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.

Using expert systems and relevant machine learning methods, automating network intrusion detection has become commonplace. However, the interconnectedness of many industrial control systems and the Internet of Things (IoT) has made cyber attacks on critical infrastructure communication networks a significant concern. These Critical Cyber-Physical Systems (CPSs) experience massive network traffic, posing a challenge for conventional machine-learning techniques to identify anomalies. This paper introduces a novel approach that overcomes these limitations, leveraging the power of deep learning to detect and classify anomalies with remarkable accuracy. By utilizing deep models like Deep Neural Network (DNN) and Deep Short-Term Memory (LSTM) in a two-stage procedure, we significantly enhance the capabilities of our proposed methodology. We also employ a Deep Sparse Autoencoder (DSAE) to resolve the feature engineering problem and prepare data for processing. The effectiveness of this strategy is evaluated using datasets collected from the IoT ecosystem, specifically IoT-23 and LITNET-2020. The evaluation results for the proposed method are presented and discussed, comparing its statistical significance with the most cutting-edge techniques for detecting network anomalies.

In the rapidly advancing landscape of artificial intelligence (AI), the intersection of privacy and security has emerged as a critical focal point. This chapter explores the multifaceted challenges and considerations involved in safeguarding personal data within the AI era. It delves into the ethical implications of AI-driven data collection, storage, and utilization, emphasizing the importance of privacy-preserving technologies and robust security measures. Through case studies and theoretical frameworks, the chapter examines current practices and future directions aimed at balancing innovation with the protection of individual privacy rights. By addressing these issues, it aims to equip stakeholders—from developers to policymakers—with the knowledge needed to navigate the complex terrain of AI ethics and ensure responsible data stewardship in the digital age.

The rapid advancement of artificial intelligence (AI) is reshaping various aspects of society, from healthcare and education to employment and entertainment. This chapter delves into the profound societal impacts of AI technologies and the crucial role of governance in steering their development and deployment. It explores the multifaceted effects of AI on economic structures, social interactions, and individual well-being, highlighting both the potential benefits and the inherent risks. Through a comprehensive analysis of current regulatory frameworks and governance models, the chapter identifies key ethical challenges and proposes strategies for ensuring that AI advancements align with societal values and human rights. Emphasis is placed on the necessity of inclusive policymaking, where diverse stakeholder voices are heard, and on the development of international standards that promote transparency, accountability, and fairness.