Application and Development Trend of AI Big Models in Communication Security and Risk Management

The rapidly evolving network landscape, characterized by surging traffic, complex structures, and increasingly sophisticated attacks, renders traditional rule-based security mechanisms inadequate, especially for critical infrastructure and large-scale deployments. We propose an intelligent communication security framework integrating multimodal perception (traffic, logs, device behavior), Transformer-based semantic understanding (BERT fine-tuned for security), dynamic threat knowledge graphs, and AI-driven adaptive response (reinforcement learning). Extensive experiments demonstrate the framework's superiority: Attack detection achieves a 94.7% detection rate and 0.95 AUC on CICIDS2017, significantly outperforming LSTM (0.91 AUC) and Random Forest (0.89 AUC). Response latency averages 149.6 ms, faster than alternatives. Attack path tracing accuracy reaches 92.7% across diverse scenarios.The framework significantly enhances communication security through high-accuracy detection, rapid adaptive response, and precise threat tracing. Future work will focus on lightweight deployment and enhanced privacy for edge environments.