The Role of Artificial Intelligence in Automating Incident Response in Cloud-Based Cybersecurity

Year : 2025 | Volume : 12 | Issue : 01 | Page : 15-24
    By

    Harshvardhan Chunawala,

  • Pratikkumar Chunawala,

  1. Cloud Infrastructure Architect, Amazon Web Services (AWS), 10 Exchange Place, Jersey City, New Jersey, USA
  2. Principal Cloud Architect, Amazon Web Services (AWS), 10 Exchange Place, Jersey City, New Jersey, USA

Abstract

As cloud computing continues to gain traction across industries, the complexity and scale of cloud environments present significant challenges to traditional cybersecurity practices. The dynamic and distributed nature of cloud infrastructures necessitates agile and effective incident response mechanisms to detect, analyze, and mitigate threats in real-time. However, conventional incident response methods often fall short due to the growing sophistication of cyber threats and the vast amounts of data generated in cloud ecosystems. This study examines the transformative role of Artificial Intelligence (AI) in automating incident response within cloud-based cybersecurity systems. By utilizing AI technologies such as machine learning, deep learning, and natural language processing, cloud security systems can detect and respond to potential threats more swiftly and accurately. AI-powered algorithms can analyze large volumes of data, identify patterns, and anticipate potential security threats, enabling proactive threat management. This automation not only speeds up the response process but also mitigates the effects of security breaches by ensuring timely and accurate interventions. The study also explores the challenges of implementing AI in cloud-based response, such as data privacy concerns, the risk of algorithmic bias, and the need for continuous learning and updating of AI models. The study also examines the future prospects of AI-augmented cybersecurity, where combining AI with other cutting-edge technologies like blockchain and edge computing could further enhance cloud security. Ultimately, this research underscores the critical role of AI in revolutionizing cloud cybersecurity by enabling automated, efficient, and adaptive incident response systems, paving the way for more secure and resilient cloud environments.

Keywords: Artificial intelligence, cloud computing, cybersecurity, incident response, automation, machine learning, threat mitigation, cloud security

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Harshvardhan Chunawala, Pratikkumar Chunawala. The Role of Artificial Intelligence in Automating Incident Response in Cloud-Based Cybersecurity. Journal of Operating Systems Development & Trends. 2025; 12(01):15-24.
How to cite this URL:
Harshvardhan Chunawala, Pratikkumar Chunawala. The Role of Artificial Intelligence in Automating Incident Response in Cloud-Based Cybersecurity. Journal of Operating Systems Development & Trends. 2025; 12(01):15-24. Available from: https://journals.stmjournals.com/joosdt/article=2025/view=190555


References

  1. Ferrag MA, Maglaras L, Argyriou A, Kosmanos D, Janicke H. Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes. J Netw Comput Appl. 2018 Jan 1; 101: 55–82.
  2. Rao Narendra, Sr Tadapaneni, Mustafa Shuaieb Sabri. Cloud computing security challenges. Int J Innov Eng Res Technol. 2020; 7(6): 1–6.
  3. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J. Artificial intelligence: A powerful paradigm for scientific research. Innovation. 2021 Nov 28; 2(4): 100179.
  4. Banala S. Exploring the Cloudscape-A Comprehensive Roadmap for Transforming IT Infrastructure from On-Premises to Cloud-Based Solutions. International Journal of Universal Science and Engineering (IJUSE). 2022; 8(1): 35–44.
  5. Gao R, Zhang Z, Shi Z, Xu D, Zhang W, Zhu D. A review of natural language processing for financial technology. In SPIE International Symposium on Artificial Intelligence and Robotics 2021. 2021 Oct 28; 11884: 262–277.
  6. Yuan X, Li C, Li X. DeepDefense: identifying DDoS attack via deep learning. In 2017 IEEE international conference on smart computing (SMARTCOMP). 2017 May 29; 1–8.
  7. Dehghantanha A, Conti M, Dargahi T, editors. Cyber threat intelligence. New York, NY: Springer International Publishing; 2018 Apr 24.
  8. Hamid A, Samidi HR, Finin T, Pappachan P, Yus R. GenAIPABench: A benchmark for generative AI-based privacy assistants. arXiv preprint arXiv:2309.05138. 2023 Sep 10.
  9. Khan MN, Ara J, Yesmin S, Abedin MZ. Machine learning approaches in cybersecurity. InData Intelligence and Cognitive Informatics: Proceedings of ICDICI 2021. Singapore: Springer Nature Singapore; 2022 Feb 1; 345–357.
  10. Zhang Z, Ning H, Shi F, Farha F, Xu Y, Xu J, Zhang F, Choo KK. Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif Intell Rev. 2022 Feb 1: 1–25.
  11. Swarnkar DM, Ambhaikar A. Improved convolutional neural network based sign language recognition. Int J Adv Sci Technol. 2019 Aug; 27(1): 302–17.
  12. St John Lynch N, Loughran R, McHugh M, McCaffrey F. Artificial Intelligence-Enabled Medical Device Standards: A Multidisciplinary Literature Review. In European Conference on Software Process Improvement. Cham: Springer Nature Switzerland; 2024 Sep 4; 112–130.
  13. Liu Y, Wang J, Li J, Niu S, Song H. Machine learning for the detection and identification of Internet of Things devices: A survey. IEEE Internet Things J. 2021 Jul 21; 9(1): 298–320.
  14. Skopik F, Schall D, Dustdar S. Start trusting strangers? bootstrapping and prediction of trust. In International conference on web information systems engineering. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009 Oct 5; 275–289.
  15. Muñoz-González L, Sgandurra D, Barrère M, Lupu EC. Exact inference techniques for the analysis of Bayesian attack graphs. IEEE Trans Dependable Secure Comput. 2017 Mar 23; 16(2): 231–44.
  16. Devarajan HR, Balasubramanian S, Swarnkar SK, Kumar P, Jallepalli VR. Deep Learning for Automated Detection of Lung Cancer from Medical Imaging Data. In 2023 IEEE International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). 2023 Dec 29; 1: 1–5.
  17. Karagiannis S, Fusco C, Agathos L, Mallouli W, Casola V, Ntantogian C, Magkos E. AI-Powered Penetration Testing using Shennina: From Simulation to Validation. In Proceedings of the 19th International Conference on Availability, Reliability and Security. 2024 Jul 30; 1–7.
  18. Albaseer A, Abdallah M. Privacy-preserving honeypot-based detector in smart grid networks: A new design for quality-assurance and fair incentives federated learning framework. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). 2023 Jan 8; 722–727.
  19. Alpaydin E. Introduction to machine learning. MIT press; Massachusetts, United States. 2020 Mar 24.
  20. Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor. 2015 Oct 26; 18(2): 1153–76.
  21. Bécue A, Praça I, Gama J. Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artif Intell Rev. 2021 Jun; 54(5): 3849–86.
  22. Swarnkar SK, Ambhaikar A, Swarnkar VK, Sinha U. Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization and Regularization. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) ICT: Applications and Social Interfaces. Singapore: Springer; 2022; 633–639.
  23. Joloudari JH, Haderbadi M, Mashmool A, GhasemiGol M, Band SS, Mosavi A. Early detection of the advanced persistent threat attack using performance analysis of deep learning. IEEE Access. 2020 Oct 6; 8: 186125–37.
  24. Jadeja Y, Modi K. Cloud computing-concepts, architecture and challenges. In 2012 IEEE international conference on computing, electronics and electrical technologies (ICCEET). 2012 Mar 21; 877–880.
  25. Miao YQ, Khamis AM, Karray F, Kamel MS. A novel approach to path planning for autonomous mobile robots. Control and Intelligent Systems. 2011; 39(4): 235–244.
  26. Dhaygude AD, Varma RA, Yerpude P, Swarnkar SK, Jindal RK, Rabbi F. Deep Learning Approaches for Feature Extraction in Big Data Analytics. In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). 2023 Dec 1; 10: 964–969.
  27. Chhabra GS, Guru A, Rajput BJ, Dewangan L, Swarnkar SK. Multimodal Neuroimaging for Early Alzheimer’s detection: A Deep Learning Approach. In 2023 IEEE 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). 2023 Jul 6; 1–5.
  28. Gaikwad VS, Deore SS, Poddar GM, Patil R, Hirolikar DS, Borawake MP, Swarnkar SK. Unveiling market dynamics through machine learning: Strategic insights and analysis. International Journal of Intelligent Systems and Applications in Engineering (IJISAE). 2024; 12(14s): 388–97.
  29. Swarnkar SK, Dewangan L, Dewangan O, Prajapati TM, Rabbi F. AI-enabled Crop Health Monitoring and Nutrient Management in Smart Agriculture. In 2023 IEEE 6th International Conference on Contemporary Computing and Informatics (IC3I). 2023 Sep 14; 6: 2679–2683.
  30. Titus AJ, Russell AH. The Promise and Peril of Artificial Intelligence–Violet Teaming Offers a Balanced Path Forward. arXiv preprint arXiv:2308.14253. 2023 Aug 28.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 18/10/2024
Accepted 07/12/2024
Published 18/03/2025
Publication Time 151 Days
Total Visits: 136


My IP

PlumX Metrics