AI systems

Release version: Australia

Updated May 14, 2026

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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 life cycle. AI systems support organizational business activities by enabling automation, enhancing data-driven decision-making, and improving process efficiency. Key components include machine learning models, natural language processing, computer vision tools, datasets, algorithms, and computing infrastructure, all working together to analyze data and generate outcomes with minimal human intervention.

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Each AI system undergoes a structured evaluation process to assess risks related to functionality, security, and societal impact, verifying compliance with regulations, industry standards, and internal policies. Governance frameworks enforce ethical principles such as fairness, accountability, and transparency through controls and oversight, with ongoing monitoring to mitigate unintended consequences.

Key Features

AI System Record and Aggregated Risk Score: The AI system record in ServiceNow provides an aggregated risk score that consolidates individual risk assessments (e.g., bias, drift, security) from related AI assets. This score offers a comprehensive view of AI risks at departmental or enterprise levels, facilitating better oversight and risk management.
Risk Score Rollup: Individual risk scores from entities assessed using the Risk Assessment Methodology (RAM) roll up into the aggregated risk score, visible under the Details tab of the AI system record. This feature requires enabling the Advanced Risk application and the migration property for advanced risk assessments.
Related AI Assets Section: Lists AI models, datasets, and other AI entities linked to the AI system, providing a centralized view of all components associated with the system.
Related Tasks: Facilities to request AI use cases, AI models, and datasets directly linked to the AI system, streamlining governance and operational management.

What This Enables for ServiceNow Customers

ServiceNow customers can leverage AI systems to safely implement AI-driven solutions that adhere to governance, compliance, and ethical standards. The aggregated risk scoring and detailed AI asset associations provide clear visibility into AI risks and dependencies, enabling proactive risk management and operational efficiency. By using the governance framework and monitoring capabilities, customers can ensure their AI initiatives deliver trustworthy, explainable, and responsible outcomes aligned with organizational policies and regulatory requirements.

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 life cycle.

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 life cycle. 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.

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:

Details page of an AI system showing the aggregated risk score — Figure 2. 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.
Related entities: The AI assets associated as entities for this AI system. Only AI assets can be linked as related entities.