## 1 Introduction  
Supply chain management plays a pivotal role in modern businesses, encompassing the coordination and optimization of various activities from raw material procurement to finished product delivery [1]. However, traditional supply chain systems often face significant challenges, such as lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. These challenges necessitate proactive and adaptable threat prevention, detection, and response security mechanisms. A comprehensive approach to supply chain security requires continuous assessments, collaborative efforts among stakeholders, and the implementation of robust cybersecurity measures, while utilizing the power of big data to identify, predict, and mitigate risks, ensuring the seamless flow of goods, services, and information [2; 3]. Traditionally, various mathematical and heuristic methods have been employed to optimize supply chain performance. For instance, [PERSON] et al. [4] proposed heuristic-based metaheuristics to address sustainable supply chain network design problems. [PERSON] et al. [5]reviewed game-theory-based models for green supply chain management, highlighting their effectiveness in capturing the strategic interactions among supply chain actors. Analytical models, such as the assignment stock inventory model, have also been developed for integrated supply chain optimization. These traditional approaches often focus on specific application scenarios and may struggle to adapt to the complex and dynamic nature of modern supply chains [6]. Furthermore, these methods often lack the ability to effectively handle the vast amounts of data generated in modern supply chains, limiting their potential for real-time optimization and risk mitigation [7]. To address the limitations of traditional methods and enhance supply chain security, researchers have been exploring the application of advanced technologies, such as blockchain and reinforcement learning. Blockchain technology has emerged as a potential solution for enhancing supply chain traceability, transparency, and trust. By leveraging distributed ledger and smart contract capabilities, blockchain enables secure, tamper-proof record-keeping and automated execution of supply chain transactions [8]. Increased transparency and immutability can help mitigate risks, such as counterfeiting, fraud, and data manipulation across the supply chain network. However, the integration of blockchain technology into supply chain management is still in its early stages, and challenges, such as scalability, interoperability, and regulatory compliance, need to be addressed [1]. Reinforcement learning techniques, particularly deep reinforcement learning (DRL), have shown promise in optimizing various aspects of supply chain performance, such as inventory management and network design [9]. DRL algorithms can learn optimal policies through interaction with the environment, adapting to dynamic and uncertain conditions. [PERSON] and [PERSON] [6] proposed a DRL and optimization approach for multi-echelon supply chains with uncertain demands, demonstrating its effectiveness in handling complex scenarios. [PERSON] et al. [10] applied DRL for capacitated supply chain optimization under demand uncertainty, showcasing its potential for real-world applications. By combining DRL with blockchain technology, researchers aim to create secure and efficient supply chain solutions that can proactively detect and mitigate threats while optimizing overall performance. However, the application of DRL in supply chain management is still an emerging field, and further research is needed to develop robust and scalable algorithms that can handle the complexity and variability of real-world supply chains. One promising approach to enhancing supply chain security is the adoption of the zero-trust (ZT) security model. The ZT principle assumes no implicit trust and continuously verifies and validates all supply chain entities and transactions [3].