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Description

This correlation computes the risk events associated with the detection analytics from "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion" analytic stories. This analytic will trigger a notable event in your incident review when there are 5 or more distinct TTPs related to these analytic stories in the last 24 hours. This incident highlights potentially suspicious activity by a compromised user.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2024-04-02
  • Author: Michael Haag, Bhavin Patel, 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` values(All_Risk.analyticstories) as analyticstories  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  where All_Risk.risk_object_type = user All_Risk.analyticstories IN ("Okta Account Takeover", "Suspicious Okta Activity","Okta MFA Exhaustion") by All_Risk.risk_object,All_Risk.risk_object_type 
| `drop_dm_object_name("All_Risk")` 
|  search mitre_technique_id_count > 5 
| `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
  • All_Risk.calculated_risk_score
  • All_Risk.annotations.mitre_attack.mitre_tactic_id
  • All_Risk.annotations.mitre_attack.mitre_technique_id
  • All_Risk.tag
  • _time

How To Implement

This search leverages the Risk Framework from Enterprise Security. Ensure that "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion" analytic stories are enabled. TTPs may be set to Notables for point detections; anomalies should not be notables but rather risk generators. The correlation relies on risk before generating a notable. Modify the value as needed.

Known False Positives

False positives will be limited to the number of events generated by the analytics tied to the stories. Analytics will need to be tested and tuned, and the risk score reduced as needed based on the organization.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
56.0 70 80 Okta Risk threshold exceeded for user [$risk_object$]. Investigate further to determine if this was authorized.

: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

source | version: 2