---
sourceDocument: Australia Security Management
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/security-management

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Security Management

ft:clusterId :

    - security

bundleId :

    - security

workflow :

    - Technology


---

# AI Exposures

# Viewing AI Exposures {#ariaid-title1}

Release version: Australia  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Viewing AI Exposures

The AI Exposures module, part of the Security Exposure Management workspace, enables ServiceNow customers to comprehensively monitor the attack surface related to AI assets.
It consolidates AI-related findings into three categories---AI vulnerabilities, AI validation findings, and AI posture findings---providing visibility across these areas through the AI Security Exposure Management dashboard.
This module helps identify and manage risks associated with AI models, their validation, and compliance posture.
Show full answer Show less  
Access requires specific roles such as **snvul.vulnerabilityadmin** , **snsecai.vulnerabilityanalyst**, and remediation owners among others.

## Key Features

* **Dashboard with Aggregated AI Exposure Data:** Displays total counts of findings by remediation status (Open, Unassigned, Approaching Target, Overdue) across AI vulnerabilities, validation, and posture.
* **AI Vulnerabilities Tab:** Shows vulnerabilities discovered in open source AI models scanned at the file level, including metrics on models and files scanned. Visualizations include findings by risk rating, vulnerability categories, MITRE ATLAS attack techniques, and trends over time.
* **AI Validation Findings Tab:** Presents results from automated penetration testing and red teaming of AI models, including open and mitigated findings, active runtime guardrails, and attack attempts. Findings are categorized by risk, threat categories, and MITRE ATLAS techniques.
* **AI Posture Findings Tab:** Focuses on configuration and compliance-related vulnerabilities across the AI agent ecosystem (agents, tools, system prompts, MCP servers), with metrics on posture issues and categorized findings.
* **Inventory Tracking:** Maintains a breakdown of AI assets discovered, which form the basis for all findings and risk assessments in the module.
* **Data Aggregation and Refresh:** Dashboard data is aggregated daily by a scheduled job, with an option to refresh on demand to view updated results.

## Practical Use and Data Management

The AI Security Exposure Management dashboard enables security analysts and remediation owners to:

* Filter and drill down into specific AI exposure categories and findings.
* Understand severity and types of AI vulnerabilities, validation failures, and posture gaps.
* Track remediation progress with clear status indicators and due date alerts.
* Leverage MITRE ATLAS techniques to comprehend adversarial behaviors behind findings.

Data underlying the dashboard is stored in dedicated tables for AI scans, validation, and posture findings, which support aggregation and reporting. This structured approach ensures customers can monitor AI security risks comprehensively and prioritize remediation effectively.  
Access the entire attack surface across various types of findings on the AI Security Exposure Management dashboard with the AI Exposures module. AI Security Exposure Management is a dedicated module of the Security Exposure
Management workspace.

## AI Exposures overview {#ai-security-exposure-home__section_wx2_swy_q3c}

See [Exploring AI Security Exposure Management](https://www.servicenow.com/docs/bLpXjCstnGn06kIxPnEKnQ "AI Security Exposure Management is a part of the Unified Security Exposure Management product suite of applications. AI Security Exposure Management integrates with third-party AI security products to help you manage various types of potential AI exposure across your environment.") for an overview and more information about the application.

See the [AI security exposure management](https://www.servicenow.com/community/secops-articles/reduce-ai-attack-surface-with-ai-security-exposure-management/ta-p/3563696) article in the Security Operations Community for more information about AI Security Exposure Management.  
Roles required.

* sn_vul.vulnerability_admin
* sn_vul.vulnerability_analyst
* sn_vul.remediation_owner
* sn_sec_ai.vulnerability_admin
* sn_sec_ai.vulnerability_analyst
* sn_sec_ai.remediation_owner
{#ai-security-exposure-home__ul_u1r_3j5_djc}  
There are three categories of AI exposures that are displayed on the AI Security Exposure Management dashboard.

* AI vulnerabilities
* AI validation findings
* AI posture findings

{#ai-security-exposure-home__ul_xdj_hxy_q3c}  
Note:  
These findings categories are also displayed on the findings page along with infrastructure, application, and container findings under All Active Findings on the Security Exposure Management workspace [Findings view](https://www.servicenow.com/docs/OoBg9NK2Fz~7riVmx_a5Sw "The Security Exposure Management Workspace Findings view aggregates the security exposure data and presents it as dashboards containing data visualization widgets.") (Home landing page).

Select a filter to view risk and exposure for AI findings.

Navigate to WorkspacesSecurity Exposure ManagementAI Security Exposure Management.

The totals displayed on the dashboard are aggregated (totaled) by a scheduled job that by default runs daily. When you open dashboard, these aggregated results from the scheduled job are displayed. To see data on-demand, select
Refresh. This activates the background job and the page refreshes with the aggregated result when the job completes.

Select a tab to view visualizations for each category.

## Overview section {#ai-security-exposure-home__section_tww_fv5_djc}

The Overview section displays the total counts of finding remediation status for AI vulnerabilities, AI validation findings, and AI posture findings of AI exposures for Open findings, Unassigned, Approaching Target, and Overdue.  
Select a tab to filter your lists by category and select a tile to open the filtered lists.{#ai-security-exposure-home__table_c4r_qjs_tjc__entry__2}

| Overview tile | Description |
|-|-|
| Open findings | Total findings still in an open state across vulnerabilities, validation, and posture combined. |
| Unassigned findings | Findings with no owner assigned. |
| Findings Approaching Target | Findings nearing their remediation-due date. |
| Overdue findings | Findings already past their due date. A value of 0 means the queue is reflecting good remediation timing. |
[ ]

{#ai-security-exposure-home__table_c4r_qjs_tjc}

## AI vulnerabilities tab {#ai-security-exposure-home__section_e51_nyy_q3c}

This is data about vulnerabilities that are discovered in open source AI models that are published in repositories.  

Scan metrics section

:   Select a card (widget) to open a list of records. Scanning happens at the file level, which is why the number of model files scanned far exceeds the number of models scanned.

    * Open vulnerabilities - The number of open Vulnerability findings discovered by scanning model files.
    * Models scanned - How many distinct AI models AISEM has scanned.
    * Model files scanned - How many individual files were scanned. One model can hold many files (weights, config, tokenizer, Python modeling code) so this value is much larger than Models scanned.
    {#ai-security-exposure-home__ul_ecg_hzy_q3c}

Findings

:   Select a card (widget) to open a list of records.

    * By risk rating - Findings grouped by severity (Critical to None).
    * By top 5 categories - Vulnerability types detected, for example, Unapproved, Knowledge Base Poisoning, Obfuscation vulnerability, Unauthorized, etc. Each bar represents a class of unsafe model file behavior.
    * By top 5 MITRE ATLAS techniques - (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of tactics and techniques attackers use against AI/ML systems --- the AI-specific cousin of MITRE ATT\&CK. Findings are tagged to ATLAS techniques so an analyst can understand the adversarial behavior behind a finding, not just the symptom.
    * By open vs closed state - A trend line of findings opened vs. closed over time. Lets you see if the backlog is increasing or decreasing.
    {#ai-security-exposure-home__ul_vk1_lzy_q3c}

## AI validation findings tab {#ai-security-exposure-home__section_etn_xzy_q3c}

These findings are from third-party automated penetration testing or automated red teaming done to verify the behavior of some of these models by scanning them against their prompt libraries.  

Validation metrics section

:   Select a card (widget) to open a list of records.

    * Open validation findings - Open findings where a model produced an unsafe or undesired response under testing.
    * Mitigated findings - Findings now covered by a guardrail that blocks them at runtime.
    * Active guardrails - Runtime guardrails currently active and blocking.
    * Models tested - Number of models put through validation attacks.
    * Number of attacks - Total adversarial attempts run against those models. Each attack is one probe; findings are the attacks that succeeded in eliciting bad behavior.
    {#ai-security-exposure-home__ul_tyr_11z_q3c}

Findings section

:   Select a card (widget) to open a list of records for Model vulnerability findings.

    Select a card to open a list of records for model validation findings. Refer to the Findings definitions in the previous sections for these definitions in the AI vulnerabilities tab.

    * By risk rating
    * By top 5 threat categories
    * By top 5 attack techniques
    * By MITRE ATLAS techniques
    * By top 5 models
    {#ai-security-exposure-home__ul_vj4_21z_q3c}

## AI posture findings tab {#ai-security-exposure-home__section_zyg_r55_djc}

These are findings for configuration-related vulnerabilities to help you verify that your AI assets are in compliance with your policies and controls.

Posture metrics
:   Select a card.

    * Open AI posture findings - Total open governance/hygiene findings.
    * Agents with findings - AI agents flagged with a posture issue.
    * Tools with findings - Tools flagged that agents can call.
    * System prompts with findings - System prompts flagged.
    * MCP servers with findings - MCP servers flagged.

    {#ai-security-exposure-home__ul_qmd_rck_ljc}  
    Note:  
    The last four tiles map to the agentic AI stackagenttoolspromptsMCP servers.

    They may read 0, but they show that AISEM watches posture across the whole agent ecosystem, not just standalone models.

Findings

:   Select a card for AI posture findings. Refer to the Findings definitions in the previous sections for these definitions in the AI vulnerabilities tab.

    * By risk rating
    * By top 5 platforms
    * By top 5 AI posture rules
    * By top 5 critical agents by platform
    * By top 5 MITRE ATLAS techniques
    * By top 5 OWASP LLM categories
    {#ai-security-exposure-home__ul_g4z_w55_djc}

## Inventory {#ai-security-exposure-home__section_tbm_31z_q3c}

AI models (total count) - A breakdown of AI inventory showing counts of different AI assets with findings reported. Inventory is the denominator. Before scanning, validation, or posture work has any meaningful data, AISEM has to
discover the AI footprint. Every finding in the other three tabs traces back to an asset that showed up here first.

## Tables storing imported data and used for the dashboard {#ai-security-exposure-home__section_tables_imported_data}

For scans of AI models, imported data is populated on the following tables and used for the dashboard. The data is aggregated, and the system currently runs daily aggregations.

For model vulnerabilities, imported data is populated on the following tables and used for the dashboard.

* AI Scan Summaries \[sn_sec_ai_scan_summary\]
* AI Scan Findings \[sn_sec_ai_scan_finding\]
* Discovered AI Assets \[sn_sec_ai_src_ci\]
* AI Vulnerability Entries \[sn_sec_ai_vul_entry\]
* Model Files \[sn_sec_ai_file\]

{#ai-security-exposure-home__ul_urq_bsz_q3c}

For model validations, imported data is populated on the following tables and used for the dashboard.

* AI Validation Findings \[sn_sec_ai_validation_finding\]
* AI Validation Threat \[sn_sec_ai_validation_threat\]
* AI Threat Signatures \[sn_sec_ai_threat_signature\]

{#ai-security-exposure-home__ul_zmb_jsz_q3c}

For AI posture findings, imported data is populated on the following tables and used for the dashboard.

* AI Posture Finding \[sn_sec_ai_posture_finding\]
* AI Posture Rule \[sn_sec_ai_posture_rule\]
* Finding guardrail \[sn_sec_ai_m2m_finding_guardrail\]
{#ai-security-exposure-home__ul_mw3_vw5_djc}

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