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AI Driven Cybersecurity: Emerging Threats and Defensive Strategies
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity
DOI: https://doi.org/10.64388/IREV9I11-1717185
Abstract
Artificial intelligence (AI) is reshaping IT security by both amplifying risks and enabling new Shielding. Malicious actors now exploit AI to automate sophisticated attacks including deep fake based social engineering, adaptive malware, and AI-powered phishing. At the same time defenders are deploying machine learning to detect anomalies, predict intrusions, and orchestrate rapid incident response. This dual-use nature of AI creates a dynamic battlefield where innovation drives both threat evolution and protective strategies. The latest research emphasizes resilience, ethical AI deployment, and hybrid shield. Prototype that combine human expertise with automated intelligence. This paper critically examines the dual role of AI as both a driver of new threats and a cornerstone of modern defenses, emphasizing the need for collaborative strategies that unite technology, policy, and education to safeguard the digital future.
Keywords
Artificial Intelligence, IT Security, AI-Enhanced Threats- Machine Learning- Manipulation Phishing -Adaptive- Malware- Anomaly Detection, Incident Response Ethical AI, Hybrid Defense Models Federated Learning Predictive Threat Modeling AI-Driven Deception.
References
[1] Bhatia,R.,&Verma,A.(2022) Artificial Intelligence in Cybersecurity: A Study of Threat Detection and Prevention.
[2] Sharma,P.,&Gupta,K.(2023) Rising Cybercrime in India: Challenges and Preventive Measures.
[3] Kumar,S.,&Singh,R.(2023) AI-Driven Fraud Detection in Financial Systems: Techniques and Applications.
[4] Reddy,M.&Nair,V.(2022) Emerging Trends in Cybersecurity Threats and Defense Mechanisms.
[5] Choudhary,V.,&Patel,S.(2024) Deepfake Technology and Its Impact on Cybersecurity
[6] Joshi,A.,&Kulkarni,P.(2023) Cybersecurity Strategies for Digital Transformation
[7] Saxena,V.,&Tripathi,S.(2024) Challenges and Limitations of AI in Cybersecurity
[8] Ferrag, M. A., Maglaras, L., & Janicke, H. (2023) Artificial Intelligence for Cybersecurity: Literature Review and Future Research Directions.
[9] Faraji, M. R., Shikder, F., Hasan, M. H., & Islam, M. M. (2024) Examining the Role of Artificial Intelligence in Cyber Security for Financial Transactions.
[10] Jha, S., Kunwar, R., Jain, A., Gupta, S., & Kumar, V. (2023) Applications of AI-Based Models for Online Fraud Detection and Analysis.
How to cite this paper
@article{1717185,
author = {Shreya verma, Ratnesh Kumar Pandey},
title = {AI Driven Cybersecurity: Emerging Threats and Defensive Strategies},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {6-15},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717185.pdf},
abstract = {Artificial intelligence (AI) is reshaping IT security by both amplifying risks and enabling new Shielding. Malicious actors now exploit AI to automate sophisticated attacks including deep fake based social engineering, adaptive malware, and AI-powered phishing. At the same time defenders are deploying machine learning to detect anomalies, predict intrusions, and orchestrate rapid incident response. This dual-use nature of AI creates a dynamic battlefield where innovation drives both threat evolution and protective strategies. The latest research emphasizes resilience, ethical AI deployment, and hybrid shield. Prototype that combine human expertise with automated intelligence. This paper critically examines the dual role of AI as both a driver of new threats and a cornerstone of modern defenses, emphasizing the need for collaborative strategies that unite technology, policy, and education to safeguard the digital future.},
keywords = {Artificial Intelligence, IT Security, AI-Enhanced Threats- Machine Learning- Manipulation Phishing -Adaptive- Malware- Anomaly Detection, Incident Response Ethical AI, Hybrid Defense Models Federated Learning Predictive Threat Modeling AI-Driven Deception.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717185}
}