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The analytic is aimed at identifying the most significant legal liability threats blocked by zcaler web proxy. It leverages web proxy logs to list the destinations, device owners, users, URL categories, and actions that are associated with Legal Liability, by utilizing stats on unique fields, it ensures a precise focus on unique legal liability threats, thereby providing valuable insights for organizations to enforce legal compliance and risk management.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2023-10-31
  • Author: Rod Soto, Gowthamaraj Rajendran, Splunk
  • ID: bbf55ebf-c416-4f62-94d9-4064f2a28014




ID Technique Tactic
T1566 Phishing Initial Access
Kill Chain Phase
  • Delivery
  • DE.AE
  • CIS 10
`zscaler_proxy` urlclass="Legal Liability" 
|  stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| dedup urlcategory 
| `zscaler_legal_liability_threat_blocked_filter`


The SPL above uses the following Macros:

:information_source: zscaler_legal_liability_threat_blocked_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.

  • action
  • threatname
  • deviceowner
  • user
  • urlcategory
  • url
  • dest
  • dest_ip
  • action

How To Implement

You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.

Known False Positives

False positives are limited to Zscaler configuration.

Associated Analytic Story


Risk Score Impact Confidence Message
16.0 20 80 Potential Legal Liability Threat from dest -[$dest$] on $src$ for user-[$user$].

: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.


Test Dataset

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

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