ENHANCING ADAPTIVE CYBERSECURITY RISK MANAGEMENT THROUGH AI-DRIVEN THREAT DETECTION
Asere Gbenga Femi, Madu Medugu · International Journal Of Trendy Research In Engineering And Technology · 2025
Adaptive cybersecurity risk management is a dynamic approach to addressing evolving security threats, and artificial intelligence (AI) plays a crucial role in enhancing its effectiveness. This paper explores how AI-driven threat detection can strengthen adaptive cybersecurity frameworks by enabling faster, more accurate identification and response to emerging risks. By leveraging machine learning, neural networks, and predictive analytics, AI systems can continuously monitor network environments, detect anomalies, and predict potential vulnerabilities before they are exploited. These AI technologies improve threat detection accuracy, reducing false positives and enabling real-time decision-making. Additionally, AI can automate incident response processes, allowing organizations to adapt to new threats with minimal human intervention. This results in a more proactive, agile cybersecurity posture that can keep pace with the constantly changing threat landscape. The integration of AI not only enhances the efficiency of existing risk management systems but also empowers organizations to make data-driven decisions and allocate resources more effectively. By optimizing threat detection and response capabilities, AI-driven solutions contribute to a more resilient, adaptive cybersecurity infrastructure.