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 the correlation between the user and risk score and identifies users with a high risk score that pose a significant security risk such as unauthorized access attempts, suspicious behavior, or potential insider threats. Next, the analytic calculates the sum of the risk scores and groups the results by user, the corresponding signals, and the repository. The results are sorted in descending order based on the risk score and filtered to include records with a risk score greater than 80. Finally, the results are passed through a correlation filter specific to the user and risk. This detection is important because it identifies users who have a high risk score and helps to prioritize investigations and allocate resources. False positives might occur but the impact of such an attack can vary depending on the specific scenario such as data exfiltration, system compromise, or the disruption of critical services. Please investigate this notable event.
- Type: Correlation
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-09-06
- Author: Patrick Bareiss, Splunk
- ID: 610e12dc-b6fa-4541-825e-4a0b3b6f6773
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(repository) as repository by user | sort - risk_score | where risk_score > 80 | `correlation_by_user_and_risk_filter`
The SPL above uses the following Macros:
correlation_by_user_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