SCALABLE ADAPTIVE SOAR FRAMEWORK FOR THREAT INTELLIGENCE–BASED CYBER THREAT DETECTION AND AUTOMATED INCIDENT RESPONSE

Cyber threats are evolving and growing rapidly and require intelligent automation in the contemporary security operations. In this paper, a proposal will be made on adaptive Security Orchestration, Automation, and Response (SOAR) framework to improve information-driven detection and incident response by cyber threat intelligence (CTI). The structure incorporates the heterogeneous security tools and multisource threat information to be automated to identify, prioritize, and mitigate the threats through advanced orchestration. The machine learning techniques are applied to continuously consume, correlate and process real-time threat intelligence in order to detect potential attacks and execute predefined playbooks to respond to them automatically. The proposed architecture ensures that the detection accuracy and efficiency are improved significantly by minimizing staff involvement and enhancing the latency of the responses. The findings of the evaluation show that the adaptive SOAR framework improves the situational awareness, increases the effectiveness of incident response, and provides a scalable support of the emerging cyber defense processes.