Abstract: This qualitative exploratory research combines data from six professionals working in the fields of cybersecurity, education, and medicine with in-depth analysis of selected white papers, reports, and case studies. The findings reveal huge detection challenges as regards the sophistication of adversarial inputs and limitations to traditional detection mechanisms. Some of the mitigation strategies discussed in the paper include adversarial training, hybrid models for detection, and the integration of watermarking technologies. Further, this study has shed light on the need for deep learning-especially of CNNs and transformers-in automating feature extraction that could improve resilience in deep learning models against adversarial types of threats.

The resolution of the challenges presented here will provide the ability to contribute toward developing scalable, transparent, and adaptive frameworks capable of ensuring cybersecurity resilience of generative AI systems throughout their lifecycle against evolving adversarial threats. In this paper, consideration is taken of some of the adversarial attacks against generative AI systems and some strategies that in efforts towards strengthening cybersecurity are made for mitigation. Qualitative exploratory research was done, combining data from six professionals working in the fields of cybersecurity, education, and medicine, coupled with in-depth analysis of selected white papers, reports, and case studies. Results pointed to big detection challenges about the sophistication of adversarial inputs and limitations to traditional detection mechanisms. Adversarial training, detection by hybrid models, and integrating watermarking technologies are some of the mitigation strategies discussed in the paper. Further, this study identified the need for deep learning, especially of CNN and transformers, in automating feature extraction, which could give better resilience for deep learning models against adversarial kinds of threats.

Anchoring on game theory, adversarial training, and explainable AI, this covers a very strong optimization approach with a view to model transparency and interpretability of the decisions of detection. Given the modular system design and distributed computing, this work enables scalability and efficiency in Anomaly Detection, Representation Learning, and Robust Optimization methods. In view of the challenges presented, these contributions become possible for the development of scalable, transparent, and adaptive frameworks that can ensure cybersecurity resilience in generative AI systems against dynamically evolving adversarial threats throughout their whole life cycle.

Key words: Cybersecurity, Deep Fakes, Machine Learning, Artificial Intelligence, Economic Impact

| DOI: 10.17148/IARJSET.2025.12204