###### Abstract
To answer these questions, we start by identifying the main requirements of IAM systems, especially in industrial environments. Then, we explore various AI-driven methods, including computer vision for facial recognition, biometric authentication, and multifactor authentication (MFA), highlighting their capabilities in providing robust identification and authentication solutions. We also discuss the advanced capabilities that AI brings to IAM systems and how these capabilities enhance security and operational efficiency in industrial environments. Furthermore, emerging trends and future directions in AI applications for industrial IAM are also discussed, providing insights into the potential of AI to transform security measures in industrial settings. Finally, this article also shows as conclusion the benefits and challenges in implementing AI-based IAM systems. In order to carry out this comprehensive review, our methodology involved the following steps. Firstly, a literature search on academic databases was conducted using carefully selected keywords relevant to the research questions such as \"artificial intelligence in industrial identity management\", \"authentication in industrial settings\", among others, ensuring that the selected studies were directly applicable to the subject matter. These sources are recognized for their relevance to industrial cybersecurity and identity management issues. This allowed us to identify a wide range of peer-reviewed articles, conference papers, and other credible sources. Secondly, inclusion and exclusion criteria were applied to ensure that only high-quality, relevant studies were selected. In our review, we included studies that specifically address the application of AI techniques in identity and access management (IAM), focusing primarily on peer-reviewed journal articles and conference papers published within the last 10 years. However, some older papers were included due to their relevance. The selected papers discuss AI-driven methods such as machine learning, anomaly detection, and automated decision-making in the context of IAM security. Articles not focused on AI or IAM, outdated technologies, non-peer-reviewed sources, and duplicate studies were excluded. The identified studies were evaluated according to their relevance, methodology, and scientific contribution, focusing on practical solutions for authentication and identity management in industry. This process aimed to eliminate bias and focus on the most pertinent research to address our research questions. Finally, the synthesis and analysis of the identified studies was conducted, comparing, contrasting, and identifying trends, gaps, and areas of consensus or disagreement in the literature. The information extracted and the corresponding articles were then organized according to different categories related with the research questions trying to identify the main requirements, use of AI technologies, advanced capabilities, trends, and challenges in the field of industrial IAM systems. This methodology ensures that the review covers the most current and relevant solutions, highlighting innovations and challenges in implementing IAM systems in industrial environments. Table 11 is provided in the Appendix A to offer a clearer overview of the reviewed works. This table summarizes the application domain, objectives, methodologies, and strengths of the reviewed articles, offering readers a concise snapshot to better understand the scope and contributions of each study.