Navigating the dual-edged sword of generative AI in cybersecurity
DOI:
https://doi.org/10.34117/bjdv11n1-062Keywords:
generative AI, cybersecurity, large language models, ethical considerations, cyber threatsAbstract
Generative Artificial Intelligence (genai) is significantly impacting the field of cybersecurity by presenting both opportunities and challenges. On one hand, technologies like large language models (llms), exemplified by openai's chatgpt and Google deepmind's Gemini, enhance operational security through their ability to analyze vast amounts of data, identify malicious activities, and automate threat responses. Genai can generate real-time alerts, assist in vulnerability testing, and facilitate training in cybersecurity through simulations and tutorials. However, its capabilities also pose serious risks, as malicious actors can leverage these same tools to execute sophisticated attacks, such as highly personalized phishing schemes and the automation of malware creation. Research studies highlight the implications of genai's evolution in cybersecurity. Gupta et al. (2023) and Neupane et al. (2023) explore how the misuse of genai can lead to new cyber threats, including deepfakes and misinformation, necessitating a proactive response. Strategies like the Cyber Kill Chain (CKC) model can help understand attack cycles and strengthen defenses against these threats. Furthermore, Barrett et al. (2023) discuss the dual-use dilemma of genai, stressing the need for ethical considerations in its development and use. As genai technologies continue to advance, the need for comprehensive regulatory frameworks and integrated security measures becomes increasingly critical. Collaboration among stakeholders—including tech companies, policymakers, and researchers—is essential for maximizing the benefits of genai while minimizing its exploitation. The ongoing discourse surrounding genai’s impact on cybersecurity is vital for addressing emerging challenges and preparing for a future where technology enhances security without compromising ethical standards.
References
Barrett, C., Boyd, B., Burzstein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A., Christodorescu, M., Datta, A., Feizi, S., Fisher, K., Hashimoto, T., Hendrycks, D., Jha, S., Kang, D., Kerschbaum, F., Mitchell, E., Mitchell, J., Ramzan, Z., Shams, K., Song, D., Taly, A., & Yang, D. (2023). Identifying and Mitigating the Security Risks of Generative AI. ArXiv, abs/2308.14840. https://doi.org/10.1561/3300000041.
Ferrara, E. (2023). GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models. ArXiv, abs/2310.00737. https://doi.org/10.48550/arXiv.2310.00737.
Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy. IEEE Access, 11, 80218-80245. https://doi.org/10.1109/ACCESS.2023.3300381.
Neupane, S., Fernandez, I., Mittal, S., & Rahimi, S. (2023). Impacts and Risk of Generative AI Technology on Cyber Defense. ArXiv, abs/2306.13033. https://doi.org/10.48550/arXiv.2306.13033.
Platt, M., & Platt, D. (2023). Effectiveness of Generative Artificial Intelligence for Scientific Content Analysis. 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT), 1-4. https://doi.org/10.1109/AICT59525.2023.10313167.
Spector-Bagdady, K. (2023). Generative-AI-Generated Challenges for Health Data Research. The American Journal of Bioethics, 23, 1 - 5. https://doi.org/10.1080/15265161.2023.2252311.
Da Silva, F. A. Evolving approaches in cybersecurity: metrics and human factors. International Seven Journal of Multidisciplinary, [S. l.], v. 1, n. 2, 2024. DOI: 10.56238/isevmjv1n2-010. Disponível em: https://sevenpublicacoes.com.br/ISJM/article/view/6161. Acesso em: 7 jan. 2025.
