Muhammad Tayyab

In this chapter, the authors explore the idea of a Generative Divide, wherein the commercialisation of advanced Large Language Models results in a pay-to-learn scenario. This situation gives an advantage to students who can pay subscription tools over those who rely on versions with fewer features. The technical analysis looks at the Impossibility Theorems about fairness metrics, particularly the tension between calibration and equalised odds. It also examines strategies to fix the problem, such as adversarial debiasing and causal inference. Moreover, the authors discuss the paradox of accessibility, highlighting how AI acts as a cognitive aid for neurodivergent learners, yet falls short for marginalised groups due to high error rates in speech recognition for African American Vernacular English (AAVE). The chapter concludes by suggesting operational governance frameworks, like the “AI Literacy Heptagon” and Algorithmic Impact Assessments, to shift from techno-solutionism towards a deliberately inclusive educational framework.

Ensuring secure data transfer over a network involves many concerns and has been considered challenging. The most commonly used technique for securing data is encryption, which allows data to be encrypted to avoid unauthorized access. Image steganography is a special kind of encryption in which an additional layer of security is provided by hiding the data in a carrier image. This process can be performed in multiple ways. For example, Least Significant Bit (LSB)-Steganography is a technique in which the data is hidden within an image by replacing the least significant bits of the images with the data which is to be encrypted. Such techniques are exploited by cyber security professionals and application developers to implement encryption according to the scenarios and domain requirements. In this research, we have proposed a metamodel based on steganography using the LSB technique to autogenerate code in any programming language.

The use of artificial intelligence (AI) in cybersecurity is a need due to the outpacing of conventional security measures by the fast growth of cyber threats. Exploring the function of machine learning (ML), deep learning (DL), and natural language processing (NLP) in recogniz- ing and reacting to new cyber dangers, this chapter offers a thorough introduction to AI-powered threat identification. The paper contrasts AI- based systems with more traditional approaches, demonstrating how AI can better identify unknown assaults, adapt to new threats, and decrease the number of false positives. It touches on topics such as training models using industry-specific data, ensuring they can commu- nicate with one other, and keeping them up-to-date and monitored. Data privacy, legislation compliance, and best practices for AI threat detection are also discussed. We discuss the need for human analysts to work with AI tools and how to overcome obstacles, including AI bias, complicated systems, and ethical considerations.

Web engineering is being transformed rapidly by Artificial Intelligence (AI), particularly by Generative AI (GenAI), through its potential for automation in content creation, source code generation, design creation, and optimization. Along with the tremendous benefits that the integration of GenAI and Web Engineering offers, significant security concerns arise related to secure deployment and data privacy. This chapter explores the importance and growing adoption of GenAI tools and techniques in web development, emphasizing the security considerations, such as potential vulnerabilities, including model manipulation, adversarial attacks, and data leakage. A thorough review of the existing studies reveals the primary security threats and attacks, including the recommended countermeasures. Moreover, the ethical implications of GenAI for web engineering have also been investigated. By highlighting the security and privacy concerns, this study shall benefit the researchers, developers, and organizations in adapting careful steps for GenAI utilization for web engineering process, taking into account the issues and challenges related to the user trust, privacy, security, and data integrity.

Generative AI is transforming the art and science of web engineering by automating content generation, design and development through models like GAN, VAE or Transformers. These models can produce authentic text like a writer and other media, including images, so developers would save more time in web development and improve creativity and scalability. From automated web design and content personalization to adaptive user interface or code optimization, the uses of generative AI could be limitless. Integrating AI with web engineering allows developers to generate dynamic and tailored user interface applications that adapt to the unique requirements of each user. Nevertheless, issues like data quality, bias and ethical considerations require ongoing attention to ensure responsible AI usage. Overall, generative AI can revolutionize web development for a future of innovation and more intelligent, efficient, user-centric web applications.

The advancement and proliferation of generative Artificial Intelligence (AI) tools and models is noticeable in recent digital transformation. Several generative AI tools, such as, ChatGPT, CoPilot, Bard have become famous and received widespread adoption and usage. The popularity of generative AI in every sector reflects its potential to boost the cybersecurity domain as well, particularly to serve security analysts. Despite the ethical, social, and privacy implications of generative AI, it can be effectively utilized in various ways. Recent reports have indicated the use of generative AI tools in both offensive and defensive sides of cybersecurity. Where the use of generative AI by the attackers can lead to an increase in the number and quality of attacks, it can also be applied in the same way to empower the defenders to better analyze the attacks and develop advanced security practices. In this chapter, the role of security analysts in identifying and mitigating cyber threats has been presented along with the role of generative AI and its potential to empower their tasks. In addition, the challenges faced by security analysts are elaborated, including information overload, alert fatigue, and the complexity of modern cyber-attacks. Moreover, a discussion is provided on how generative AI can enhance threat detection, incident response, and threat intelligence analysis. Furthermore, the characteristics of generative AI to support anomaly detection, pattern recognition, visualization and interpretation of security data, automated threat simulation and response along with the case studies have been presented. Lastly, the ethical and legal considerations as well as the future directions and emerging trends of the transformative potential of generative AI for empowering cybersecurity operations have been discussed. Overall, this research is an effort towards exploring the positive prospective of generative AI tools for not only increasing the robustness of security practices but also to empower the capabilities of security analysts.

This chapter discusses how blockchain technology might be combined with artificial intelligence-driven healthcare systems to meet the urgent demand for improved data security and system efficiency. AI brings vulnerabilities to data breaches and model manipulation as it transforms healthcare using diagnostics and patient management, requiring strong security measures. Emerging as a solution with a distributed, tamper-proof ledger guaranteeing data integrity and against illegal access is blockchain technology. The authors address the two advantages of this integration: blockchain guarantees artificial intelligence operations and improves data interchange efficiency in healthcare systems. Aiming to increase healthcare services' general dependability and efficiency, the topics discussed include ways to strengthen data privacy, guarantee regulatory compliance, and safe medical data interchange. The chapter also covers technical and operational issues related to combining various technologies and presents strategic directions to negotiate their complexity. Ultimately, this conversation emphasizes how blockchain might create a patient-centered, safe, and efficient healthcare ecosystem, giving readers a complete knowledge of the consequences and advantages of blockchain in environments enhanced by artificial intelligence.

Today, with the involvement of artificial intelligence (AI), where sensitive information is being collected massively to analyse data for various purposes, privacy-preserving data analysis has gained much importance in the data-driven world. Traditionally, using normal data analysis techniques, privacy-preserving methods often faced several security challenges to ensure the privacy of individuals and organizations where the shared data resources have been used for collaboration or decision-making. Hence, generative artificial intelligence has offered numerous solutions that can ensure privacy to such security concerns by enabling privacy-preserving analysis while getting fruitful knowledge from data. Therefore, in this chapter, the authors aim to provide an overview of privacy-preserving data analysis techniques using AI-based approaches such as differential privacy, federated learning (FL), generative adversarial network (GAN), and other variational auto-encoders (VAEs). While providing individuals protection in privacy, they have discussed how generative AI can be used to preserve statistical properties of plain data using synthetic data analysis. Moreover, some real-world applications have also been part of the discussion to illustrate the effectiveness of such techniques in various domains. Finally, they have also provided some future challenges, limitations, and ethical considerations for implementing these techniques effectively and have provided future directions in the rapidly emerging domain.

The logistics industry is rapidly transforming, driven by emerging technologies and evolving cybersecurity threats. These threats have also affected the industries financially on one side but also fined some of the most reputed industries due to poor quality supplied to the customers. Many cybersecurity threats have also been reported. However, due to the usage of cloud services, many more threats have been opened to this industry. Therefore, this study explores the complex relationship between logistics and cybersecurity, highlighting the industry's challenges and opportunities. The adoption of technologies like the Internet of Things (IoT), cloud computing, and artificial intelligence offers new possibilities for efficiency and visibility, but also introduces cybersecurity vulnerabilities that must be addressed. From securing supply chains to implementing advanced encryption and access control, logistics companies are navigating a dynamic landscape that demands proactive cybersecurity measures. Furthermore, this study underscores the importance of staying informed about emerging threats and trends while adopting innovative solutions to protect data, operations, and customer trust in an interconnected world.

With the increase in the complexity and number of cyber threats, security practitioners and defenders are looking for enhanced and robust security practices and solutions for effective cyber defense. Traditional cyber defense practices have been facing several challenges including data silos, data privacy concerns, and limited collaboration between organizations. In addition, the centralized machine learning models have their limitations, in terms of heterogeneity and formatting of datasets, for addressing distributed cyber threats. Federated learning has emerged as a decentralized machine learning approach with its potential for privacy preservation and contextualizing data for collaborative cyber defense. Federated learning has several characteristics, such as model aggregation, federated averaging, and differential privacy, which enable the collaborative training of machine learning models across distributed datasets.