The goal of Industry 4.0 is the sustainable modernization and simplification of industrial processes and production. It uses cutting-edge technologies such as additive manufacturing, robotics, and IoT [12]. Security has always been a critical element in an industrial context as it prevents production interruptions, prevents accidents, and improves staff training. Using sensing devices and data processing through ML methods has resulted in smart personal protective equipment (PPE) [13] that has helped monitor workers' health in the workplace and check that such smart devices are being worn correctly. The challenges in the field of operator safety are creating more compact, more ergonomic, easily wearable, and self-recharging devices [14]. With the digitalization of industry, new specific security risks have emerged, such as data theft and manipulation, attacks on automation systems, and cybersecurity. The solutions analyzed in this paper aim to overcome implementation difficulties such as technological complexity, the management of large amounts of data, the evolution of cyber threats, and threat classification [15]. Additive manufacturing (AM), an essential pillar of digital technology, is a production method that changes the traditional manufacturing process. AM is a general name for manufacturing technologies that add material layer by layer to create desired physical parts. Many of the steps required by traditional production processes have been eliminated with additive manufacturing [16]. For example, AM removes the need to design the machining cycle, eliminates the need for bulky machining centers, eliminates the need to manage chips after machining, and makes maintenance easier and faster [16]. This technology, which initially emerged as rapid prototyping (RP) systems, has evolved into direct production by developing various AM processes. Although there are many varieties, the most prominent AM technologies are stereolithography (SLA), selective laser sintering (SLS), three-dimensional printing (3 DP), laminated object fabrication, and fused deposition modeling (FDM) [17]. One of the main applications of the IoT paradigm in the field of AM within smart manufacturing is monitoring the printing process to eliminate or minimize production defects [18]. For example, using an augmented interface or a digital twin for conventional AM processes allows for early identification of manufacturing defects, thus increasing the predictability of the process [19; 20]. One real-world example of the IoT applied to additive manufacturing (AM) is given by the Fast Radius company, now part of SyBridge Technologies company (Spring Lake Drive Itasca, IL, USA) [21]. In 2018, the World Economic Forum recognized its Chicago facility as one of the top nine smart factories worldwide, specialized in AM alongside CNC machining and injection molding. A key element of Fast Radius' success is its proprietary IoT platform, which collects data from every part designed and produced in its virtual warehouse. This data helps teams identify suitable applications for 3D printing and assess the engineering and economic feasibility of manufacturing components [21]. The Geico Taikisha Group gives another example of a real-world IoT application. The Geico Group, a global leader in designing and building automated auto body paint shops, leveraged IoT digital twin technology to stay connected with customers by providing access to detailed production data [22]. This connection allowed Geico to monitor machine performance, identify common issues, and enhance the performance of paint shop cells for their clients. Customers also benefited from these digital twin capabilities, enabling them to run live simulations to explore potential efficiencies, address vulnerabilities, and achieve cost savings. This transparency helped predict maintenance needs and downtime, reducing costs. Additionally, with smart energy management features, customers could optimize resource usage for sustainability and further savings [22].