Are GPT-Powered AI Systems Superior to Traditional Cybersecurity Tools: Applications and Challenges

Abstract

Generative Pre-trained Transformer (GPT) models are revolutionizing cybersecurity by enhancing threat detection, risk evaluation, phishing defense, and automatic vulnerability analysis. This study delves into the various applications of GPT Technologies in security operations, emphasizing their competence in processing security information of large volume, anomaly detections, and providing real-time insights. Case studies cite quantifiable benefits: Anomaly detection by AI reached a high of 80% accuracy, malware and phishing classification 75–95% accuracy, and Microsoft Copilot reduced phishing attacks by 45% in commercial settings. VirusTotal and Cylance AI improved malware categorization accuracy by 38%, reducing false positives by 35%. Incident response effectiveness was improved by as high as 40% in reported deployments. However, GPT models are also exposed to adversarial exploitation, gaps in explanation, integration issues, and dependence on previous data. This paper lists countermeasures, such as prompt engineering, fine-tuning, domain-specific training, and hybrid AI-human decision systems. Findings further highlight the significance of continuous updates, interdisciplinary collaboration with adherence to ethical frameworks to reap the full benefits of GPT-powered cybersecurity. So, take into consideration integrating these models into present security ecosystems. This way, organizations may strengthen their defenses, improve risk management, and make resilience against cyber threats.

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