The integration of artificial intelligence and machine learning in threat hunting represents a transformative evolution in cybersecurity defense strategies. As traditional signature-based detection methods prove inadequate against sophisticated cyber threats, AI-driven systems offer advanced capabilities in real-time threat detection, analysis, and response. The article delves into the technical foundations of AI-based threat hunting systems, exploring their multi-layered architecture, data processing mechanisms, and advanced detection capabilities. From zero-day attack detection to advanced persistent threats and insider threat monitoring, these systems leverage neural networks, machine learning algorithms, and automated response mechanisms to enhance security operations. The discussion encompasses crucial aspects of data protection, privacy considerations, and future technological developments in the field.
Keywords: artificial intelligence security, privacy-preserving machine learning, security automation, threat detection systems, zero-day attack prevention
