Detection: Okta Risk Threshold Exceeded

Updated Date: 2024-05-28 ID: d8b967dd-657f-4d88-93b5-c588bcd7218c Author: Michael Haag, Bhavin Patel, Splunk Type: Correlation Product: Splunk Enterprise Security

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.

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 Supported App
Okta N/A 'OktaIM2:log' 'Okta' N/A

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

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1078 Valid Accounts Defense Evasion
T1110 Brute Force Initial Access
KillChainPhase.DELIVERY
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT18
APT28
APT29
APT33
APT39
APT41
Akira
Axiom
Carbanak
Chimera
Cinnamon Tempest
Dragonfly
FIN10
FIN4
FIN5
FIN6
FIN7
FIN8
Fox Kitten
GALLIUM
Ke3chang
LAPSUS$
Lazarus Group
Leviathan
OilRig
POLONIUM
PittyTiger
Sandworm Team
Silence
Silent Librarian
Suckfly
Threat Group-3390
Wizard Spider
menuPass
APT28
APT38
APT39
DarkVishnya
Dragonfly
FIN5
Fox Kitten
HEXANE
OilRig
Turla

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
This configuration file applies to all detections of type Correlation. These correlations will generate Notable Events.

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

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: 3