Adaptive Threat Search for AI-Based Security Event Detection

The patent replaces traditional mechanical rule-based detection systems with AI/ML-based automated detection systems. The AI/ML models learn patterns from historical security data and automatically identify threats without requiring manual rule configuration, thereby improving detection accuracy for novel threats while reducing the burden of system maintenance.

The AI/ML system performs self-learning and self-improvement by continuously analyzing security events and updating its detection models. The system automatically adapts to new threat patterns without requiring external intervention or manual rule updates, enabling it to effectively identify evolving threats while maintaining operational simplicity.

The patent applies AI/ML detection selectively to specific security events and data types where pattern recognition provides the most value. Rather than analyzing all possible data with complex models, the system focuses computational resources on high-priority detection tasks, achieving high precision while controlling computational complexity.

The detection system is divided into multiple specialized AI/ML models, each trained for specific threat types or data sources. This segmentation allows the system to achieve high detection precision for various threat categories while managing overall computational complexity by distributing processing loads across multiple simpler, specialized models rather than one large complex model.