Today, cybercrimes continue to spike at an alarming rate with sophisticated techniques designed to cause maximum damage to privacy and security. Under the prevailing circumstances, there have been constant calls to improve security operations capabilities to quickly identify and confront cyberattacks using applicable incident response techniques. The nature of evolving cybersecurity attacks dictates the need to seek innovative solutions to confront the ever-changing threat landscape, which tends to impact the efficient functioning of the security operations center (SOC). Therefore, security orchestration, automation, and response (SOAR) solutions have been found to play a significant role in incident response processes [1-4]. In their study, Kinyua and Awuah defined SOAR as “the end-to-end planning, coordination, cooperation, and integration of the activities of disparate security services, processes, applications, and tools, along with the SOC team, to automate required actions in response to security incidents across enterprise security processes and technologies” [1].
Consequently, the key determinants of how swiftly SOC teams can identify and mitigate cyberattacks amounts to the extent to which innovations and vibrant business processes are adopted and implemented within a highly optimized SOC environment. Specifically, the ability to secure robust business models or implement a combination of cutting-edge technologies such as SOAR and machine learning (ML)/deed learning (DL) algorithms remains a dominant concern for both academic researchers and practitioners in the field. For example, to be efficient in combating evolving threats, some modern SOC leaders now embrace security operations (SecOps) culture of collaboration and coordination with the notion of modernizing incident response (IR) activities as a joint effort between SOC and SecOps teams. It is believed that a preferred solution is one that aims to operationalize and integrate security processes and thereby bridge the gap between the two entities for a resilient security posture within organizations.
Traditionally, security devices and appliances—security information and event management (SIEM), intrusion detection and prevention systems (IDS/IPS), unified threat management (UTM), threat intelligence systems (TIS), Endpoint Detection and Response (EDR), and sandbox solutions—produce huge security logs, which make it tedious and challenging for SOC analysts and other IR teams to manually track real and suspicious cyber threats and attacks. However, SOAR solutions provide critical enhancements to help stamp out cyber threats when disparate tools are integrated within a common architectural platform. These sophisticated solutions employ a platform-based approach for provisioning security orchestration and automation activities within the scope of the threat defense lifecycle [1].
Furthermore, the adoption of artificial intelligence (AI) in SOAR implementations continues to gain significant traction among many experts. ML/DL models have become a vital solution for cybersecurity mitigation efforts, and thus serve to preemptively confront cyber threats through pattern recognition, anomaly detection, predictive analytics, and real-time threat intelligent mapping. Among the several competing algorithms that exist, these models have been actively employed in several of these SOAR solutions by various vendors. A recent study revealed that organizations are increasing the pace of adoption of AI/ML in cybersecurity with high utilization rates of 51%, 34%, and 18% for detection, prediction, and response respectively [2]. Overall, the firms surveyed admitted that focusing their AI cybersecurity initiatives on fraud detection, malware detection, intrusion detection, user behavioral analytics, and other mitigation strategies can gain immense synergy savings. Hence, the desire to use machine intelligence to learn, adapt and potentially act autonomously to streamline IR processes, enhance decision-making, and reinvent business strategies is expected to be at the forefront for cybersecurity professionals and technology vendors for digital initiatives through 2025 [3].
In summary, it is imperative to target sustainable cyber defense operations with proper security control implementations using orchestrated playbooks and automated capabilities for practical incident response solutions. Accordingly, to be efficient in mitigating evolving threats, organizations should endeavor to empower the security operations team with AI-based automated mitigation systems, which tend to offer security orchestration and response processes to fully automate and manage the complexity of the SOC ecosystems. Therefore, the quest for reinventing cybersecurity solutions with applicable machine intelligence and SOAR platforms must be a continuous focus to bolster cybersecurity infrastructure with appreciable visibility, which gravitates toward broader security operations strategies for added benefits.
References
[1] J. Kinyua and L. Awuah, “AI/ML in security orchestration, automation and response: future research directions,” Intelligent Automation & Soft Computing, vol. 28, no.2, pp. 527–545, 2021.
[2] Capegemini Research Institute, “Reinventing Cybersecurity with Artificial Intelligence, The New Frontier in Digital Security.” 2019. [Online]. Available: https://www.capgemini.com/wp-content/uploads/2019/07/AI-in-Cybersecurity_Infographic-2.pdf
[3] Gartner, “Cybersecurity Scenario 2025: Outrageous Intelligence,” 2017. [Online]. Available: https://www.gartner.com