Adaptive generative AI for dynamic cybersecurity threat detection in enterprises

Naveen Vemuri 1, *, Naresh Thaneeru 2 and Venkata Manoj Tatikonda 1

1 Masters in Computer Science, Silicon Valley University, Bentonville, AR, USA.
2 Masters in Computer Applications, Kakatiya University, Bentonville, AR, USA.
 
Review
International Journal of Science and Research Archive, 2024, 11(01), 2259–2265.
Article DOI: 10.30574/ijsra.2024.11.1.0313
Publication history: 
Received on 08 January 2024; revised on 16 February 2024; accepted on 19 February 2024
 
Abstract: 
This research paper provides a thorough examination of the application of Generative Artificial Intelligence (AI) in the context of dynamic cybersecurity threat detection within enterprises. Recognizing the evolving nature of cyber threats, the study focuses on adaptive generative AI models designed to enhance threat detection capabilities. Through an extensive review of existing literature and case studies, the paper explores various Adaptive Generative AI methodologies, including machine learning algorithms, continuous learning mechanisms, and real-time data processing. The analysis encompasses the strengths and limitations of these approaches, shedding light on their efficacy in addressing the complex and dynamic cybersecurity landscape. By offering a comprehensive overview, this research aims to guide the development and implementation of adaptive generative AI solutions for effective threat detection and mitigation in enterprise cybersecurity.
 
Keywords: 
Artificial intelligence; Cyber threat intelligence; Enterprise security; Enterprise risk management; Machine learning in cybersecurity
 
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