Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security

Generative artificial intelligence (AI) is reshaping supply chain security, providing innovative tools for risk management while introducing formidable new threats. This chapter dissects AI-generated phishing, deepfakes, malicious code, and data poisoning, revealing how attackers compromise workflows, finances, and strategic decisions. It also shows how organizations can adopt anomaly detection, zero-trust architectures, and robust governance to block infiltration. Real-world scenarios illustrate the interplay of advanced tactics and effective defenses, emphasizing how staff training, multi-factor verification, and policy oversight curtail these threats. Generative AI can enhance resilience, but only if firms integrate secure AI pipelines and collaborate across the chain. Consequently, holistic planning and vigilant coordination among stakeholders are essential to ensure that AI-driven innovations bolster, rather than undermine, supply chain stability.

In most recent time, the global landscape of supply chain management has experienced unprecedented challenges during the COVID-19 pandemic, which has significantly impacted the routine and smooth operations of different firms in the United States. This paper explains about the importance of artificial intelligence (AI) and machine learning (ML) in the mitigation of these disruptions and possible ways of improving supply chain security and its efficiency. This research adopted a questionnaire survey involving 281 managers, with the aim to comprehensively examine the current state of AI integration across the U.S. supply chain sector, with focus on some key components like real-time tracking, cost optimization, and risk management. A mixed method approach was adopted for this research, utilizing both inferential and descriptive analyses to unravel insights and trends into the role of AI in enhancing supply chain security. The results indicate that integrating AI, most especially through cost optimization, real-time tracking, and risk management components, emerges as a significant determinant of supply chain security in the United States. Real-time tracking technologies are identified as crucial for monitoring shipments and assets, enabling quick responses to security incidents, and ensuring end-to-end visibility throughout the supply chain. Despite the potential benefits, the study highlights challenges that hinder the widespread integration of AI technologies in the U.S. supply chain. The high cost of AI adoption and the limited availability of skilled personnel are major obstacles. To address these challenges, the paper proposes practical recommendations. Firstly, real-time tracking technologies are recommended to monitor shipments and assets, facilitating rapid responses to security incidents, and ensuring visibility across the entire supply chain. Furthermore, the paper suggests optimizing costs by investing in cost-effective security solutions. This includes leveraging AI for automated monitoring systems and adopting secure packaging measures. These strategies aim to minimize vulnerabilities without compromising security standards, offering a balanced approach to enhancing supply chain security while mitigating the financial implications associated with AI adoption.

Recent catastrophic events and risks which have disrupted economies and supply chains around the world create new avenues/agendas for supply chain risks and security. Researcher and practitioner are showing increasing research interest on this area. This research has an effort to identified key factors and relation in supply chain risks and security and development of research framework. Given the increasing awareness of this important topic, the purpose of this research is to study supply chain critical risks important for supply chain security perspective and to investigate the relationship between supply chain vulnerability and supply chain risk. Interpretive structural modelling (ISM) has emerged as a means to analyse the relational aspects of the variables. The objective of this paper is to identify critical enablers of supply chain security management and to establish and analyse relationships among these enables using interpretive structural modelling. This research is continuing in this direction.

With the development of the information technology industry and the expansion of software development requirements, the scale and complexity of software are increasing, which makes the software development process turn into a software supply chain with superior or subordinate relationships. However, due to the inherent fragility of the software supply chain and the uneven quality of third-party open-source software, accidents occur frequently, and the security of the software supply chain has received extensive attention. We first figure out the security risks faced by the software supply chain and then innovatively proposes a software supply chain management system based on blockchain. To the best of our knowledge, this is the first blockchain-based software supply chain management system. The transfer processes of all third-party components or software products in the system are permanently recorded in the distributed ledger through smart contracts, which become the basis when handling and tracking software supply chain security incidents.

The software supply chain is becoming a widespread analogy to designate the series of steps taken to go from source code published by developers to executables running on the users? computers. A security vulnerability in any of these steps puts users at risk, and evidence shows that attacks on the supply chain are becoming more common. The consequences of an attack on the software supply chain can be tragic in a society that relies on many interconnected software systems, and this has led research interest as well as governmental incentives for supply chain security to rise. GNU Guix is a software deployment tool and software distribution that supports provenance tracking, reproducible builds, and reproducible software environments. Unlike many software distributions, it consists exclusively of source code: it provides a set of package definitions that describe how to build code from source. Together, these properties set it apart from many deployment tools that center on the distribution of binaries. This paper focuses on one research question: how can Guix and similar systems allow users to securely update their software? Guix source code is distributed using the Git version control system; updating Guix-installed software packages means, first, updating the local copy of the Guix source code. Prior work on secure software updates focuses on systems very different from Guix -- systems such as Debian, Fedora, or PyPI where updating consists in fetching metadata about the latest binary artifacts available -- and is largely inapplicable in the context of Guix. By contrast, the main threats for Guix are attacks on its source code repository, which could lead users to run inauthentic code or to downgrade their system. Deployment tools that more closely resemble Guix, from Nix to Portage, either lack secure update mechanisms or suffer from shortcomings. Our main contribution is a model and tool to authenticate new Git revisions. We further show how, building on Git semantics, we build protections against downgrade attacks and related threats. We explain implementation choices. This work has been deployed in production two years ago, giving us insight on its actual use at scale every day. The Git checkout authentication at its core is applicable beyond the specific use case of Guix, and we think it could benefit to developer teams that use Git. As attacks on the software supply chain appear, security research is now looking at every link of the supply chain. Secure updates are one important aspect of the supply chain, but this paper also looks at the broader context: how Guix models and implements the supply chain, from upstream source code to binaries running on computers. While much recent work focuses on attestation -- certifying each link of the supply chain -- Guix takes a more radical approach: enabling independent verification of each step, building on reproducible builds, "bootstrappable" builds, and provenance tracking. The big picture shows how Guix can be used as the foundation of secure software supply chains.

Existing research has overwhelmingly emphasized the positive effects of generative artificial intelligence (AI) on corporate environmental, social, and governance (ESG) performance, while largely neglecting the risk of dimensional imbalance in ESG resource allocation and its potential contagion across supply chains. Drawing on utilitarian theory, this study introduces the novel concept of ESG opportunism and empirically examines the impact of generative AI adoption on its emergence and intensity. Results show that generative AI significantly heightens firms' opportunistic ESG behavior by increasing agency costs and weakening internal controls. This relationship is further amplified by stringent government environmental regulations and strong green investor preferences yet attenuated by greater analyst attention and higher-quality information disclosure. Moreover, a clear supply chain spillover effect is identified: generative AI adoption by focal firms transmits and intensifies ESG opportunism among both upstream suppliers and downstream customers. By challenging the dominant optimistic narrative surrounding generative AI's ESG implications, this study offers timely and critical insights for establishing responsible generative AI governance throughout global supply chains. • We define ESG opportunism as firms strong in environment but weak in social/governance, showing ESG isn't uniform. • Our data shows a strong positive connection between using generative AI and ESG opportunism. • Generative AI increases agency problems and weakens company controls, leading to more ESG opportunism. • We find supply chain spillover: a firm's generative AI use boosts ESG opportunism in suppliers and customers. • Government rules and green investors can unintentionally increase ESG opportunism; analysts and disclosure reduce it.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

The Effect of Facility Management in Supply Chain Security Operational Performance and Firm in Malaysia

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

ABSTRACTGenerative AI (Gen‐AI) refers to artificial intelligence (AI) models that are capable of generating original text, imagery, music, code, and even films on the basis of patterns identified in extensive datasets. Even though Generative AI (Gen‐AI) benefits many enterprises (i.e., business, healthcare, supply chain, and entertainment), it also introduces new concerns, particularly in the field of cybersecurity. Gen‐AI is a formidable tool for both hackers (i.e., Internet‐based attackers) and protectors (i.e., security experts), as its capacity to produce comprehensive, unique data patterns and mimic human behavior makes it extremely successful. Gen‐AI can be used to carry out sophisticated attacks, such as escaping detection systems using adaptive, randomized patterns that resemble real‐world activity. Improving intrusion detection systems, building adaptive firewalls, and establishing proactive defensive mechanisms capable of predicting and responding to attacks in real time can all help to improve security. This study investigates the dual nature of Gen‐AI for the domain of cybersecurity. It focuses on both its potential hazards and its defensive function in improving server security. In this paper, we propose a Gen‐AI‐envisioned bidirectional scheme for server attacks and protection. A practical implementation of the proposed scheme is also provided to assess its suitability in real‐world scenarios. Furthermore, a comparative performance analysis is provided, which identifies that the proposed scheme outperforms other similar competing schemes.

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

In response to the changing international landscape and the risks associated with China’s supply chain security, conducting policy simulations on semiconductor supply chain security helps clarify the industry’s policies and governance strategies for semiconductor supply chain security in China. It also enables a better understanding of the current state and focus areas of China’s semiconductor supply chain security, which is of great significance for improving the security levels of semiconductor supply chains across provinces and cities and for constructing a secure, efficient, and autonomous semiconductor supply chain system. Firstly, this paper reviews the current research on semiconductor supply chains, supply chain security, and industrial policies. Secondly, based on the industrial policies for semiconductor supply chain security, an evolutionary game model is constructed, involving government departments, chain owner enterprises, and upstream and downstream small and medium-sized enterprises (SMEs) within the supply chain. Finally, the MATLAB R2016b system simulation method is employed to conduct a policy simulation analysis of China’s semiconductor supply chain security and further analyze the industrial policies related to semiconductor supply chain security. The results show that: (1) Supply chain security depends on multi-agent collaborative governance, with government leadership, and chain owner enterprises driving innovation in SMEs, improving digitalization levels, and ensuring supply chain autonomy and control. (2) Increasing government management revenue, raising the responsibility costs for chain owner enterprises, and reducing the innovation costs for SMEs can accelerate the achievement of the ideal governance state. Lastly, policy recommendations are proposed to build an autonomous and controllable supply chain system.

In the fourth industrial revolution (Industry 4.0), Artificial Intelligence (AI) had been a cognitive science that produces actual value needed for relevant data with processing capabilities and algorithms. Manufacturing in the Internet-of-Thing (IoT) era will be more efficient, better quality, easier to manage, and more transparent through integration of physical and cyber technologies in Industry 4.0-based smart factories. Factory automation relies heavily on sensors and AI to make the system intelligent. Sensor technology advancements and developments linked to Industry 4.0 serve as the backbone for the inclusive expansion of industry and the economic success of any country. It is imperative that manufacturing organizations and supply chains have access to the latest low-cost sensor technology for collecting data and putting it to good use. Standard sensor types include position sensors, flow, temperature, flow rate, pressure, and force. A wide range of fields, including motorsport, health care, manufacturing, the armed forces, and agriculture all make use of them on a day-to-day basis. Increasing efficiency through automation is the goal of Industry 4.0. The purpose of this paper is to provide a brief overview and viewpoint on the most recent advancements in AI and the associated problems.Attempts to define the main ideas and tools behind this new era of manufacturing in the early years of the so-called fourth industrial revolution (Industry 4.0) always ended up referring to the concept of smart machines that could communicate with each other and with the environment. When it comes to the new industry 4.0, it’s the defined cyber physical systems connected by the IoT that get all the attention. Nonetheless, several tools and applications will benefit the new industrial environment, complementing the actual formation of a smart, embedded system capable of performing autonomous tasks. And the majority of these revolutionary ideas are based on the same background theory as artificial intelligence, in which the analysis and filtration of massive amounts of incoming data from various types of sensors aids in the interpretation and recommendation of the best course of action. As a result, artificial intelligence science is well suited to the challenges that arise during the fourth industrial revolution’s consolidation. The purpose of this paper is to provide a brief overview and viewpoint on the most recent advancements in AI and the associated problems.KeywordsIndustry 4.0Smart sensorArtificial Intelligence (AI)Machine Learning (ML)Digital metrology