In the complex field of incident response, effective training for Security Operations Center (SOC) operators is critical. One of the key challenges in SOC training is providing realistic, data-driven environments that accurately simulate the threats and incidents operators will face. Additionally, detection engineers need reliable and actionable data to create robust detection rules that align with real-world security monitoring systems. However, gathering and analyzing real-world malware samples, which is essential to this process, can be time-consuming and prone to errors when done manually.
Enhancing Detection Engineering with Automated Malware Sandboxing
· 5 min read