Vol. 2 No. 2 (2022): Advances in Deep Learning Techniques
Articles

Automating Security Incident Mitigation Using AI/ML-Driven SOAR Architectures

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  • Abdul Samad Mohammed,
  • Vincent Kanka,
  • Aarthi Anbalagan
Abdul Samad Mohammed
Abdul Samad Mohammed, Dominos, USA
Vincent Kanka
Vincent Kanka, Homesite, USA
Aarthi Anbalagan
Aarthi Anbalagan, Microsoft Corporation, USA
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Published 08-08-2022

Keywords

  • AI-driven SOAR,
  • ML for cybersecurity,
  • automated incident response

How to Cite

[1]
Abdul Samad Mohammed, Vincent Kanka, and Aarthi Anbalagan, “Automating Security Incident Mitigation Using AI/ML-Driven SOAR Architectures”, Adv. in Deep Learning Techniques, vol. 2, no. 2, pp. 22–65, Aug. 2022.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) within Security Orchestration, Automation, and Response (SOAR) platforms represents a transformative evolution in the cybersecurity domain. This paper explores the automation of security incident mitigation through the application of AI/ML-driven SOAR architectures, emphasizing advanced methodologies for incident prioritization, classification, and response automation. By leveraging sophisticated deep learning models, these platforms enable the dynamic creation of adaptive playbooks and facilitate autonomous threat mitigation processes. Such capabilities significantly enhance the efficiency and scalability of modern security operations centers (SOCs), addressing challenges posed by increasing attack vectors, rising incident volumes, and the shortage of skilled cybersecurity professionals.

The research delves into the integration of AI/ML technologies within SOAR platforms, providing a systematic analysis of their role in enhancing key functionalities such as event correlation, root cause analysis, and decision-making for incident response. Notable SOAR platforms, including Palo Alto Cortex XSOAR and IBM Resilient, serve as focal points for this study. These platforms exemplify the deployment of advanced ML models and natural language processing (NLP) for context-aware threat detection and automated remediation. Furthermore, the adaptability of these systems to evolving threats is highlighted, underscoring their capacity for continuous learning through reinforcement learning mechanisms and real-time data ingestion.

The paper investigates the critical components of AI/ML-enabled SOAR platforms, including data preprocessing pipelines, feature engineering techniques, and model deployment strategies tailored to cybersecurity requirements. Special attention is given to the development of autonomous playbooks, which employ predictive analytics to dynamically recommend or execute response actions based on historical data and threat intelligence feeds. These playbooks not only accelerate response times but also reduce manual intervention, mitigating the risk of human error in critical decision-making processes.

Case studies presented in this research illustrate the practical application of AI/ML-driven SOAR architectures in mitigating advanced persistent threats (APTs), ransomware attacks, and insider threats. For instance, Palo Alto Cortex XSOAR demonstrates the application of ML algorithms in automating incident triage and prioritization, while IBM Resilient showcases the use of NLP to enhance incident context enrichment and playbook execution. These real-world implementations validate the effectiveness of AI/ML in optimizing SOC workflows and achieving measurable improvements in threat response efficiency.

The research also addresses key challenges associated with implementing AI/ML-driven SOAR architectures, including the complexity of model training, data quality issues, and the interpretability of AI-driven decisions. Additionally, ethical considerations, such as ensuring transparency in automated responses and maintaining compliance with data privacy regulations, are critically examined. Potential solutions, such as the adoption of explainable AI (XAI) and robust governance frameworks, are proposed to mitigate these challenges and ensure the ethical deployment of AI/ML within cybersecurity ecosystems.

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