Can We Trust Artificial Intelligence Models That Are Trained With Synthetic Data?

We’ve all seen those scenes from sci-fi movies where a scientist runs a program and then an artificial intelligence (AI) model magically appears to provide some mind-blowing results. It would be great if we could simply ask our AI model to provide us with output without having to first provide it with a data set. However, this is not how AI works. In order for an AI model to provide us with the desired product, it must first be trained on a data set. Data is a critical component of AI and deep learning that provides the foundation needed to create robust AI and predictive models. Sometimes, real data can be insufficient or inaccurate. To make effective use of data, it is imperative to conduct a thorough qualitative control and quantitative assessment. In most cases, the lack of quality resources is identified as the main obstacle to successful data-based decision-making.

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Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.