AI-driven threat detection: Enhancing cybersecurity automation for scalable security operations

Joshua, Emmanuel and Mylavarapu, Pavan (2025) AI-driven threat detection: Enhancing cybersecurity automation for scalable security operations. International Journal of Science and Research Archive, 14 (3). pp. 681-704. ISSN 2582-8185

As the digital landscape becomes increasingly interconnected, organizations face a surge in sophisticated cyber threats that traditional security measures struggle to mitigate. The emergence of artificial intelligence (AI) in cybersecurity has revolutionized threat detection and response, enabling organizations to analyze vast datasets, identify anomalies, and automate security operations. AI-driven threat detection systems, leveraging machine learning and predictive analytics, enhance detection accuracy, reduce false positives, and improve incident response times. However, challenges such as data bias, adversarial AI manipulation, integration complexities, and ethical considerations must be addressed to ensure the effective deployment of AI-driven solutions. This paper explores the evolution of cyber threats, the fundamentals of AI in cybersecurity, and the benefits and challenges of AI-driven security measures. Additionally, we analyze successful implementations in large enterprises, lessons from AI failures, and future trends in AI-driven cybersecurity. The findings underscore the importance of balancing automation with human oversight to build scalable, resilient security frameworks that can adapt to the ever-evolving cyber threat landscape.