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Securing the Imagination: Security and Privacy Challenges in Generative AI Systems
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Author(s): Nikhil Kumar Goyal (Noida International University, Greater Noida, India), Kapil Gulati (Poornima University, Jaipur, India), Ashish Avasthi (Poornima University, Jaipur, India), Sumit Kumar Kapoor (Poornima University, Jaipur, India), Bright Keswani (Poornima University, Jaipur, India)and Sangita Gupta (Poornima University, Jaipur, India)
Copyright: 2026
Pages: 28
Source title:
Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics
Source Author(s)/Editor(s): Bahaa Eddine Elbaghazaoui (Sultan Moulay Slimane University, Morocco), Mohamed Amnai (Ibn Tofail University, Morocco)and Noreddine Gherabi (Sultan Moulay Slimane University, Morocco)
DOI: 10.4018/979-8-3373-5616-7.ch010
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Abstract
Generative Artificial Intelligence (AI) has rapidly become a mainstream technology capable of autonomously producing realistic and diverse media content, including text, images, video, music, and code. As models like GPT, DALL·E, and Stable Diffusion transform industries such as entertainment, marketing, education, and software development, they also introduce a complex landscape of security and privacy risks. This chapter, per the authors, explores critical vulnerabilities such as prompt injection, adversarial attacks, data poisoning, and model inversion, which can lead to unethical outputs and exposure of sensitive training data. It further examines threats arising from large-scale data scraping, copyright violations, and lack of user consent, while also addressing ethical issues tied to misinformation, deepfakes, and surveillance. The chapter reviews technical safeguards including differential privacy, federated learning, watermarking, adversarial training, and secure deployment methods like sandboxing and real-time monitoring.
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