With the rapid development of cloud computing and cybersecurity technologies, the SaaSification of security software has become an important trend for enhancing network defense capabilities. Traditional security components such as cloud firewalls and cloud WAFs face challenges like inflexible resource allocation and poor scalability when handling complex network threats. This paper proposes an intelligent traffic orchestration method based on the SaaSification of security software. It designs a traffic orchestration algorithm with AI self-learning capabilities, achieving elastic scaling and deep traffic inspection based on dynamic traffic. The innovation lies in optimizing traffic scheduling through deep reinforcement learning (DRL), modeling traffic orchestration as a Markov decision process (MDP), and incorporating a self-supervised elastic scaling mechanism to automatically adjust resources with traffic changes. Additionally, the method uses a deep detection model combining CNN and RNN to enhance the accuracy of network attack detection model results show that the proposed method significantly outperforms traditional methods in terms of resource utilization, response time, and throughput. Specifically, CPU utilization increases to 85%, response time reduces to 80 ms, and system throughput rises to 1100 requests per second, verifying the method’s effectiveness in cloud security protection.