This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management.

Customers all over the world now enjoy remarkable levels of accessibility and convenience thanks to the digital transformation of the banking industry. However, technology has also brought up new difficulties, including cybersecurity. The incapacity of conventional rule-based fraud detection strategies to keep up with the rapid evolution of cyber threats has generated interest in flexible and efficient approaches like unsupervised learning. The potential of unsupervised learning to improve fraud detection in the banking sector is examined in this article. The article addresses the disadvantages of traditional methods, the benefits of unsupervised learning, and how cybersecurity measures may be affected. A thorough framework for putting unsupervised fraud detection strategies into practice, including data preprocessing, feature engineering, isolation forest implementation, thresholding, and assessment, is provided in the methodology section. To further improve anomaly detection frameworks, future efforts propose integrating advanced machine learning techniques, dynamic thresholding, enhanced feature engineering, and continuous model monitoring. In summary, this essay offers useful insights on using modern machine learning algorithms to reduce cybersecurity threats and ensure the security of digital transactions within the banking industry.

In the rapidly evolving landscape of artificial intelligence (AI), the principles of accountability and transparency are pivotal in ensuring ethical and responsible development. This chapter delves into the fundamental concepts and practical applications of accountability and transparency within AI systems. It begins by outlining the importance of these principles in mitigating risks such as bias, privacy infringement, and unintended consequences. The discussion progresses to explore methodologies and frameworks that promote transparency in AI algorithms, decision-making processes, and data usage. Additionally, the chapter examines the role of stakeholders—developers, policymakers, and users—in fostering a culture of accountability throughout the AI lifecycle. Through case studies and real-world examples, this chapter aims to provide a comprehensive guide for practitioners, researchers, and policymakers striving to navigate the ethical complexities of AI development while upholding societal trust and responsibility.

Electroencephalography (EEG) is a widely used non-invasive method for capturing brain activity, offering valuable insights into cognitive and emotional states relevant to mental health. With the growing complexity and volume of EEG data, machine learning (ML) techniques—particularly deep learning—have become integral in extracting meaningful patterns. While much of the current literature focuses on supervised learning methods that rely on labeled data, unsupervised learning offers an alternative approach capable of discovering hidden structures and novel biomarkers without requiring predefined labels. This systematic review aimed to identify and synthesize recent peer-reviewed research that applied unsupervised or self-supervised learning techniques to EEG data in the context of mental health monitoring, diagnosis, or analysis. A comprehensive search was conducted across six major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and Google Scholar, covering literature from January 2018 to March 2025. Following PRISMA guidelines, predefined inclusion and exclusion criteria were applied to screen and assess the relevance and quality of studies. From 512 initial records, 403 unique articles were screened, and 20 underwent full-text review. Ultimately, no studies met all the inclusion criteria. Most were excluded for employing only supervised methods, being review articles, or focusing on non-mental-health applications. The absence of eligible studies highlights a significant gap in current research and emphasizes the need for future empirical work exploring unsupervised techniques in EEG-based mental health applications. Such efforts could pave the way for more scalable, label-free approaches to understanding brain dynamics in psychological conditions.

This Grounded Theory examines the in-depth dynamics of cybersecurity professionals working in financial organizations regarding stress management, risk handling, communication challenges, and perceived benefits from work. This study conducted a grounded theory approach with cybersecurity professionals through in-depth interviews to comprehensively understand these dynamics. The findings identify critical themes about emotional and psychological resilience, effective risk management strategies, overcoming communication challenges, and recognizing organizational and personal benefits from robust cybersecurity practices. The current research highlights that for better well-being and performance of cybersecurity teams, extensive support systems, proactive risk management, and communication are very important and should be embedded within organizations. By utilizing the grounded theory, this study offers a nuanced exploration of the complex and entwined factors that impact daily operations and the overall effectiveness of cybersecurity professionals, offering valuable insights for improving organizational policies and creating a supportive work environment. These findings suggest, most importantly, that robust support systems, risk assessments, clear communication protocols, and training opportunities should be developed and operationalized in financial organizations to help professionals reduce stress at work and strengthen resilience factors. Not only does this research project cover significant gaps in the literature, but it also really helps financial organizations further improve their currently existing cybersecurity strategies to better support personnel by using this framework.