International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | The Risks and Opportunities of Generative AI in Cyber Security: Investigating the Dual-Use Nature of Generative Models |
|---|---|
| ABSTRACT | Generative artificial intelligence (GenAI) represents a paradigm shift in cybersecurity, offering both transformative opportunities and profound risks due to its dual-use nature. This study investigates how GenAI models, such as large language models (LLMs) and generative adversarial networks (GANs), facilitate phishing attacks and malware development while simultaneously enabling defensive strategies through synthetic data generation and automated threat detection. Employing a mixed-methods approach, including a comprehensive literature review, simulation-based analysis of hypothetical datasets, and quantitative performance evaluations, the research reveals that GenAI amplifies phishing efficacy by 58.2% (Zscaler, 2024) and lowers malware creation barriers, yet enhances detection accuracy by up to 15% via synthetic training data. Key findings highlight the need for balanced regulatory frameworks to mitigate risks without stifling innovation. Conclusions underscore GenAI's potential to fortify cyber defenses if ethical guidelines and robust oversight are prioritized, contributing to theoretical advancements in dual-use technology governance and practical recommendations for cybersecurity practitioners. |
| AUTHOR | ROHIT AHUJA Vice President - Software Engineering, J.P. Morgan Chase, 575 Washington Blvd, Jersey City, U.S. |
| VOLUME | 166 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1302059 |
| pdf/59_The Risks and Opportunities of Generative AI in Cyber Security.pdf | |
| KEYWORDS | |
| References | [1] Varun Kumar Tambi, Nishan Singh (2023). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence that is Generative. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 12(10). [2] Alghamdi, N. S., Alqahtani, H. J., & Alghamdi, A. (2024). Phishing attacks in the age of generative artificial intelligence: A systematic review of human factors. Future Internet, 16(8), 174. https://doi.org/10.3390/fi16080174 [3] Pankit Arora & Sachin Bhardwaj (2022). Integrating Wireless Sensor Networks and the Internet of Things: A Hierarchical and Security-based Analysis. International Journal Of Multidisciplinary Research In Science, Engineering and Technology (IJMRSET), 5(5). [4] Ammara, D. A. (2024). Synthetic network traffic data generation: A comparative study. arXiv. https://doi.org/10.48550/arXiv.2410.16326 [5] Sidharth Sharma (2022). Enhancing Generative AI Models for Secure and Private Data Synthesis. [6] Pankit Arora & Sachin Bhardwaj (2021). Methods for Threat and Risk Assessment and Mitigation to Improve Security in the Automotive Sector. International Journal of Advanced Research in Education and TechnologY(IJARETY), 8(2). [7] Varun Kumar Tambi (2022). REAL-TIME COMPLIANCE MONITORING IN BANKING OPERATIONS USING AI. INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), 9(9), 35-47. [8] Cybersecurity Ventures. (2024). Cybercrime magazine. https://cybersecurityventures.com/ [9] Sidharth Sharma (2021). Multi-Cloud Environments: Reducing Security Risks in Distributed Architectures. Journal of Artificial Intelligence and Cyber Security (Jaics) 5 (1):1-6. [10] IBM. (2023). Cost of a data breach report 2023. https://www.ibm.com/reports/data-breach [11] McKinsey & Company. (2024). The cybersecurity provider's next opportunity: Making AI safer. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-cybersecurity-providers-next-opportunity-making-ai-safer [12] Varun Kumar Tambi, Nishan Singh (2022). Creating J2EE Application Development Using a Pattern-based Environment. International Journal of Innovative Research in Computer and Communication Engineering, 10(11). [13] Monetary Authority of Singapore. (2024). Cyber risks associated with generative artificial intelligence. https://www.mas.gov.sg/-/media/mas-media-library/regulation/circulars/trpd/cyber-risks-associated-with-generative-artificial-intelligence.pdf [14] National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1 [15] Popescul, D., & Radu, L. D. (2024). AI in phishing detection: A bibliometric review. Frontiers in Artificial Intelligence, 7, Article 1258902. https://doi.org/10.3389/frai.2024.1258902 [16] Varun Kumar Tambi (2021). NATURAL LANGUAGE UNDERSTANDING MODELS FOR PERSONALIZED FINANCIAL SERVICES. International Journal of Current Engineering and Scientific Research, 8(1):1-11. [17] Tech Advisors. (2024). AI cyber attack statistics. https://tech-adv.com/blog/ai-cyber-attack-statistics/ [18] Sidharth Sharma (2019). Quantum-Enhanced Encryption Methods for Securing Cloud Data. Journal of Theoretical and Computationsl Advances in Scientific Research (Jtcasr) 3 (1):1. [19] Samita Devi, Manish Kumar, Sachin Bhardwaj, PN Hrisheekesha (2021). Dynamic Trust based IDS to Mitigate Gray Hole Attacks in Mobile Adhoc Networks. 2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST), pp.137-142, IEEE Xplore. [20] Varun Kumar Tambi, Nishan Singh (2022). A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 11(5). [21] Sidharth Sharma (2019). Data loss prevention (dlp) strategies in cloud-hosted applications. Journal of Theoretical and Computationsl Advances in Scientific Research (Jtcasr) 3 (1):1-8. [22] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27. [23] Khare, S., De, T., & Gupta, S. (2023). Generative AI (GAI) use for cybersecurity resilience: A scoping review. Journal of International Academy for Case Studies, 29(2), 1-15. [24] Varun Kumar Tambi, Nishan Singh (2021). New Applications of Machine Learning and Artificial Intelligence in Cybersecurity Vulnerability Management. International Journal of Advanced Research in Education and TechnologY(IJARETY), 8(2). [25] Tivon, I. (2023). Impact of AI and generative AI in transforming cybersecurity. Journal of Student Research, 12(4), 1-8. [26] Pankit Arora & Sachin Bhardwaj (2021). Using Knowledge Discovery and Data Mining Techniques in Cloud Computing to Advance Security. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), 10(10). |