Generative Artificial Intelligence and Cybersecurity: A Systematic Review of Opportunities, Threats, and Challenges of Generative Models in Digital Security (2021–2026)
DOI:
https://doi.org/10.70577/asce.v5i4.1119Keywords:
Automation, Technological development, Artificial intelligence, Security, Digital transformationAbstract
Digital transformation has driven the adoption of Generative Artificial Intelligence (GenAI), across multiple sectors. In the realm of cybersecurity, these technologies facilitate threat detection, and the optimization of incident responses; however, this same technological advancement has also fostered, the emergence of new attack vectors, such as personalized phishing, and the automated generation of malware. The objective of this study was to, analyze scientific evidence published between 2021 and 2026, regarding the application of GenAI in cybersecurity, focusing on the opportunities, threats, challenges, and emerging trends associated with its implementation. A systematic review, based on the PRISMA methodology was conducted, selecting peer-reviewed articles published during that period that were directly related to the application of GenAI in cybersecurity. The results show that large-scale language models dominate the field due to their utility in threat intelligence, vulnerability detection, malware analysis, and the automation of security operations; risks associated with advanced phishing, deepfakes, automated social engineering, and malicious code generation are also reported. Generative AI, has established itself as a technology with high potential, to strengthen cybersecurity defense capabilities; however, its dual-use nature introduces new risks that increase the complexity of the digital landscape. The evidence reviewed suggests that, the future development of cybersecurity will depend on both technological advancements, and the establishment of ethical, regulatory, and governance frameworks capable of ensuring the safe, transparent, and responsible use of these tools.
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