+ Principles.

Abstract

The Medicines and Healthcare Products Regulatory Agency (MHRA) defines data integrity as the maintenance of accuracy, consistency, and completeness of data over time. Recently, "artificial intelligence" has become prevalent across industries, education, culture,and technology, denoting systems that mimic human intelligence and critical thinking using computers and related technologies. This article examines the construction of a robust artificial intelligence (AI) system and the incorporation of ALCOA+ principles for data validation, with a specific focus on enhancing data certainty and security. This study was carried out through a comprehensive review of various Scopus-indexed literature over the past decade. Results and Discussion: AI has been widely applied in Manufacturing System Optimization, involving organizing production systems, including machines, robots, conveyors, and related operations like maintenance and material handling. Moreover, it's used for Process Monitoring, Diagnostics, and Prognostics in medicine, as well as supervision and regulation in industries. Yet, it's not immune to shortcomings, which could result in system biases and jeopardize data security. This article explores the creation of a robust AI system, implementing ALCOA+ for data validation in AI-Driven Digital Transformation to improve data certainty and security in industries. It involves systematically recording AI system activities, ensuring database validity, sustaining data recording practices, regularly updating records, ensuring authenticity and completeness, and facilitating data accessibility for review and audits. As AI integration in education advances, there's a crucial need for oversight to maintain data integrity in these systems.