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sourceDocument: Australia Governance, Risk, and Compliance
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/de-DE/governance-risk-compliance

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ft:publication_title :

    - Australia Governance, Risk, and Compliance

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---

# Control Objective clustering skill

# Control Objective clustering skill {#ariaid-title1}

* Freigeben Version: Australia
* 
* Aktualisiert 12. März 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 Minute Lesedauer

Understand how the Control objective clustering uses a generative AI framework (GAF) to automatically group similar control objectives into clusters, helping compliance managers identify duplicates and perform rationalization
more efficiently.

## How clustering works {#control-objective-clustering-skill__section_tv4_t4z_3hc}

Cluster-based rationalization uses a generative AI framework (GAF) scheduled job to create groups of similar control objectives before the user begins rationalization. The user then reviews these clusters rather than searching for
similarities record by record.

The GAF job evaluates control objectives based on their name, description, and table name. The results are stored in the control objective cluster table. Clusters consist of normal groups, which contain similar control objectives, and
outlier groups, which contain control objectives that the system could not confidently place in a cluster.

## Clusters and outliers {#control-objective-clustering-skill__section_d2c_z4z_3hc}

After the GAF job runs, control objectives are organized as follows:

Clustered control objectives
:   Control objectives that the system has grouped together based on similarity. These appear in the Clusters module along with their cluster group and subgroup information.

Outliers
:   Control objectives that could not be confidently assigned to a cluster. Outliers appear in both the Clusters module (accessible through the Outliers filter) and the dedicated
    Unclustered Control Objectives module. A single main outlier group can contain multiple subgroups, each identified with a -1 suffix.

Users can rationalize both clustered and outlier control objectives. Although outliers are those the system was not confident about, users can still select and rationalize them if they determine that two or more outliers are
duplicates.