Cortex Advanced Email Security threat detection and issues - The Cortex Advanced Email Security module uses artifact-based, metadata-driven, and LLM-powered engines to generate detections and insights. - Administrator Guide - Cortex XDR - Cortex

Cortex Advanced Email Security threat detection and issues - The Cortex Advanced Email Security module uses artifact-based, metadata-driven, and LLM-powered engines to generate detections and insights. - Administrator Guide - Cortex XDR - Cortex - Security Operations

Cortex XDR 5.x Documentation

Product

Cortex XDR

License

XDR + Cloud

Creation date

2025-07-13

Last date published

2026-06-11

Threat detection categories

All analytics-based detections are documented in the external Analytics Issue Reference, including the following:

Issue name
Trigger conditions
Associated MITRE TTPs (where applicable)
Recommended response actions

In addition to Analytics listed in the reference, the module supports the following extended categories:

WF Analysis issues: Generated based on WildFire verdicts (malicious/suspicious) for file attachments, where integration is enabled.
AURL issues: Based on Advanced URL analysis verdicts (for example, detected phishing kit, dynamic redirects, credential harvesting behavior).
IOC-Based issues: Triggered when an email contains known malicious indicators (SHA256, domain, URL, or sender) that match internal or external blocklists.
User-Reported Phishing issues : Generated when users forward emails to a designated phishing report address. These issues can be generated independently or correlated with other detection logic if matches are found.

Each issue type may be subject to additional correlation and aggregation into case entities based on shared characteristics, for example, sender, artifact, theme, etc.

Email security issue metadata and fields

Each issue contains a structured set of metadata fields that provide forensic and contextual insight for downstream investigation. Below is a breakdown of key issue fields available via the console, APIs, or case export.

Attribute	Field Name	Description
Issue Name	issue_name	High-level issue classification
Issue Description	issue_description	Human-readable description of the threat
Message ID	internet_message_id	Unique ID of the email message
Conversation ID	conversation_id	Thread/conversation identifier
Email Created Date	created_date	Timestamp when the message was sent
Subject	subject	Subject line of the email
Email Recipient(s)	recipients.name, recipients.email	All TO recipients of the email
CC Recipient(s)	cc_recipients.name, cc_recipients.email	All CC recipients
BCC Recipient(s)	bcc_recipients.name, bcc_recipients.email	All BCC recipients
From Address	from.address	Displayed From: email address
From Display Name	from.name	Display name shown in From field
Sender Address	sender.address	Actual sender address (SMTP-level)
Sender Name	sender.name	Display name of sender (SMTP envelope)
Return-Path	return_path_data.address	Return path address (SMTP envelope)
Attachment SHA256	attachments.hash_str	Hashes of attached files
Attachment Name	attachments.name	Filenames of attached files
URLs	url_verdicts.url_name	URLs extracted from the message body
Internet Message Headers	internet_message_headers	Full set of original headers

Note

Depending on data collection mode and platform capabilities, not all fields may be populated for every message. API and export documentation provides further clarification on optional vs required fields.

Email security issue correlation mechanism

The issue correlation layer is responsible for linking related issues into cohesive cases. Correlation is performed using the following logic.

Sender-based correlation: Issues from the same sender with similar delivery patterns across multiple recipients.
Artifact-based correlation: Issues with shared attachment hashes, URLs, or domains.
User-based correlation: Issues involving the same recipient or identity in a short time window.

Correlated issues are grouped into a single case object with unified investigation timelines and shared contextual insights (for example, conversation metadata, risky user involvement, cumulative score).