Exploring AI Security Exposure Management

  • Release version: Australia
  • Updated June 8, 2026
  • 4 minutes to read
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    Summary of Exploring AI Security Exposure Management

    AI Security Exposure Management is a component of the Unified Security Exposure Management suite designed to help organizations manage AI-related security risks across their environments. It integrates with third-party AI security tools to identify and manage vulnerabilities in open source AI models, behavioral risks from harmful AI prompts, and infrastructure configuration issues in AI assets. This management capability is critical as AI adoption grows and new attack surfaces emerge, including runtime security breaches and data loss risks.

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    Key Features

    • AI Exposures Module: Provides comprehensive visibility into AI vulnerabilities, validation findings from automated penetration testing, and posture findings related to AI infrastructure configuration.
    • Third-Party Integrations: Imports AI vulnerability data, validation results, and posture findings into the ServiceNow AI Platform, enabling centralized management and correlation with your CMDB assets.
    • Guardrails Detector Skill: Identifies existing runtime guardrails in AI security platforms that mitigate risky AI behaviors, allowing vulnerability teams to defer findings that are already protected.
    • Agentic Workflow Automation: Automates deferral of findings with mitigations or guardrails and creates exception rules to streamline future findings management, optimizing remediation workflow efficiency.
    • MITRE ATLAS Integration: Associates AI security findings with relevant MITRE ATLAS tactics and techniques, improving contextual understanding of AI risks.

    Who Benefits

    This solution is primarily targeted at vulnerability analysts, vulnerability managers, and Chief Information Security Officers (CISOs) who need to:

    • Monitor AI-related risk posture comprehensively.
    • Assign remediation tasks based on asset ownership and severity.
    • Streamline prioritization and remediation of AI exposures.
    • Communicate AI risk posture and remediation progress to stakeholders effectively.

    Practical Outcomes

    • Efficient prioritization of AI security exposures by focusing on high-risk issues while deferring lower-risk findings protected by guardrails.
    • Reduced meantime to remediate critical AI vulnerabilities and behavioral risks.
    • Improved visibility and control over the AI attack surface, including open source model vulnerabilities, harmful AI behaviors, and configuration issues.
    • Automated workflows that reduce manual effort in managing AI security findings.

    Next Steps for ServiceNow Customers

    To effectively implement AI Security Exposure Management, customers should explore configuration and usage guides, including:

    • Viewing AI exposures within the AI Exposures module.
    • Installing and configuring AI Security Exposure Management.
    • Utilizing the AI guardrails helper skill and agentic workflows for automated findings deferral and mitigation understanding.

    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.

    AI Security Exposure Management overview

    With the rapid growth and adoption of AI in enterprises, a new attack surface emerging in the form of AI security exposures, that includes open source AI model vulnerabilities and AI model behavioral risks with harmful prompts that could result in security breaches and data loss during runtime. In addition, AI infrastructure configuration issues might exist in AI agents, data sets, or any other type of AI assets in your environment.

    AI Security Exposure Management can help your organization efficiently manage AI security exposures such as AI model vulnerabilities, harmful AI model behavior, and AI infrastructure configuration issues.

    Use the Guardrails Detector skill to identify existing guardrails that can mitigate some of the AI validation findings that indicate risky behavior of the AI application or model.

    Use an agentic workflowAI to automate the deferral of findings that have mitigations or guardrails and create exception rules to auto-defer future findings.

    With AI Security Exposure Management, vulnerability managers can prioritize high risk exposures and defer low risk exposures that might have mitigations or guardrails already in place. This prioritization ultimately helps vulnerability management teams optimize remediation workflows to help them reduce the meantime to remediate their high risk exposures.

    Key terms for AI Security Exposure Management

    Navigate to Workspaces > Security Exposure Management > AI Exposures.

    AI vulnerabilities
    Vulnerabilities that are discovered in open source AI models that are published in repositories. Third-party integrations perform static scans of AI models for these vulnerabilities. The findings (AISF) generated by this application are generally open source, but other models such as self-hosted models are also supported. A finding is created when a known model vulnerability or behavior can be matched to an AI model (asset) in your CMDB.
    The following types of findings are generated and maintained AI Security Exposure Management (AISEC):
    • AI Vulnerability Finding (AIVUL)
    • AI Validation Finding (AIVF)
    • AI Posture Finding (AIPF)
    AI validation findings
    Findings from third-party automated penetration testing or automated red teaming done to verify the behavior of AI applications or models by validating them against their prompt libraries. For example, third-party vendors test AI applications for issues like Personally Identifiable Information (PII) leakage.
    AI posture findings
    Configuration issues in AI agents, tools, prompts, MCP servers that are detected by third-party AI security tools in various platforms such as Microsoft Copilot Studio, AWS, and others.
    Service Graph Connector
    Type of third-party integration that imports AI inventory data into your CMDB.
    AI security exposure management integrations
    Third-party integrations that import AI vulnerabilities, validation findings, and posture or configuration findings from AI security tools into tables in your ServiceNow AI Platform® instance.
    Guardrails detection

    AI security platforms support runtime protection policies or guardrails that can detect AI behavior risks such as sensitive information disclosure and block or redact the content in the payload.

    ServiceNow® AI Security Exposure Management employs an AI skill to map these guardrails that are enabled in AI security platforms with the AI validation findings (automated red teaming results) that are reported by those platforms. This information about available guardrails can be used by vulnerability analysts to defer AI validation findings that are mitigated by these guardrails.

    MITRE ATLAS techniques

    AI security platforms associate all the findings, that is, vulnerabilities, validation findings, and posture findings, with relevant MITRE ATLAS tactics and techniques. This information is imported by your ServiceNow AI Platform® instance and displayed as part of the AI security finding details.

    AI Security Exposure Management users

    As an example, consider a vulnerability analyst and a vulnerability team that is working for a large financial services company. The team is in the process of converting legacy applications into AI-native applications and is encountering a high volume of AI exposures. To help them mitigate high risk exposure threats and identify and defer AI security issues that have guardrails already in place for their large volume of exposures, this vulnerability team requires an automated remediation workflow.

    Table 1. Users
    User Description
    Vulnerability analysts, vulnerability managers, and Chief Information Security Officers (CISO) Monitors the organization’s overall risk posture across integrated environments, ensuring accurate asset discovery and classification for AI exposures correlation. These roles serve as an escalation point for remediation teams, assigns remediation tasks based on asset ownership and severity, and organizes AI exposure information into dynamic remediation tasks to streamline prioritization. Additionally, the role delivers actionable dashboards and reports to track remediation progress, highlight critical AI exposures, and communicate the current risk posture to stakeholders.

    AI Security Exposure Management benefits

    Benefit Feature Users
    A dedicated module, AI exposures, that provides visibility into the entire AI attack surface, including vulnerabilities, validation or automated red teaming findings, and security posture findings or configuration issues in various AI assets. AI Exposures module Vulnerability analysts, vulnerability management teams, Chief Information Security Officers (CISO).

    What to explore next

    To learn more about configuring and using AI Security Exposure Management, see: