Automated Incident Containment Using SOAR Platforms in Large-Scale Enterprises

Abstract

Large-scale enterprises are experiencing rapid growth in cyber incidents due to expanded attack surfaces, increased cloud integration, and the speed at which modern threats evolve. Traditional Security Operations Centers rely on manual processes that result in slow containment, inconsistent decision making, and high levels of alert fatigue. This study examines the effectiveness of automated incident containment using Security Orchestration, Automation, and Response platforms within large enterprise environments. A qualitative synthesis of twenty peer reviewed sources, NIST and ISO security frameworks, and empirical studies was conducted to evaluate containment speed, analyst workload, incident classification accuracy, and zero trust alignment.
The findings demonstrate a significant improvement in containment performance when SOAR is deployed. Results indicate that automated workflows reduce average containment time from multiple hours to several minutes, lower analyst workload by up to sixty percent, and enhance the overall consistency of zero trust enforcement. Two performance comparison tables and two visual graphs support these findings by illustrating measurable gains in alert triage quality, containment efficiency, and operational accuracy. AI enabled capabilities such as predictive triage, dynamic playbook recommendation, and automated host isolation further strengthen containment processes across distributed enterprise networks.
The study concludes that SOAR driven containment provides a scalable, accurate, and highly efficient response model for large enterprises. However, successful implementation requires strong governance, high quality SIEM data, and strategies to mitigate automation bias. These insights provide valuable guidance for enterprises seeking to modernize their cybersecurity posture and transition toward automated, intelligence driven response operations.