Malicious insiders pose the biggest cybersecurity threat for companies today because they can cause the most damage, and are much harder to detect than outsiders. According to the 2018 Verizon Data Breach Investigations Report, 28 percent of all data breaches involved insiders and that insiders accounted for 76 percent of all compromised records.
Detecting insider threats using conventional security monitoring techniques is difficult, if not impossible. Data science, however, provides a promising alternative. The emerging field of security analytics uses machine learning technologies to establish baseline patterns of human behavior, and then applies algorithms and statistical analysis to detect meaningful anomalies from those patterns that may indicate illicit behavior.
Read more about how science can fight insider threats on Help Net Security.