Effectiveness of AI/ML in SOAR (Security Automation and Orchestration) Platforms
Abstract: Security Operations Centres (SOCs) are consistently confronted with an ongoing challenge posed by the evolution of cyber threats. Security Automation and Orchestration (SOAR) platforms have effectively tackled this challenge through the optimization of workflows and the automation of tasks. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into SOAR represents a significant advancement in enhancing security efficacy. Within this context, the current study explores the influence of AI/ML in SOAR on threat identification, efficiency of response, and the overall security stance. Drawing upon data derived from academic research, publications, reports, as well as industry investigations, in conjunction with semi - structured interviews conducted with specific security experts, this research scrutinizes security data to measure enhancements realized through AI/ML in SOAR. Furthermore, the qualitative data offers perspectives into user encounters and outlooks, unveiling a human - centred view on the functionalities of AI/ML. Through an assessment of the efficacy of AI/ML in SOAR, this investigation facilitates the advancement and deployment of forthcoming AI - driven SOAR solutions, enabling organizations to harness AI/ML for bolstering their security stance against the constantly evolving threat landscape.
Keywords: AI - powered SOAR, Security Automation and Orchestration (SOAR), Machine Learning (ML), Threat Detection, Incident Response, Security Alert Prioritization, Automated Response, Security Posture, Threat Landscape
How to Cite?: Srihari Subudhi, "Effectiveness of AI/ML in SOAR (Security Automation and Orchestration) Platforms", Volume 13 Issue 8, August 2024, International Journal of Science and Research (IJSR), Pages: 201-206, https://www.ijsr.net/getabstract.php?paperid=MR24802085215, DOI: https://dx.doi.org/10.21275/MR24802085215