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sourceDocument: Australia Governance, Risk, and Compliance
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/governance-risk-compliance

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    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Governance, Risk, and Compliance

ft:clusterId :

    - grc

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    - Technology


---

# Datasets

# Datasets {#ariaid-title1}

Release version: Australia  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 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 Datasets in AI Risk and Compliance

Datasets in AI Risk and Compliance are essential for capturing and governing data utilized by AI models.
They enable organizations to evaluate risks, ensure regulatory compliance, and maintain transparency throughout the AI asset life cycle.
Proper dataset management supports governance objectives by documenting key details such as risk assessments, compliance status, ownership, audit trails, and performance metrics.
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## Key Features

* **Risk and Compliance Management:** Each AI dataset record includes an aggregated risk score derived from individual risk assessments (e.g., bias, drift, security) using the Risk Assessment Methodology (RAM). This consolidated score provides a comprehensive view of AI risks at departmental or enterprise levels, enhancing oversight and accountability.
* **Data Quality and Fairness:** Dataset quality---including completeness, accuracy, and relevance---is critical to model performance and fairness. Identification and mitigation of biases in datasets prevent unfair or inaccurate AI predictions.
* **Traceability and Transparency:** Tracking data lineage ensures accountability and transparency in dataset usage and maintenance.
* **Compliance with Regulations:** Datasets must be managed in accordance with data protection laws, privacy regulations, and organizational policies, with regular reviews to maintain quality and adapt to evolving standards.
* **Related AI Assets:** The system links datasets to associated AI systems and AI models that use them, facilitating holistic risk and asset management.

## Practical Benefits for ServiceNow Customers

By leveraging datasets within AI Risk and Compliance, ServiceNow customers can:

* Gain a unified, high-level understanding of AI risks across multiple models and business units through aggregated risk scores.
* Ensure AI models are built on reliable, well-curated data, improving model accuracy and fairness.
* Maintain compliance with relevant data governance and privacy regulations through structured dataset management and audit trails.
* Enhance decision-making and accountability by tracking dataset lineage and associating datasets with AI assets.

To access aggregated risk scores, customers need to enable the Advanced Risk application and migrate to Advanced Risk Assessments.  
Datasets in AI Risk and Compliance capture and govern the data used by AI models, enabling organizations to evaluate risk, ensure compliance, and maintain transparency across the AI asset life cycle.

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.

Manage datasets according to applicable data protection regulations, 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  
Individual risk scores for entities that use Risk assessment for AI inventory as the Risk Assessment Methodology (RAM) roll up to form the aggregated risk score. An AI dataset record provides 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](https://www.servicenow.com/docs/S17ZHpe9VYFpmR9vrY1X7w "In Advanced Risk Assessment, risk scores are calculated across risk statement hierarchy, entity hierarchy, or a combination of both. These methods enable stakeholders to monitor their risk posture and provide visibility of the overall aggregated risk score.").  
Note:  
To see the aggregated risk score, you must enable the Migrate to Advanced Risk Assessments (sn_risk_advanced.migrate_to_advanced_risk) under AllAdvanced RiskProperties.  
Note:  
This section appears only if the Advanced Risk application is installed.

The 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, rather than relying on fragmented risk assessments.

## Related AI assets {#dataset-airc__section_esp_xnt_bgc}

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.
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