Data-Driven Cybersecurity: AI-Based Predictive Models for Threat Intelligence and Risk Mitigation
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P103Keywords:
AI in cybersecurity, machine learning, deep learning, threat detection, adversarial attacks, data privacy, interpretability, anomaly detection, hybrid AI-human systems, risk managementAbstract
In the rapidly evolving landscape of cybersecurity, traditional methods of threat detection and risk management are increasingly inadequate to address the sophisticated and dynamic nature of cyber threats. The advent of artificial intelligence (AI) and machine learning (ML) has opened new avenues for enhancing cybersecurity through data-driven approaches. This paper explores the application of AI-based predictive models in threat intelligence and risk mitigation. We discuss the theoretical foundations, methodologies, and practical implementations of these models, highlighting their effectiveness in identifying and mitigating cyber threats. The paper also examines the challenges and future directions in this field, providing insights for researchers, practitioners, and policymakers
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