Artificial Intelligence Security through Input Data Quality Steps

Abstract

In times of digital transformation, data generation constitutes a new type of asset, as it requires new forms of organizational learning, giving rise to new business models. The focus of all this transformation is Artificial Intelligence (AI), a subfield of Computer Science responsible for creating computational resources with capabilities similar to human reasoning for automated problem-solving. Elements of mathematics and engineering are used to reproduce aspects of human intelligence, as well as insights from other areas such as philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, cybernetics, and linguistics. Hence the name AI. Machines learn to speak, write, interpret data, and solve problems through AI, a tool that is now essential for Industry 4.0, as a transition from industrial society to the knowledge and digital economy. In turn, Generative AI uses multimodal tools to work with elements such as spoken language, images, sounds, body movements, etc. All this technology requires innovations in companies through the adoption of frameworks that enable the selection and storage of qualified data. This original article was developed based on extensive bibliographic research and scientific materials. Its objective is to demonstrate the relevance of data analysis as a way to apply compliance practices to AI. As a result of this research, there was a recognized need for greater attention to input data quality processes for generative AI training.