--- sourceDocument: Australia Governance, Risk, and Compliance sourceDocumentLink: https://www.servicenow.com/docs/r/australia/governance-risk-compliance Release : - australia ft:locale : - en-US ft:publication_title : - Australia Governance, Risk, and Compliance ft:clusterId : - grc bundleId : - grc workflow : - Technology --- # AI systems # AI systems {#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 AI systems An AI system is a governed, AI-powered solution developed and managed within a formal framework to ensure responsible, compliant, and risk-aware operation throughout its life cycle. It functions within an organization's digital or operational environment to support business activities by enabling automation, enhancing data-driven decisions, and improving process efficiency. Components such as machine learning models, natural language processing engines, and computer vision tools work together with datasets, algorithms, and infrastructure to analyze data, recognize patterns, and generate outcomes with minimal human intervention. Show full answer Show less Each AI system undergoes a structured evaluation process assessing risks related to functionality, security, and societal impact, as well as compliance with relevant regulations, standards, and internal policies. Governance frameworks enforce ethical principles like fairness, accountability, and transparency through controls and oversight mechanisms. Continuous monitoring helps ensure responsible AI use and mitigates unintended consequences. ## Key Features * **Aggregated risk score:** AI system records display an aggregated risk score consolidating individual risks such as bias, drift, and security. This score is visible under the Details tab in the AI system record if the Advanced Risk application is installed and the migration to Advanced Risk Assessments is enabled. * **Risk score roll-up:** Individual risk scores from related entities assessed using the Risk Assessment Methodology roll up into a consolidated AI system risk score, providing a comprehensive view of risks at departmental or enterprise levels. * **Related AI assets:** Each AI system record lists associated AI models, datasets, and related AI entities, enabling clear visibility into the components comprising the system. * **Governance and compliance:** Formal governance frameworks apply throughout the AI system life cycle to ensure security, explainability, trustworthiness, and adherence to ethical principles. ## Key Outcomes * Enhanced organizational oversight of AI risks by consolidating fragmented risk assessments across multiple models and teams. * Improved ability for AI Risk and Compliance teams to monitor and manage AI risks at scale, supporting responsible AI adoption. * Greater transparency and accountability in AI system operations through structured governance and continuous monitoring. * Support for enterprise-wide AI risk profiles that inform decision-making and risk mitigation strategies. 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/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."). 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/TXYz1JsnVUTz~sDfFgUe2w "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/A2Mjmx9Eb7YBEQ~fPKbLPw "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/IER6aZijTWa5Sn_At4LBSw "Request a dataset to support AI model training, testing, validation, or analytical use cases. Datasets are reviewed for quality, privacy, and compliance.") *[\>]: and then