--- 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 --- # AI systems # AI systems {#ariaid-title1} Release version: Brazil 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 AI systems An AI system is an AI-powered solution developed, deployed, and managed within a formal governance framework. This framework ensures the system operates responsibly, complies with regulations, and manages risks throughout its life cycle. AI systems support business activities by enabling automation, enhancing data-driven decision-making, and improving process efficiency. They consist of components like machine learning models, natural language processing engines, or computer vision tools, supported by datasets, algorithms, and computing infrastructure to analyze data, recognize patterns, and generate outcomes with minimal human intervention. Show full answer Show less ## Governance and Risk Management Each AI system undergoes a structured evaluation process to assess risks related to functionality, security, and societal impact, while verifying compliance with regulations, industry standards, and internal policies. Governance frameworks enforce ethical principles such as fairness, accountability, and transparency through controls and oversight. Regular monitoring supports responsible AI use and mitigates unintended consequences. ## Risk Assessment and Aggregated Risk Score The AI system record includes an aggregated risk score that consolidates individual risks like bias, drift, and security. This score provides departmental or enterprise-level visibility into AI risk profiles, enabling better oversight. To view this score, customers must enable the Advanced Risk application and activate the migration to Advanced Risk Assessments property. This consolidated risk view helps risk and compliance teams monitor organizational AI risks across multiple models, teams, and business units, moving beyond isolated risk assessments. ## Related AI Assets and Tasks The AI system record lists related AI assets, including: * **AI models:** Models included within the AI system. * **Datasets:** Data sets used by the AI system. * **Related entities:** Associated AI assets linked as entities. Additionally, customers can request AI use cases, models, or datasets directly from the platform to support AI system development and management. 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.Figure 1. AI system overview page 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](https://www.servicenow.com/docs/Bf75lYM3mws6r3zIUj7gBA "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."). Important: 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: 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:Figure 2. Aggregated risk score ## Related AI assets {#ai-system-airc__section_esp_xnt_bgc} 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. {#ai-system-airc__ul_cjc_b4t_bgc} **Related tasks** * [Request an AI use case](https://www.servicenow.com/docs/dI_H6r2~rn_yulgxVL_o5Q "Request the development or procurement of an AI system to initiate and document the need for an AI-driven solution within an organization.") * [Request an AI model](https://www.servicenow.com/docs/eBfvEdelunsBF1HwHHvH5w "Request the development or procurement of an AI model to support an AI system and initiate governance review, risk assessment, and life cycle tracking.") * [Request a dataset](https://www.servicenow.com/docs/dUzg~TXYmEfIAmPX2H9dKQ "Request a dataset to support AI model training, testing, validation, or analytical use cases. Datasets are reviewed for quality, privacy, and compliance.") *[\>]: and then