Use SOC-ML anomalies to detect threats in Azure Sentinel
What are SOC-ML anomalies?
With attackers and defenders constantly fighting for advantage in the cybersecurity arms race, attackers are always finding ways to evade detection. Inevitably, though, attacks will still result in unusual behavior in the systems being attacked. Azure Sentinel's SOC-ML machine learning-based anomalies can identify this behavior with analytics rule templates that can be put to work right out of the box. While anomalies don't necessarily indicate malicious or even suspicious behavior by themselves, they can be used to improve detections, investigations, and threat hunting:
Additional signals to improve detection: Security analysts can use anomalies to detect new threats and make existing detections more effective. A single anomaly is not a strong signal of malicious behavior, but when combined with several anomalies that occur at different points on the kill chain, their cumulative effect is much stronger. Security analysts can enhance existing detections as well by making the unusual behavior identified by anomalies a condition for alerts to be fired.
Evidence during investigations: Security analysts also can use anomalies during investigations to help confirm a breach, find new paths for investigating it, and assess its potential impact. These efficiencies reduce the time security analysts spend on investigations.
The start of proactive threat hunts: Threat hunters can use anomalies as context to help determine whether their queries have uncovered suspicious behavior. When the behavior is suspicious, the anomalies also point toward potential paths for further hunting. These clues provided by anomalies reduce both the time to detect a threat and its chance to cause harm.
Anomalies can be powerful tools, but they are notoriously very noisy. They typically require a lot of tedious tuning for specific environments or complex post-processing. Azure Sentinel SOC-ML anomaly templates are tuned by our data science team to provide out-of-the box value, but should you need to tune them further, the process is simple and requires no knowledge of machine learning. The thresholds and parameters for many of the anomalies can be configured and fine-tuned through the already familiar analytics rule user interface. The performance of the original threshold and parameters can be compared to the new ones within the interface and further tuned as necessary during a testing, or flighting, phase. Once the anomaly meets the performance objectives, the anomaly with the new threshold or parameters can be promoted to production with the click of a button. Azure Sentinel SOC-ML anomalies enable you to get the benefit of anomalies without the hard work.
In this document, you learned how SOC-ML helps you detect anomalies in Azure Sentinel.