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Description

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 (may be changed), it will trigger an anomaly. As needed, reduce or raise the risk scores assocaited with the anomaly and TTP analytics tagged to these two 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

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
T1110 Brute Force Credential Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| 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`

Macros

The SPL above uses the following Macros:

:information_source: 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.

Required fields

List of fields required to use this analytic.

  • All_Risk.risk_object
  • All_Risk.risk_object_type
  • All_Risk.analyticstories

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

RBA

Risk Score Impact Confidence Message
56.0 70 80 Risk threshold exceeded for $risk_object_type$=$risk_object$ related to Okta events.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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