The 3x2A Strategy for Societal Adaptation in the GenAI Era: A Framework for Human-AI Synergy

This review paper explores how Generative AI (GAI) and Large Language Models (LLMs) have the potential to reshape smart cities in the industry 5.0 era. By examining case studies and relevant literature, we analyze the influence of these technologies on industrial operations and urban management. The paper focuses on GAI as a key tool for optimizing industries and enabling predictive maintenance, while demonstrating how experts can leverage LLMs to enhance municipal services and communication with citizens. It also discusses the practical and ethical challenges of implementing these technologies. Additionally, the paper highlights emerging trends, illustrated through real-world examples ranging from factories to city-wide pilot projects, and identifies potential pitfalls. The widespread adoption of GAI faces obstacles such as infrastructure constraints and the lack of specialized knowledge needed for effective implementation. While LLMs open new opportunities for citizen services in smart cities, they also raise concerns about privacy, which this study seeks to address. Finally, the paper suggests future research areas, including the development of new ethical AI frameworks and long-term studies on the societal impacts of these technologies. This paper serves as a starting point for industrial leaders and urban developers to navigate the complexities of integrating GAI and LLMs, balancing technological innovation with ethical considerations.

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today’s breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL·E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the Scaling Laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.

This paper is concerned with issues of trust and bias in generative AI in general, and chatbots based on large language models in particular (e.g. ChatGPT). The discussion argues that intercultural communication scholars must do more to better understand generative AI and more specifically large language models, as such technologies produce and circulate discourse in an ostensibly impartial way, reinforcing the widespread assumption that machines are objective resources for societies to learn about important intercultural issues, such as racism and discrimination. Consequently, there is an urgent need to understand how trust and bias factor into the ways in which such technologies deal with topics and themes central to intercultural communication. It is also important to scrutinize the ways in which societies make use of AI and large language models to carry out important social actions and practices, such as teaching and learning about historical or political issues.

This review paper examines how Generative AI (GAI) and Large Language Models (LLMs) can transform smart cities in the Industry 5.0 era. Through selected case studies and portions of the literature, we analyze these technologies’ impact on industrial processes and urban management. The paper targets GAI as an enabler for industrial optimization and predictive maintenance, underlining how domain experts can work with LLMs to improve municipal services and citizen communication, while addressing the practical and ethical challenges in deploying these technologies. We also highlight promising trends, as reflected in real-world case studies ranging from factories to city-wide test-beds and identify pitfalls to avoid. Widespread adoption of GAI still faces challenges that include infrastructure and lack of specialized knowledge as a limitation of proper implementation. While LLMs enable new services for citizens in smart cities, they also expose certain privacy issues, which we aim to investigate in this study. Finally, as a way forward, the paper suggests future research directions covering new ethical AI frameworks and long-term studies on societal impacts. Our paper is a starting point for industrial pioneers and urban developers to navigate the complexity of GAI and LLM integration, balancing the demands of technological innovation on one hand and ethical responsibility on the other.

In the rapidly evolving domain of cyber security, the advent of AI (AI) and ML (ML) has marked a paradigmatic shift, enhancing the accuracy and efficiency of identifying and mitigating vulnerabilities. This paper delves into the dual-edged dynamics of AI in cyber security, examining the historical trajectory from manual identification methods to AI-driven approaches. While AI and ML have significantly improved the Accuracy and timeliness that accelerated the detection and analysis of cyber threats, offering predictive insights and adaptive defenses, they also introduce new challenges, including ethical considerations, transparency issues, and the potential for adversarial manipulations. The integration of Generative AI and Large Language Models (LLMs) further complicates the security landscape, presenting both novel opportunities and vulnerabilities. This paper emphasizes the critical role of responsible AI and governance in navigating these complexities, advocating for a balanced approach that leverages AI's strengths while addressing its limitations. As cyber security continues to adapt to these technological advancements, fostering innovation while ensuring ethical and transparent practices remains paramount. Significant findings from the study reveal that the integration of AI (AI), Large Language Models (LLMs), and Generative AI has markedly enhanced both accuracy and timeliness in cyber security operations. This advancement facilitates the early detection of threats and vulnerabilities, thereby substantially improving preventative measures. Furthermore, Generative AI and LLMs have pioneered innovative pathways for Threat Modeling techniques, revolutionizing the approach to threat prevention. Both Cyber Security Specialists and Hackers use these advances to improve upon each other continue this evolving trend.

This study explores the transformative impact of Generative AI and Large Language Models (LLMs) on software development by leveraging intelligent automation. It delves into sophisticated methods for refining LLMs to enhance code generation, improve adaptive learning abilities, and support autonomous software engineering processes [1] [2]. This study investigates how these technologies can be integrated into current development workflows to tackle issues such as code quality, scalability, and ethical concerns. Innovative strategies to boost model performance have been introduced, such as targeted data augmentation and domain-specific pre-training. The results showed notable advancements in the accuracy, efficiency, and adaptability of code generation across various programming languages and frameworks. Finally, the study discusses the implications of these developments for future software development and outlines a roadmap for further research and industrial implementation. Keywords — Generative AI, Large Language Models, Intelligent Automation, Software Development, Code Generation, Adaptive Learning, Autonomous Engineering, Data Augmentation, Domain-Specific Pre-trainings, Transfer Learning, Code Quality, Ethical Considerations.

Software testing is a challenging topic in software engineering education and requires creative approaches to engage learners. For example, the Code Defenders game has students compete over a Java class under test by writing effective tests and mutants. While such gamified approaches deal with problems of motivation and engagement, students may nevertheless require help to put testing concepts into practice. The recent widespread diffusion of Generative AI and Large Language Models raises the question of whether and how these disruptive technologies could address this problem, for example, by providing explanations of unclear topics and guidance for writing tests. However, such technologies might also be misused or produce inaccurate answers, which would negatively impact learning. To shed more light on this situation, we conducted the first empirical study investigating how students learn and practice new software testing concepts in the context of the Code Defenders testing game, supported by a smart assistant based on a widely known, commercial Large Language Model. Our study shows that students had unrealistic expectations about the smart assistant, “blindly” trusting any output it generated, and often trying to use it to obtain solutions for testing exercises directly. Consequently, students who resorted to the smart assistant more often were less effective and efficient than those who did not. For instance, they wrote 8.6% fewer tests, and their tests were not useful in 78.0% of the cases. We conclude that giving unrestricted and unguided access to Large Language Models might generally impair learning. Thus, we believe our study helps to raise awareness about the implications of using Generative AI and Large Language Models in Computer Science Education and provides guidance towards developing better and smarter learning tools.

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

Generative AI and Large Language Models (LLMs) are revolutionizing industries by enabling advanced natural language understanding and content creation.However, their adoption introduces significant security, privacy and compliance risks.This paper identifies key vulnerabilities, highlights the OWASP Top 10 risks specific to LLMs and presents a comprehensive security framework leveraging AI Security Posture Management (AISPM) and proactive scanning techniques.Recommendations for secure, ethical and compliant deployment of Generative AI are provided, ensuring resilience and trustworthiness in modern AI systems.

Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences.

The prevalence of Generative AI (GenAI) and Large Language Models (LLMs) is increasing rapidly. For Extension professionals, the utilization of prompt engineering is key to leveraging GenAI and LLMs effectively. Prompt engineering involves crafting prompts that elicit desired LLM responses. This article discusses prompt engineering principles, providing examples and guidance. The application of prompt engineering in Extension is explored, showcasing its potential to enhance programs, deliver personalized advice, engage audiences, and disseminate research-based information. By learning prompt engineering skills, Extension professionals can harness the power of GenAI and LLMs, enhancing their ability to address complex challenges in the 21st century.