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

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil Governance, Risk, and Compliance

ft:clusterId :

    - grc

bundleId :

    - grc

workflow :

    - Technology


---

# Create a compliance evaluation configuration

# Create a compliance evaluation configuration {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read  
Define the AI systems, control objectives, metrics, and schedule that a compliance evaluation monitors on an ongoing basis.

## Before you begin

Role required: sn_grc_ai_gov.ai_risk_and_compliance_manager

## About this task

A compliance evaluation configuration monitors AI systems for compliance and updates evaluation scores on an ongoing basis. Use [evaluation frameworks](https://www.servicenow.com/docs/access?context=mon-ai-exploring-aict-evaluations&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US) from ServiceNow® and Traceloop to produce trace, session, or span records for your AI systems. Use these frameworks to produce an evaluation of your AI systems. For information about connecting external AI
systems to send trace data, see [Activate evaluation scoring for external AI systems](https://www.servicenow.com/docs/access?context=mon-ai-monitor-external-ai-system&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US).

## Procedure

1. Navigate to AllAI Risk and ComplianceAI Risk and Compliance Workspace.
2. Select the list icon ![]().
3. From the Controls monitoring module, select the Compliance evaluation configurations and select New.
4. Define the evaluation details and data scope details.  
   {#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__2}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__3}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__4}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__6}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__8}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__9}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__11}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__13}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__15}{#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc__entry__17}

   | Field | Description |
   | Basic details ||
   | Evaluation name | Unique, descriptive name for this evaluation, using specific terms that identify the AI system, assessment scope, or version being evaluated (for example, <kbd class="ph userinput">Monitoring AI Toxicity</kbd>). |
   | Description | Purpose and scope of this evaluation, including what is being assessed, why it matters, and any key constraints or context users should know. |
   | Data scope details ||
   | Authority documents | Authority documents that govern this evaluation. The following options are provided: * NIST AI Risk Management Framework * EU Artificial Intelligence Act * AI Content Safety \& Toxicity Standard {#airc-compliance-evaluation-configuration__ul_q4b_cwk_bkc} |
   | Policies | Policies that this evaluation aligns with or enforces. The following options are provided: * Artificial Intelligence Software Development Lifecycle Policy * Enterprise Artificial Intelligence Governance Policy * Internal use of AI Systems {#airc-compliance-evaluation-configuration__ul_r4b_cwk_bkc} |
   | AI system type | Category of AI system being evaluated. The following options are provided: * Agentic AI * Generative AI {#airc-compliance-evaluation-configuration__ul_s4b_cwk_bkc} |
   | Provider | Provider of the AI system. The following options are provided: * ServiceNow * Others {#airc-compliance-evaluation-configuration__ul_t4b_cwk_bkc} |
   | Metric category | Metric category used in this evaluation: * Safety * Security * Quality {#airc-compliance-evaluation-configuration__ul_u4b_cwk_bkc} |
   |-|-|
   [Table 1. Evaluation details and data scope fields]

   {#airc-compliance-evaluation-configuration__table_p4b_cwk_bkc}
5. Select Next.
6. Select Add control objectives to add control objectives to the scope.
7. Map control objectives from the filtered list by selecting the check box of each desired control objective.
8. Select Add to complete the control objective mapping, and then select Next.  
9. Define the evaluation criteria and frequency in the Evaluation criteria and frequency section.  
   {#airc-compliance-evaluation-configuration__table_kx1_2dj_bkc__entry__2}{#airc-compliance-evaluation-configuration__table_kx1_2dj_bkc__entry__3}{#airc-compliance-evaluation-configuration__table_kx1_2dj_bkc__entry__5}

   | Field | Description |
   | Frequency | Frequency at which the evaluation runs and updates evaluation scores for the AI systems in scope. The following options are provided: * Daily: Runs the evaluation every day. * Weekly: Runs the evaluation once a week. * Monthly: Runs the evaluation once a month. {#airc-compliance-evaluation-configuration__ul_lx1_2dj_bkc} For example, for AI toxicity, a monthly evaluation monitors control drift. |
   | Success condition result | Result assigned to an AI system when the evaluation criteria are met. The following options are provided: * Passed * Failed {#airc-compliance-evaluation-configuration__ul_succ_cond_res} |
   |-|-|
   [Table 2. Evaluation criteria and frequency fields]

   {#airc-compliance-evaluation-configuration__table_kx1_2dj_bkc}
10. In the All Evaluations section, define the conditions and the metrics under which the configuration is applicable.  
    If multiple conditions are specified, they are evaluated in the order listed to determine applicability.  
    {#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc__entry__2}{#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc__entry__3}{#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc__entry__5}{#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc__entry__7}{#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc__entry__8}

    | Field | Description |
    | Name | Name for the evaluation. |
    | Conditions | Field, operator, and value that filter which records this configuration applies to, for example, \[Risk classification\] \[is\] \[High\]. Select and or or to combine multiple conditions, and select New condition set to add a condition set that is evaluated independently of other condition sets. |
    | Metric thresholds ||
    | Metric | Metric, operator, and threshold value that determine when a control is evaluated as compliant or non-compliant, for example, \[Toxicity\] \[is\] \[True\]. Select Add metric to define additional metric thresholds for this configuration. |
    |-|-|
    [Table 3. Conditions and metric thresholds fields]

    {#airc-compliance-evaluation-configuration__table_tt5_cdj_bkc}
11. Select Submit.
{#airc-compliance-evaluation-configuration__steps_zrr_5tl_1kc}

## What to do next

The compliance evaluation configuration is ready to be mapped to one or more AI systems. For information about mapping an evaluation configuration to AI systems, see [Use a compliance evaluation on an AI system record](https://www.servicenow.com/docs/bX4pzNgLc4pTHstC~L0LMw "Map a control objective to an AI system, run its compliance evaluation, and review evaluation results, supporting data, and automatically created issues.").

*[\>]: and then