Which technique uses AI or ML to identify unusual patterns that may indicate malicious activity?

Behavioral analytics focuses on using artificial intelligence (AI) and machine learning (ML) to analyze baseline behaviors and detect deviations from these norms. This technique is particularly effective in identifying unusual patterns that may signify malicious activity, such as insider threats, account takeovers, or advanced persistent threats.

By continuously monitoring user activity and system behaviors, behavioral analytics can adapt to evolving patterns over time, allowing organizations to spot anomalies that may go unnoticed with traditional security measures. This proactive approach enhances the capability to detect potential threats by automatically flagging actions that deviate from established behavioral patterns, leading to quicker incident response and reduced risk.

In contrast, SOAR playbooks are structured responses to specific incidents but don’t inherently involve AI or ML for pattern recognition. Long-tail analysis focuses on identifying rare occurrences in large datasets rather than real-time behavior patterns. Risk mitigation encompasses strategies for reducing risk but does not specifically imply the use of AI or ML techniques to identify unusual activities. Thus, behavioral analytics is the most suitable choice for the stated purpose of detecting malicious activity through pattern recognition.