--- sourceDocument: Zurich Governance, Risk, and Compliance sourceDocumentLink: https://www.servicenow.com/docs/r/zurich/governance-risk-compliance Release : - zurich ft:locale : - en-US ft:publication_title : - Zurich Governance, Risk, and Compliance ft:clusterId : - grc bundleId : - grc workflow : - Technology --- # Intake requests # Intake requests {#ariaid-title1} * Release version: Zurich * * Updated March 12, 2026 * * ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 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 Intake requests Intake requests serve as the initial step in managing and governing AI initiatives within an organization. They collect crucial information about proposed AI systems, models, and datasets to enable early review, triage, and evaluation in the AI life cycle. These requests help organizations understand the AI's purpose, usage, and associated risk or compliance considerations, supporting the creation of AI asset records that are tracked and assessed through AI governance workflows. Show full answer Show less ## Key Features * **Request an AI use case:** Proposes new or modified AI systems focusing on the overall business problem, scope, and intended outcomes. When configured, intake supports risk-based classification at submission, providing an early screening based on how the AI is intended to be used. * **Risk-based classification:** Enabled by configuration and appropriate user roles, this feature assigns an initial risk rating (e.g., Low, Medium, High, Unacceptable) derived from answers in the Use and Purpose section. It helps capture AI risk context early and guides governance workflows but does not approve or govern AI assets. * **Request an AI model:** Focuses on proposing specific AI models, capturing details like intended use, versioning, and risk considerations for review. * **Request a dataset:** Facilitates requests for dataset access needed for analysis, training, or evaluation, ensuring data usage is reviewed before AI activities. * **Automated risk classification:** Performed post-intake by AI stewards, re-evaluating classifications as AI systems evolve, and maintaining an audit trail of changes. ## Practical Application Example For instance, a corporate IT team might submit an intake request for an Email Assistant AI Agent designed to assist employees with drafting and summarizing emails to improve productivity. The intake captures key information such as the business purpose, intended users, data types processed, automation level, and human oversight. Based on this, an initial risk classification is assigned, which informs subsequent governance actions like additional assessments or legal reviews before deployment. ## Key Outcomes * Structured capture of AI proposals ensures all relevant risk and compliance factors are considered early. * Initial risk-based classification helps prioritize governance attention and resources effectively. * Creation of AI asset records enables ongoing tracking and management through the AI governance life cycle. * Supports regulatory compliance and risk mitigation by formalizing AI initiative intake and review processes. Intake requests are the entry point for managing and governing AI initiatives. They capture essential information about proposed AI systems, models, and datasets so that requests can be reviewed, triaged, and evaluated early in the AI life cycle. ## Intake request overview Intake requests are used to capture AI-related needs and concerns in a structured way. They help organizations understand what AI is being proposed, how it's intended to be used, and what risk or compliance considerations may apply. Submitting an intake request supports the creation of AI asset records that can be tracked in the AI Control Tower and evaluated using AI Risk and Compliance workflows as part of the AI governance life cycle. Intake can support early screening and initial risk-based classification, but doesn't automatically approve or govern an AI asset. ## Request an AI use case Use this intake request to propose a new or changed AI system that addresses a business problem. AI use case requests focus on the overall purpose, scope, and intended outcome of the AI capability. After upgrading to version 22.0.3, if you have the AI risk and compliance user \[sn_grc_ai_gov.ai_risk_and_compliance_business_user\] role, you can support configurable risk‑based classification of AI systems at intake where this capability is enabled. Note: Risk‑based classification at intake occurs only when all required configuration steps are completed by a teammate with the AI Risk and Compliance Admin \[sn_grc_ai_gov.ai_risk_and_compliance_admin\] role. * Set the Migrate to Advanced Risk Assessments property to publish Risk Assessment Methodologies (RAMs). For more information, see [Set up Advanced Risk assessments properties](https://www.servicenow.com/docs/xvkY8m3PD4HhMDcmFrLsBA "Enable Advanced Risk Assessments (ARA) to confirm that risk‑based assessments and risk score roll‑up function correctly in the AI Risk and Compliance application."). * Publish assessment templates. For more information, see [Publish the assessment templates](https://www.servicenow.com/docs/55AatMu2Isbv8Feix7uAKg "When creating a new AI system or updating an existing one, publish the assessment templates before initiating the required assessments such as impact assessment and conformity assessment. You must publish an assessment template so that the assessments can be initiated."). * Configure the `sn_grc_ai_gov.ai_system_automated_risk_classification_asmt_ram` to specify a default Risk Assessment Methodology (RAM) used for automated regulatory risk classification of AI systems at intake. For more information, see [Set up AI Risk and Compliance properties](https://www.servicenow.com/docs/1MSrUA212ZBbJ3UdaFtKLg "Configure AI Risk and Compliance properties to specify which authority documents and policies you want to display on the home page. You can also specify a default automated risk classification assessment RAM for AI systems and specify a default RAM to be used for risk assessments of AI systems."). * Configure the `sn_grc_ai_gov.aisystem_primary_ram` property to specify a default Risk Assessment Methodology (RAM) for AI systems. For more information, see [Set up AI Risk and Compliance properties](https://www.servicenow.com/docs/1MSrUA212ZBbJ3UdaFtKLg "Configure AI Risk and Compliance properties to specify which authority documents and policies you want to display on the home page. You can also specify a default automated risk classification assessment RAM for AI systems and specify a default RAM to be used for risk assessments of AI systems."). {#airc-intake__ul_q5m_55p_33c}If these prerequisites aren't met, intake submissions are accepted but risk classification isn't performed and the risk classification defaults to To Be Determined. After the AI asset is managed by an AI Steward, the Automated Risk Classification is performed to pre-classify as High/ Medium/ Low/Unacceptable. Risk-based classification at intake provides an initial screening of AI systems based on how they're intended to be used. This early classification helps organizations capture AI risk context at the start of the life cycle and determine the level of governance attention required. Classification is derived from responses to screening questions in the Use and purpose section of the intake form and is evaluated using a configured Risk Assessment Methodology (RAM). The resulting classification (for example, Low, Medium, High, Unacceptable, or To Be Determined) is recorded on the AI system and used to guide downstream governance workflows. Risk classification is re-evaluated automatically when Use and Purpose responses are updated. Changes to factors such as data sensitivity, system autonomy, or level of human involvement can result in an updated classification, which is captured in the activity history for audit purposes. The Use and Purpose section is automatically enabled when risk-based classification at intake is configured. This section is delivered as part of the product and doesn't require manual form customization or a separate enablement setting. Submitting an AI use case request can result in an AI system record being created through configured intake and onboarding workflows. Once created, the AI system can be reviewed, risk-classified, and assessed as part of the broader AI governance life cycle. Note: Risk-based classification at intake provides early screening only. Risk Rating is evaluated for those AI system requests explicitly Managed by an AI steward \[sn_ai_governance.ai_steward\]. It doesn't approve deployment, initiate life-cycle workflows, or replace impact assessments, detailed risk assessments, or control evaluations. For more information, see [Request an AI use case](https://www.servicenow.com/docs/sDL7CTidIRzZ2lmw_HSWUA "Request the development or procurement of an AI system to initiate and document the need for an AI‑driven solution within an organization. This process helps ensure that AI implementations are aligned with business goals, compliant with regulations, and technically feasible."), [Request an AI use case form](https://www.servicenow.com/docs/SddmQz9szQy2_mu0tG3YrA "The Request an AI use case form is designed to streamline the request process for developing or procuring an AI-based solution. This intake form confirms that all necessary details, supporting documents, and compliance considerations are captured before moving forward with development and deployment of the AI-solution."), and [AI systems](https://www.servicenow.com/docs/tQU7IhM4Cm90u4QEsmJTWg "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."). ## Request an AI model Use this intake request when the primary focus is on an AI model rather than a complete AI system. This intake request includes proposing the development, procurement, or onboarding of a specific model. AI model requests capture model-specific details needed for review, such as intended usage, versioning, and risk considerations. For more information, see [Request an AI model](https://www.servicenow.com/docs/bvztLL2kvL2kvm2iHFn2mA "Request the development or procurement of an AI model to support an AI system. This process captures the technical and business context needed to initiate governance review, risk assessment, and life cycle tracking for the model."), [Request an AI model form](https://www.servicenow.com/docs/9Nb8hjeXdUqxB3ZalPXqNg "The Request an AI model form is designed to streamline the request process for developing or procuring an AI model. This intake form confirms that all necessary details, supporting documents, and compliance considerations are captured before moving forward with the approval process."), and [AI models](https://www.servicenow.com/docs/87~L_GY2UfNDTRJe6e_~Pg "An AI model is designed, deployed, and monitored in accordance with structured governance frameworks. These frameworks ensure the AI model’s ethical use, regulatory compliance, and risk mitigation throughout its life cycle."). ## Request a dataset Use this intake request to request access to a dataset for purposes such as analysis, model training, or evaluation. Dataset requests help ensure that data usage is reviewed before it's used in AI-related activities. For more information, see [Request a dataset](https://www.servicenow.com/docs/1uAgctBZRDa3KD4Qyiq14w "Request a dataset to support AI model training, testing, validation, or analytical use cases. This process helps ensure data sources are reviewed for quality, privacy, and compliance."), [Request a dataset form](https://www.servicenow.com/docs/Hr9jxxKV1~rFWTNVvgYP6A "The Request a dataset form is designed to streamline the request process for procuring a dataset. This intake form confirms that all necessary details, supporting documents, and compliance considerations are captured before moving forward with the approval process."), and [Datasets](https://www.servicenow.com/docs/_1E8bOryJmSLoW4TZbaKqQ "A dataset is a curated collection of structured data used to develop, deploy, and monitor AI systems in line with organizational policies, regulations, and ethical standards."). ## Intake request example: Email Assistant AI Agent for corporate communications This example explains how an organization might use an intake request to propose and evaluate an AI use case. A corporate IT team proposes an Email Assistant AI Agent to support internal business users with drafting, summarizing, and responding to work‑related emails. The AI agent is intended to improve productivity by generating suggested email responses, summarizing long email threads, and highlighting required actions for employees. To initiate governance review, a team member, such as AI Product Owner, Use Case Submitter or Product Innovator from the Corporate Communication Business Unit submits a Request an AI use case intake request through the Employee Center. The intake captures key information, including: * The business purpose of the AI agent, such as improving response time and reducing manual effort in corporate communications. * The intended users, for example, employees using corporate email systems. * The types of data processed, including internal emails that may contain business‑sensitive or confidential information. * The level of automation, such as whether the AI generates suggestions only or can send emails automatically. * The role of human oversight, including employee review and approval of AI‑generated content before sending. Based on responses in the Use and purpose section, the intake workflow performs an initial risk‑based classification. Factors such as data sensitivity, exposure to personal or confidential information, and the degree of human involvement contribute to the preliminary classification. The resulting classification is recorded on the AI system and used to determine next governance steps, such as whether additional risk assessments, legal review, or security evaluations are required before deployment.