## 4 Results and Discussions
### Implementation Challenges
#### 4.4.2 Cybersecurity, Privacy, and Data Management  
Digitally driven industrial systems face increasingly complex and evolving security threats as technology advances. Table 12 outlines the various barriers to the adoption and secure operation of digitally driven industrial systems, categorizing them into (1) cybersecurity and system vulnerabilities, (2) data management and protection, and (3) human and ethical concerns. These advancements have broadened security concerns beyond simple equipment failures to encompass vulnerabilities in crucial operational layers, including process control, monitoring, encryption, and software systems [3]. The increasing sophistication and interconnectedness of digital systems, such as CPS, DTs, IoT, and XR technologies, pose escalating cybersecurity, privacy, and ethical challenges across industrial environments [9; 12; 18; 35; 81]. For instance, cyberattacks, such as ransomware, directly jeopardize automated safety systems, leading to unintended operations, compromised protective measures, endangering both workers and operations, and substantial societal and financial losses [7; 8; 9]. In addition, many IoT devices are produced rapidly without sufficient attention to security vulnerabilities, risks, and potential threats [3], and their integration into large-scale industrial systems heightens the attack surface, contributing to broader cybersecurity threats and compromising worker health and safety [53]. Hence, unauthorized access to industrial control systems can cause production halts, data leaks, and safety risks for workers in automated settings. The second overarching barrier category, data management and protection, addresses the intricate challenges associated with ensuring data integrity, security, and efficient handling in smart manufacturing environments. Cybersecurity in this context is complex due to the lack of standardized protocols, unprotected remote configurations, and integration of diverse socio-technical systems. For instance, DTs are critically reliant on high-quality, real-time data, which is often difficult to collect, manage, and scale securely. The integrity of this data is very important, as inaccuracies can lead to flawed predictions and hazardous situations [6]. Cyber threats are commonly addressed through cybersecurity measures like data encryption, robust enterprise security architectures, authentication protocols, and blockchain technologies [15; 18; 53]. Although encrypting industrial data can prevent leakage and tampering, the encryption and decryption processes can delay data transmission, impacting its timeliness and stability, and affecting performance and real-time decision-making [4]. Furthermore, reliance on cloud computing for centralized data management introduces the risk of data leakage [4] and limitations in consistency and reliability [9]. On the other hand, edge computing offers a decentralized alternative to mitigate some of these concerns, but it requires a specific infrastructure, which can be challenging. Finally, human and ethical concerns explore the profound implications of advanced digital technologies for the workforce. Beyond technical risks, workforce monitoring through advanced digital technologies also raises serious ethical and privacy concerns regarding employee autonomy and data use. The deployment of XR systems, wearable technologies, and intelligent sensors enables the collection of highly sensitive personal data, including biometric signals, emotional states, and motion profiles [4; 12; 18; 22; 79]. This raises serious ethical questions about employee autonomy, privacy, and consent, with concerns extending to the potential for corporate misuse of such data and vulnerability to external data breaches. For example, data in the IM is exploited by major corporations,with personal information and biometric data already exposed online, raising both privacy and ethical concerns [4].