The following correlation will take risk associated with the content from "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories and tally it up. Once it hits the threshold of 100 (can be changed), it will trigger an a notable. As needed, reduce or raise the risk scores assocaited with the anomaly and TTP analytics tagged to these two analytic stories.
- Type: Correlation
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Risk
- Last Updated: 2022-09-29
- Author: Michael Haag, Splunk
- ID: d8b967dd-657f-4d88-93b5-c588bcd7218c
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 | tstats `security_content_summariesonly` sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk by All_Risk.risk_object,All_Risk.risk_object_type All_Risk.analyticstories | `drop_dm_object_name("All_Risk")` | eval "annotations.mitre_attack"="annotations.mitre_attack.mitre_technique_id", risk_threshold=100 | where All_Risk.analyticstories IN ("Suspicious Okta Activity", "Okta MFA Exhaustion") risk_score > risk_threshold | `get_risk_severity(risk_score)` | `okta_risk_threshold_exceeded_filter`
The SPL above uses the following Macros:
okta_risk_threshold_exceeded_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
Ensure "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories are enabled. TTP may be set to Notables for point detections, anomaly should not be notables but risk generators. The correlation relies on risk before generating a notable. Modify the value as needed. Default threshold is 100. This value may need to be increased based on activity in your environment.
Known False Positives
False positives will be limited to the amount of events generated by the analytics tied to the stories. Analytics will need to be tesetd and tuned, risk score reduced, as needed based on organization.
Associated Analytic Story
|56.0||70||80||Risk score $risk_score$ threshold exceeded for $risk_object$ related to Okta events.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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