AI-Driven Threat Intelligence Platforms: A Revolution in Cyber security Monitoring and Response

Authors

  • Nalini Independent Researcher, India. Author

DOI:

https://doi.org/10.56472/ICCSAIML25-144

Keywords:

AI-Driven, Threat Intelligence, Machine Learning, Predictive Analytics, Cyber security

Abstract

We acknowledge that the threat continues to change, and therefore, there is a need to use new technologies to support structures in cyber security. Cyber threat intelligence solutions supported by Artificial Intelligence (AI) are the innovative solutions implemented to identify, analyse and prevent cyber threats in advance. The current article offers a detailed review of threats with the help of AI-based solutions, focusing on the issue of monitoring and responding capabilities. Innovative elements of those platforms involve a breakdown of how machine learning algorithms, natural language processing, and predictive analytics can be incorporated into these tools. The discussed issues include data protection, algorithmic fairness or accountability, and practical implementation difficulties. This work supports the effectiveness of using AI by presenting case studies and experimental evaluations of response time, threat detection, and threat modeling. Prospective studies and implementation tactics for raising the usage of threat intelligence based on artificial intelligence algorithms are suggested in the last section of the article

Downloads

Download data is not yet available.

References

[1] Muniraju Hullurappa, Mohanarajesh Kommineni, “Integrating Blue-Green Infrastructure Into Urban Development: A Data-Driven Approach Using AI-Enhanced ETL Systems,” in Integrating Blue-Green Infrastructure Into Urban Development, IGI Global, USA, pp. 373-396, 2025.

[2] Sarker, I. H. (2024). Introduction to AI-Driven Cybersecurity and Threat Intelligence. In AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability (pp. 3-19). Cham: Springer Nature Switzerland.

[3] Maroju, P. K. (2024). Advancing synergy of computing and artificial intelligence with innovations challenges and future prospects. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 1-14.

[4] Kirti Vasdev (2024).” Spatial Data Clustering and Pattern Recognition Using Machine Learning”. International Journal for Multidisciplinary Research (IJFMR).6(1). PP. 1-6. DOI: https://www.ijfmr.com/papers/2024/1/23474

[5] Anumolu, V. R., & Marella, B. C. C. (2025). Maximizing ROI: The Intersection of Productivity, Generative AI, and Social Equity. In Advancing Social Equity Through Accessible Green Innovation (pp. 373-386). IGI Global Scientific Publishing.

[6] Balantrapu, S. S. (2024). AI for Predictive Cyber Threat Intelligence. International Journal of Management Education for Sustainable Development, 7(7), 1-28.

[7] Praveen Kumar Maroju, "Assessing the Impact of AI and Virtual Reality on Strengthening Cybersecurity Resilience Through Data Techniques," Conference: 3rd International conference on Research in Multidisciplinary Studies Volume: 10, 2024.

[8] Kodi, D. (2024). “Performance and Cost Efficiency of Snowflake on AWS Cloud for Big Data Workloads”. International Journal of Innovative Research in Computer and Communication Engineering, 12(6), 8407–8417. https://doi.org/10.15680/IJIRCCE.2023.1206002

[9] Sarker, I. H. (2024). AI-driven cybersecurity and threat intelligence: cyber automation, intelligent decision-making and explainability. Springer Nature.

[10] Attaluri, V., & Aragani, V. M. (2025). “Sustainable Business Models: Role-Based Access Control (RBAC) Enhancing Security and User Management”. In Driving Business Success Through Eco-Friendly Strategies (pp. 341- 356). IGI Global Scientific Publishing.

[11] Zahra, Y., & Sanmorino, A. (2024). Exploring the Evolving Role of AI in Cybersecurity. European Journal of Privacy Law & Technologies.

[12] L. N. Raju Mudunuri, P. K. Maroju and V. M. Aragani, "Leveraging NLP-Driven Sentiment Analysis for Enhancing Decision-Making in Supply Chain Management," 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2025, pp. 1-6, doi: 10.1109/ICAECT63952.2025.10958844.

[13] Sun, N., Ding, M., Jiang, J., Xu, W., Mo, X., Tai, Y., & Zhang, J. (2023). Cyber threat intelligence mining for proactive cybersecurity defense: a survey and new perspectives. IEEE Communications Surveys & Tutorials, 25(3), 1748-1774.

[14] Sudheer Panyaram, Muniraju Hullurappa, “Data-Driven Approaches to Equitable Green Innovation Bridging Sustainability and Inclusivity,” in Advancing Social Equity Through Accessible Green Innovation, IGI Global, USA, pp. 139-152, 2025.

[15] Ashima Bhatnagar Bhatia Padmaja Pulivarthi, (2024). Designing Empathetic Interfaces Enhancing User Experience Through Emotion. Humanizing Technology With Emotional Intelligence. 47-64. IGI Global.

[16] Naga Ramesh Palakurti Vivek Chowdary Attaluri,Muniraju Hullurappa,comRavikumar Batchu,Lakshmi Narasimha Raju Mudunuri,Gopichand Vemulapalli, 2025, “Identity Access Management for Network Devices: Enhancing Security in Modern IT Infrastructure”, 2nd IEEE International Conference on Data Science And Business Systems.

[17] Islam, S. M., Bari, M. S., Sarkar, A., Khan, A. O. R., & Paul, R. (2024). AI-Powered Threat Intelligence: Revolutionizing Cybersecurity with Proactive Risk Management for Critical Sectors. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 1-8.

[18] Silvestri, S., Islam, S., Amelin, D., Weiler, G., Papastergiou, S., & Ciampi, M. (2024). Cyber threat assessment and management for securing healthcare ecosystems using natural language processing. International Journal of Information Security, 23(1), 31-50.

[19] Bentz, D., & Schiller, D. (2015). Threat processing: models and mechanisms. Wiley interdisciplinary reviews: cognitive science, 6(5), 427-439.

[20] Mohanarajesh Kommineni, (2023/9/17), Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware, International Journal of Innovations in Applied Sciences & Engineering, 9. 48-59. IJIASE

[21] V. M. Aragani, "Securing the Future of Banking: Addressing Cybersecurity Threats, Consumer Protection, and Emerging Technologies," International Journal of Innovations in Applied Sciences and Engineering, vol. 8, no.1, pp. 178-196, Nov. 11, 2022.

[22] S. Panyaram, "Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement," International Transactions in Artificial Intelligence, vol. 7, no. 7, pp. 1-15, 2023.

[23] Qamar, S., Anwar, Z., Rahman, M. A., Al-Shaer, E., & Chu, B. T. (2017). Data-driven analytics for cyber-threat intelligence and information sharing. Computers & Security, 67, 35-58.

[24] Vasdev K. “The Role of GIS in Monitoring Upstream, Midstream and Downstream Oil and Gas Activities”. J Artif Intell Mach Learn & Data Sci 2023, 1(3), 1916-1919. DOI: doi.org/10.51219/JAIMLD/kirti-vasdev/424

[25] Singh, U. K., Joshi, C., & Kanellopoulos, D. (2019). A framework for zero-day vulnerabilities detection and prioritisation. Journal of Information Security and Applications, 46, 164-172.

[26] B. C. C. Marella, “Data Synergy: Architecting Solutions for Growth and Innovation,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 11, no. 9, pp. 10551–10560, Sep. 2023.

[27] Mr. G. Rajassekaran Padmaja Pulivarthy,Mr. Mohanarajesh Kommineni,Mr. Venu Madhav Aragani, (2025), Real Time Data Pipeline Engineering for Scalable Insights, IGI Global.

[28] Sahil Bucha, “Integrating Cloud-Based E-Commerce Logistics Platforms While Ensuring Data Privacy: A Technical Review,” Journal Of Critical Reviews, Vol 09, Issue 05 2022, Pages1256-1263.

[29] Vootkuri, C. AI-Powered Cloud Security: A Unified Approach to Threat Modeling and Vulnerability Management.

[30] Divya Kodi, "Zero Trust in Cloud Computing: An AI-Driven Approach to Enhanced Security," SSRG International Journal of Computer Science and Engineering, vol. 12, no. 4, pp. 1-8, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I4P101

[31] Advanced Technique for Analysis of the Impact on Performance Impact on Low-Carbon Energy Systems by Plant Flexibility, Sree Lakshmi Vineetha Bitragunta1, Lakshmi Sneha Bhuma2, Gunnam Kushwanth3, International Journal for Multidisciplinary Research (IJFMR), Volume 2, Issue 6, November-December 2020, PP-1-9.

[32] Sreekandan Nair, S. (2023). Digital Warfare: Cybersecurity Implications of the Russia-Ukraine Conflict. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 31-40. https://doi.org/10.63282/7a3rq622

[33] Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,” Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media”, Journal of Artificial Intelligence and Big Data (jaibd),1(1),11-20,2016.

[34] Agarwal S. “Privacy-Enhancing Technologies in Personalized Recommender Engines”. IJETCSIT [International Journalof EmergingTrendsinComputerScienceandInformationTechnology]. 2024 Jun. 30 [cited 2025 Jun. 4]; 5(2):73-81. Available from: https://ijetcsit.org/index.php/ijetcsit/article/view/161

[35] R. Daruvuri, K. Patibandla, and P. Mannem, "Leveraging unsupervised learning for workload balancing and resource utilization in cloud architectures," International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 10, pp. 1776-1784, 2024.

[36] N. Bibi et al., "Sequence-Based Intelligent Model for Identification of Tumor T Cell Antigens Using Fusion Features," in IEEE Access, vol. 12, pp. 155040-155051, 2024, doi: 10.1109/ACCESS.2024.3481244.

[37] A. Garg, M. Pandey, and A. R. Pathak, “A Multi-Layered AI-IoT Framework for Adaptive Financial Services”, IJETCSIT, vol. 5, no. 3, pp. 47–57, Oct. 2024, doi: 10.63282/3050-9246.IJETCSIT-V5I3P105

[38] Khan, S., Noor, S., Awan, H.H. et al. “Deep-ProBind: binding protein prediction with transformer-based deep learning model”. BMC Bioinformatics 26, 88 (2025). https://doi.org/10.1186/s12859-025-06101-8.

[39] Vootkuri, C. Neural Networks in Cloud Security: Advancing Threat Detection and Automated Response.

[40] Settibathini, V. S., Kothuru, S. K., Vadlamudi, A. K., Thammreddi, L., & Rangineni, S. (2023). Strategic analysis review of data analytics with the help of artificial intelligence. International Journal of Advances in Engineering Research, 26, 1-10.

Published

2025-05-18

How to Cite

1.
Nalini. AI-Driven Threat Intelligence Platforms: A Revolution in Cyber security Monitoring and Response. IJETCSIT [Internet]. 2025 May 18 [cited 2025 Sep. 12];:361-7. Available from: https://ijetcsit.org/index.php/ijetcsit/article/view/275

Similar Articles

1-10 of 235

You may also start an advanced similarity search for this article.