The author also highlights the role of machine learning and artificial intelligence in automating investigations. These technologies can help identify patterns of attack and anomalous behavior, correlate threats with historical data, and automate the gathering of relevant information. However, the implementation of automated investigations comes with challenges, including the integration of automation tools with existing systems and the need for careful planning to avoid human errors.
Key takeaways:
- Security operations centers (SOCs) play a crucial role in managing and mitigating cybersecurity incidents and threats, but often face challenges due to the high volume of alerts, many of which turn out to be false positives.
- Automated investigations can help SOCs manage the threat detection, investigation, and response (TDIR) cycle more efficiently, saving time and resources.
- Implementing automated investigations requires careful planning, including determining which processes can be automated, developing playbooks for various threat scenarios, and ensuring effective integration with existing systems.
- Machine learning and artificial intelligence are foundational to automating investigations, helping to identify indicators of compromise, patterns of attack, and anomalous behavior, and enabling rapid gathering of relevant information and prioritization of responses.