Anomaly detection is a cybersecurity technique that identifies patterns, events, or data points that significantly deviate from established normal behavior within systems, networks, or datasets. By continuously monitoring activity and establishing baselines of typical operations—such as network traffic volumes, user login patterns, and data access sequences—anomaly detection systems flag any activity falling outside these norms as potentially suspicious. This approach is essential for uncovering sophisticated threats like zero-day exploits, insider threats, and advanced persistent threats (APTs) that often evade traditional signature-based security measures.

Unlike rule-based detection methods that rely on known threat signatures, anomaly detection excels at identifying previously unknown attack vectors and emerging threats. When an anomaly is detected, security teams can proactively investigate potential vulnerabilities and take swift action to mitigate risks. This capability makes anomaly detection a cornerstone of modern threat intelligence strategies, significantly enhancing an organization's ability to identify indicators of compromise and strengthen its overall defensive posture against evolving cyber threats.