AI systems

  • Release version: Yokohama
  • Updated July 31, 2025
  • 2 minutes to read
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    Summary of AI systems

    An AI system in ServiceNow is an AI-powered solution developed, deployed, and managed under a formal governance framework. This framework ensures responsible, compliant, and risk-aware operation throughout the AI system’s lifecycle. AI systems function within an organization’s digital or operational environment to support business activities by enabling automation, enhancing data-driven decisions, and improving process efficiency.

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    These systems include components like machine learning models, natural language processing engines, and computer vision tools, supported by datasets, algorithms, and computing infrastructure. Together, they analyze data, recognize patterns, and generate outcomes with minimal human intervention.

    Governance and Risk Management

    Each AI system undergoes a structured evaluation process assessing risks related to functionality, security, and societal impact. Compliance with regulations, industry standards, and internal policies is verified. Governance frameworks enforce ethical principles such as fairness, accountability, and transparency through controls and oversight, ensuring secure, predictable, explainable, and trustworthy AI outcomes.

    Regular monitoring reduces unintended consequences and supports responsible AI use.

    Aggregated Risk Score and Risk Visibility

    The AI system record in ServiceNow provides an aggregated risk score that consolidates individual risks—including bias, drift, and security—across AI assets. This score is visible on the Details tab of the AI system record under the Aggregated risk score section, provided the Advanced Risk application is installed and the migration to Advanced Risk Assessments is enabled.

    This aggregated score empowers AI Risk and Compliance teams to gain a consolidated view of AI risks across multiple models, teams, and business units, improving higher-level visibility and oversight beyond fragmented risk assessments. For example, multiple customer-facing AI models exhibiting bias can be identified as an organizational risk through this consolidated score.

    Related AI Assets

    The AI system record also lists related AI assets, including:

    • AI models: The AI models associated with the system.
    • Datasets: The datasets utilized within the AI system.

    This relationship tracking helps maintain transparency and manage dependencies within the AI system.

    An AI system is an AI-powered solution that is developed, deployed, and managed under a formal governance framework. This framework ensures that the system operates in a responsible, compliant, and risk-aware manner throughout its lifecycle.

    An AI system functions within an organization’s digital or operational environment to support business activities. It enables automation, enhances data-driven decision-making, and improves overall process efficiency. The system includes components such as machine learning models, natural language processing engines, or computer vision tools.

    These components are often supported by datasets, algorithms, and computing infrastructure. Together, they enable the system to analyze data, recognize patterns, and generate outcomes with minimal human intervention. Each AI system is subject to a structured evaluation process. This process assesses potential risks related to functionality, security, and societal impact. It also verifies compliance with applicable regulations, industry standards, and internal governance policies.

    Governance frameworks are applied to manage the AI system throughout its lifecycle. They ensure that the system operates securely and delivers predictable, explainable, and trustworthy results. Ethical principles such as fairness, accountability, and transparency are enforced through defined controls and oversight mechanisms. Regular monitoring supports responsible AI use and reduces the risk of unintended consequences.

    The following image shows the overview page of an AI system.
    Figure 1. AI system overview page
    AI system overview page
    An AI system record provides an aggregated risk score. The individual risk scores for entities, that have Risk assessment for AI inventory as the Risk Assessment Methodology (RAM) roll-up and form an aggregated risk score. You can see the aggregated risk score under the Details tab of the AI system record in the Aggregated risk score section. For more information about how risk score is rolled up, see Risk score rollup in Advanced Risk Assessment.
    Important:
    To see the aggregated risk score, you must enable the Migrate to Advanced Risk Assessments (sn_risk_advanced.migrate_to_advanced_risk) under All > Advanced Risk > Properties.
    Note:
    You can see this section only if the Advanced Risk application is installed.

    Aggregated risk score consolidates individual risks such as bias, drift, and security, to inform departmental or enterprise-level AI risk profiles, enabling higher-level visibility and oversight. For example, several customer-facing AI models exhibiting signs of bias can lead to organizational risks. Aggregated risk score enables the AI Risk and Compliance team to obtain a consolidated view of AI risks across multiple models, teams, and business units, moving beyond fragmented risk assessments.

    The following image shows the aggregated risk score section:
    Figure 2. Aggregated risk score
    Details page of an AI system showing the aggregated risk score

    Related AI assets

    The Related AI assets section lists the following for an AI system:

    • AI models: The AI models within this AI system.
    • Datasets: The datasets used within this AI system.