Article Details

Artificial Intelligence (AI) has become an important technology for enhancing cybersecurity threat detection and response as cyber threats continue to increase in sophistication and frequency. This study evaluated the role of AI in improving cybersecurity threat detection and response in Jigawa State, Nigeria. Specifically, the study examined AI's contribution to cybersecurity operations and evaluated the effectiveness of Random Forest, Long Short-Term Memory (LSTM), and Autoencoder models for detecting cyber threats. A descriptive survey research design was adopted. Data were collected from 100 respondents comprising cybersecurity professionals, information technology personnel, software developers, academic staff, and students with knowledge of Artificial Intelligence and cybersecurity using a structured questionnaire with a reliability index of 0.84. Two research questions were asked. The data was collected and analysed using means and standard deviations. The findings revealed that respondents agreed that AI significantly improves threat-detection accuracy, enables real-time monitoring, reduces incident-response time, detects previously unknown threats, and supports automated incident response. The findings also showed that all three AI models were effective in detecting cyber threats, with LSTM having the highest mean score, followed by Random Forest and Autoencoder. The study concludes that AI significantly enhances cybersecurity threat detection and response when integrated with conventional security mechanisms and supported by skilled cybersecurity professionals. The study recommends, among others, that increased adoption of AI-based cybersecurity solutions, investment in advanced AI models, continuous professional training, and the development of ethical guidelines