Datasets

  • Release version: Zurich
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Datasets

    A dataset in ServiceNow’s AI governance framework is a curated collection of structured data essential for developing, deploying, and monitoring AI systems under organizational policies, regulations, and ethical standards. Proper dataset management supports governance by capturing vital information about AI models—such as risk assessments, compliance status, ownership, audit trails, and performance metrics—enabling effective oversight, accountability, and decision-making.

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    The quality and composition of datasets significantly influence AI model performance, fairness, and accuracy. Evaluating datasets for completeness, accuracy, relevance, and bias mitigation is critical to ensure reliable, ethical model outputs. Maintaining data lineage and compliance with data protection regulations is necessary for transparency and accountability. Regular dataset reviews and updates align data quality with evolving standards and business needs.

    Key Features

    • Aggregated Risk Score: Each AI dataset record provides an aggregated risk score that consolidates individual risk scores (e.g., bias, drift, security) from related entities using the Risk Assessment Methodology (RAM). This score appears in the AI system record’s Details tab, facilitating enterprise-level AI risk profiling and enhanced visibility.
    • Risk Score Rollup: The aggregated risk score requires enabling the Advanced Risk application and specific migration properties to be visible and functional, supporting consolidated risk management across models and business units.
    • Related AI Assets: The dataset record links to related AI systems and AI models that utilize the dataset, ensuring traceability between data and AI assets for governance and operational clarity.

    Key Outcomes

    • Improved AI governance through transparent dataset management aligned with compliance and ethical standards.
    • Enhanced oversight and accountability enabled by consolidated risk scoring that informs organizational AI risk profiles.
    • Higher confidence in AI model reliability and fairness by managing dataset quality, bias mitigation, and data lineage.
    • Clear relationships between datasets and AI assets, supporting traceability and effective impact analysis.

    A dataset is a curated collection of structured data used to develop, deploy, and monitor AI systems in line with organizational policies, regulations, and ethical standards.

    The AI dataset supports governance objectives by capturing key information about AI models, including risk assessments, compliance status, ownership, audit trails, and performance metrics. It also enables effective oversight, accountability, and decision-making within the organization. The quality and composition of a dataset directly impact the performance, fairness, and accuracy of the AI model. Well-curated datasets help verify that models learn meaningful patterns and generate reliable outputs in real-world scenarios.

    Each dataset should be evaluated for completeness, accuracy, and relevance to the intended use case. Bias in datasets can lead to unfair or inaccurate model predictions and should be identified and mitigated. Tracking data lineage helps verify traceability, transparency, and accountability in how datasets are used and maintained.

    Datasets must comply with data protection regulations, including privacy laws and organizational data handling policies. Regular reviews and updates help maintain dataset quality and reflect evolving data standards or business needs.

    The following image shows the overview page of datasets.
    Figure 1. Datasets overview page
    Datasets overview page
    An AI dataset 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.

    Related AI assets

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

    • AI systems: The AI systems that use this AI dataset.
    • AI models: The AI models that use this AI dataset.