ID | Technique | Tactic |
---|---|---|
T1078 | Valid Accounts | Defense Evasion |
T1110 | Brute Force | Initial Access |
Detection: Okta Risk Threshold Exceeded
Description
The following correlation identifies when a user exceeds a risk threshold based on multiple suspicious Okta activities. It leverages the Risk Framework from Enterprise Security, aggregating risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. This detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs) within a 24-hour period. If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
Search
1
2| 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
3| `drop_dm_object_name("All_Risk")`
4| search mitre_technique_id_count > 5
5| `okta_risk_threshold_exceeded_filter`
Data Source
Name | Platform | Sourcetype | Source |
---|---|---|---|
Okta | N/A | 'OktaIM2:log' |
'Okta' |
Macros Used
Name | Value |
---|---|
security_content_summariesonly | summariesonly= summariesonly_config allow_old_summaries= oldsummaries_config fillnull_value= fillnull_config`` |
okta_risk_threshold_exceeded_filter | search * |
okta_risk_threshold_exceeded_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
Setting | Value |
---|---|
Disabled | true |
Cron Schedule | 0 * * * * |
Earliest Time | -70m@m |
Latest Time | -10m@m |
Schedule Window | auto |
Creates Notable | Yes |
Rule Title | %name% |
Rule Description | %description% |
Notable Event Fields | user, dest |
Creates Risk Event | False |
Implementation
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
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
Okta Risk threshold exceeded for user [$risk_object$]. Investigate further to determine if this was authorized. | 56 | 70 | 80 |
References
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | ✅ Passing | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | risk_data |
stash |
Integration | ✅ Passing | Dataset | risk_data |
stash |
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: GitHub | Version: 4