Data Security

Anomaly Detection Advances Help Companies, Not Just Detect, But Prevent Data Breaches

rectangle Written by: Purandar Das rectangle 2 5 min read

Anomaly detection is an important technique for identifying fraudulent or suspicious activity, network intrusion, and other abnormal events that may have great significance but are difficult to detect.

In this post, we give a quick overview of two key advances in anomaly detection. For a deeper dive, we invite you to download the Anomaly Detection Brief.

Anomaly Detection Hasn’t Lived Up To The Hype

Broad adoption of anomaly detection solutions has been hampered by a range of challenges, such as:

  • Insufficient productization of the technology
  • No clear definition of use cases and actionable insights
  • Lack of knowledgeable resources in house
  • Difficult to build internally

However, there are two technology advances that change the game for anomaly detection.

  1. Anomaly Detection at the Data Level

Traditional anomaly detection solutions are deployed at the firewall or network level, rather than at the data access level. This prevents them from detecting data requests that are benign at the access level but still malicious at the data level.

The good news is that today we can deploy anomaly detection at the data level to protect the actual assets that are the targets of malicious actors. Anomaly detection at the data level ensures that even if an attacker is able to get past the network level, he or she is most likely unable to get past the data level.

  1. Real-Time Anomaly Detection with Machine Learning

Machine learning (ML) has proven highly advantageous for advancing anomaly detection accuracy and helping companies and organizations manage big data. The ability of ML systems to learn by their own experience, thus refining their analytical and predictive capacity on their own, is a valuable feature for accurate anomaly detection.

An anomaly detection solution with ML enables the system to handle unlabeled data proactively, determining what is normal and what may be regarded as a data anomaly via a threat detection score. In addition, ML systems are much more sensitive to distinguishing data anomalies from mere noise, allowing them to differentiate data units based on the degree of their deviation from the norm.

Real-time anomaly detection combined with machine learning is now enabling organizations to proactively detect and prevent malicious attempts to access, use, and steal information.

To learn more, download Sotero’s Anomaly Detection Brief.


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