AI in enhancing cybersecurity incident response

Bagane, Pooja; Kothari, Sonali; Goyal, Ankur; Patel, Prisha; Chauhan, Sakshi; Gupta, Shaurya; Gupta, Tawishi

doi:10.47974/JDMSC-2528

Open Access Research Article

AI in enhancing cybersecurity incident response

^*Pooja BaganeCorresponding authorpooja.bagane@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) LavalePune, Maharashtra, 412115, IndiaView full profile → , Sonali Kotharisonali.kothari@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) LavalePune, Maharashtra, 412115, IndiaView full profile → , Ankur Goyalankur_gg5781@yahoo.co.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) LavalePune, Maharashtra, 412115, IndiaView full profile → , Prisha Patelprisha.patel.btech2021@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune, Maharashtra, 412115, IndiaView full profile → , Sakshi Chauhansakshi.chauhan.btech2021@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune, Maharashtra, 412115, IndiaView full profile → , Shaurya Guptashaurya.gupta.btech2021@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) LavalePune, Maharashtra, 412115, IndiaView full profile → , Tawishi Guptatawishi.gupta.btech2021@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune, Maharashtra, 412115, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 817–826Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2528 Crossmark XML

Received:: 08 Apr 2025
Published Online:: 12 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2528
Pages:: 817–826

Abstract

With new threats emerging all over the place, the capability to assess and address an advanced threat has become paramount to the health and longevity of organizations. AI provides a potent capacity in augmenting the procedures concerned with cybersecurity incidents response. In this paper, the writer looks at how AI and its applications such as Machine learning algorithms and Automation is being adopted in Cybersecurity to improve on the detection of threats, analysis, and mitigations. AI can quickly analyze attack patterns, assess the likelihood of risks, and eradicate threats mathematically and without delay compared to human protection. Moreover, this paper outlines the concerns and risks when implementing an AI solution for cybersecurity, as well as the right ways to ensure that the AI system is secure and reliable. The evidence presented in the paper supports the AI’s application in defining the cybersecurity of the future and establishing its relevance as an essential tool against cyber threats.

Keywords

Artificial intelligence Cybersecurity Incident response Automation Threat detection Machine learning

Subject Classifications

Primary 68T01Secondary 94A60

References

[1] I. Jada and T. O. Mayayise, “The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review,” Data and Information Management, vol. 8, no. 2, pp. 100063 (Jun. 2024).
[2] S. Lysenko, N. Bobro, K. Korsunova, O. Vasylchyshyn, and Y. Tatarchenko, “The role of artificial intelligence in cybersecurity: Automation of protection and detection of threats,” Economic Affairs, vol. 69, no. 1, pp. 45–52 (Feb. 2024).
[3] S. Chahal, “AI-enhanced cyber incident response and recovery,” International Journal of Science and Research (IJSR), vol. 12, no. 3, pp. 1795–1801 (Mar. 2023).
[4] K. Fysarakis, A. Lekidis, V. Mavroeidis, K. Lampropoulos, G. Lyberopoulos, I. Garcia-Milà Vidal, J. C. Terés i Casals, E. Rodriguez Luna, A. A. Moreno Sancho, A. Mavrelos, M. Tsantekidis, S. Pape, A. Chatzopoulou, C. Nanou, G. Drivas, V. Photiou, G. Spanoudakis, and O. Koufopavlou, “PHOENI2X: A European cyber resilience framework with artificial-intelligence-assisted orchestration, automation and response capabilities for business continuity and recovery, incident response, and information exchange,” in 2023 IEEE Int. Conf. Cyber Security and Resilience (CSR) (Jul. 2023).
[5] M. F. Rafy, “Artificial intelligence in cyber security,” SSRN Electron. J. (Jan. 2024).
[6] M. A. M. Farzaan, M. C. Ghanem, A. El-Hajjar, and D. N. Ratnayake, “AI-enabled system for efficient and effective cyber incident detection and response in cloud environments,” arXiv preprint arXiv:2404.05602 (Apr. 2024).
[7] K. Chandran, S. Panda, and B. Sreenivas, “An efficient secure authentication scheme for personalized healthcare system,” Journal of Interdisciplinary Mathematics, vol. 23, no. 1, pp. 215–227 (2020).
[8] M. Binhammad, S. Alqaydi, A. Othman, and L. H. Abuljadayel, “The role of AI in cyber security: Safeguarding digital identity,” Journal of Information Security, vol. 15, no. 2, pp. 245–278 (2024), doi: 10.4236/jis.2024.152015.
[9] D. Goyal, F. Sheth, P. Mathur, and A. K. Gupta, “Discrete mathematical models for enhancing cybersecurity: A mathematical and statistical analysis of machine learning approaches in phishing attack detection,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-B, pp. 569–599 (2024).
[10] K. M. Manjunatha and M. Kempanna, “Count vectorizer model based web application vulnerability detection using artificial intelligence approach,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 7, pp. 2039–2048 (2022).
[11] R. Kaushik, V. Singh, and R. Kumari, “Multi-class SVM based network intrusion detection with attribute selection using infinite feature selection technique,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 8, pp. 2137–2153 (2021).

Views: 58Downloads: 10Citations: 0