THIS IS A DEPRECATED DETECTION
This detection has been marked deprecated by the Splunk Threat Research team. This means that it will no longer be maintained or supported.
The following analytic detects by correlating repository and risk score to identify patterns and trends in the data based on the level of risk associated. The analytic adds any null values and calculates the sum of the risk scores for each detection. Then, the analytic captures the source and user information for each detection and sorts the results in ascending order based on the risk score. Finally, the analytic filters the detections with a risk score below 80 and focuses only on high-risk detections.This detection is important because it provides valuable insights into the distribution of high-risk activities across different repositories. It also identifies the most vulnerable repositories that are frequently targeted by potential threats. Additionally, it proactively detects and responds to potential threats, thereby minimizing the impact of attacks and safeguarding critical assets. Finally, it provides a comprehensive view of the risk landscape and helps to make informed decisions to protect the organization's data and infrastructure. False positives might occur so it is important to identify the impact of the attack and prioritize response and mitigation efforts.
- Type: Correlation
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-09-06
- Author: Patrick Bareiss, Splunk
- ID: 8da9fdd9-6a1b-4ae0-8a34-8c25e6be9687
Kill Chain Phase
- CIS 13
1 2 3 4 5 6 `risk_index` | fillnull | stats sum(risk_score) as risk_score values(source) as signals values(user) as user by repository | sort - risk_score | where risk_score > 80 | `correlation_by_repository_and_risk_filter`
The SPL above uses the following Macros:
correlation_by_repository_and_risk_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
For Dev Sec Ops POC
Known False Positives
Associated Analytic Story
|70.0||70||100||Correlation triggered for user $user$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1