## 4 Challenges and Limitations  
### Integration Challenges  
The integration of AI systems into maritime operations has been significantly enhanced by recent developments in communication technologies, particularly 5G networks and Non-Terrestrial Networks (NTNs), including satellite-based systems. These technologies address one of the primary barriers to AI adoption in the maritime domain: the lack of reliable connectivity in remote and open-sea environments. By ensuring constant data exchange between vessels, ports, and cloud-based infrastructures, 5G enables real-time decision-making for navigation, risk management, and predictive maintenance, which were previously constrained by intermittent or low-bandwidth connections [70; 85; 86; 80]. In addition to improving communication, IoT-enabled sensors are playing a pivotal role in transforming shipboard systems. These sensors, embedded in critical equipment, allow for real-time monitoring of the ship's operational health, environmental conditions, and fuel consumption. Coupled with edge computing, these data are processed locally on the vessel, reducing the need for high-capacity onboard servers while leveraging cloud computing to analyze complex patterns in equipment performance and navigation routes [85]. This hybrid model ensures that AI-driven decisions can be made in real time, optimizing operations even in data-constrained environments. However, despite these advancements, the integration of modern AI systems with legacy maritime infrastructure remains a significant challenge. Many ships currently in operation were not designed with AI and IoT systems in mind. As a result, retrofitting these vessels requires considerable investment in both hardware (e.g., sensors, connectivity modules) and software (e.g., AI-compatible platforms) [87]. Moreover, the heterogeneity of existing maritime systems, which vary significantly in their design and capabilities depending on their age, purpose, and geographic area of operation, complicates this integration [87]. Cybersecurity Risks  
As more maritime operations become digitized, cybersecurity has emerged as a critical concern. The reliance on real-time data streams, cloud-based decision-making platforms, and IoT-enabled systems exposes the maritime sector to potential cyberattacks. Recent studies have identified that compromised AI systems could lead to catastrophic failures, including navigational errors, unauthorized access to sensitive data, and even ship collisions [86; 87]. As a result, robust cybersecurity measures must be integrated into both new and existing infrastructures. This includes encrypting data, securing IoT devices, and implementing real-time monitoring systems to detect and mitigate potential threats [88]. To address these challenges, new cybersecurity frameworks are being developed specifically for maritime operations. These include secure communication protocols for vessel-to-port data exchanges and AI-based anomaly detection systems, which can identify and neutralize suspicious activity in real time. Ensuring that these protocols are standardized across the industry is essential for creating a unified approach to maritime cybersecurity [88]. Cost and Scalability  
The financial cost of upgrading maritime infrastructure to support AI integration is another key hurdle. While large shipping companies with extensive fleets may have the resources to invest in cutting-edge technologies, smaller operators may struggle to afford the necessary upgrades. As AI becomes increasingly vital for ensuring safety and operational efficiency, there is a growing need for scalable solutions that can be adapted to different vessel sizes and operational scopes [85; 87]. Modular AI systems are emerging as a promising solution to this problem. These systems allow operators to implement AI technologies incrementally, starting with basic functionalities like predictive maintenance and gradually adding more complex applications such as autonomous navigation and crew resource management.