AI-Driven Risk Management in Telecommunication Systems: Evaluating Cyber Defense, Vulnerability Prediction, and Response Strategies

Author(s)	Prashant Roy
Country	India
Abstract	Telecommunication systems are increasingly exposed to complex and evolving cyber threats due to rapid digital transformation, requiring more intelligent and proactive security mechanisms. This study proposes an AI-driven risk management framework for telecom networks to enhance cybersecurity through automated detection, prediction, and response. The framework is grounded in risk management theory, cybersecurity frameworks, artificial intelligence, and predictive analytics. It integrates machine learning, deep learning, and reinforcement learning techniques to detect anomalies, classify cyber threats, and enable adaptive defense strategies. Predictive analytics supports early vulnerability forecasting, while automated incident response ensures fast mitigation and recovery. Evaluation results show improved threat detection accuracy, reduced response time, and enhanced network resilience compared to traditional approaches, demonstrating the effectiveness of AI in strengthening telecom cybersecurity systems.
Keywords	Artificial Intelligence, Cybersecurity Risk Management, Telecommunication Networks, Machine Learning and Threat Detection.
Field	Computer > Network / Security
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-04
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.80516

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