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Improving Cyber Threat Intelligence Through Generative AI From Data to Actionable Intelligence
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Author(s): Vijay Singh Rana (J.V. Jain College, Saharanpur, India), Tarun Kumar Vashishth (Department of Computer Applications, Vidya University, Meerut, India), Puneet Chauhan (School of Computer Application, Swami Vivekanand Subharti University, Meerut, India), Vikas Sharma (Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, India), Sachin Tomar (Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR, India), Shahanawaj Ahamad (College of Computer Science and Engineering, University of Hail, Saudi Arabia)and Aditi Chauhan (School of Humanities and Mass Communication, IIMT University, Meerut, India)
Copyright: 2027
Pages: 30
Source title:
Generative AI for Cyber Risk Management
Source Author(s)/Editor(s): Yassine Maleh (Sultan Moulay Slimane University, Morocco), Lahby Mohamed (Hassan II University, Casablanca, Morocco)and Ahmed A. Abd El-Latif (Prince Sultan University, Saudi Arabia)
DOI: 10.4018/979-8-3693-8397-1.ch002
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Abstract
Cyber threat intelligence (CTI) is of great importance in the proactive nature of the cyber defense process, allowing organizations to make actionable plans for evolving threats. Historically, acquiring cyber threat intelligence has been beset by the lack of comprehensiveness, immediacy, and the ability to synthesize disparate data from undefined sources of threat intelligence. Generative AI affords an opportunity to rise above these deficiencies from the perspective of the depth, speed, and readability of threat intelligence. This chapter assesses how generative AI may be of assistance in driving forward the study of cyber threat intelligence from the millennial perspective, but from an initial perspective – how generative models do indeed have the capability of producing threat hints and warnings, modelling essential threat/attack scenarios and augmenting sources of data for training detection systems.
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