--- sourceDocument: Brazil Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # AI governance # AI governance on the ServiceNow AI Platform {#ariaid-title1} Release version: Brazil Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 4 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 governance on the ServiceNow AI Platform As AI adoption grows, the ServiceNow AI Platform provides a comprehensive governance framework to ensure responsible, compliant, and effective AI use aligned with enterprise objectives. This framework encompasses policy considerations, defined stakeholder roles, governance processes, and specialized tools designed to manage AI throughout its lifecycle securely and transparently. Show full answer Show less ## AI Policy Considerations * **Data Security and Privacy:** Enforces strict data classification, encryption, residency, retention, and deletion policies. ServiceNow Otto for Data Privacy enables masking of sensitive data fields and controls data shared with third-party AI models. * **Compliance and Regulations:** Ensures AI deployments comply with regulations like HIPAA, PCI DSS, GDPR, CCPA, and FedRAMP. Includes third-party risk management, logging, traceability, and legal review processes. * **Responsible AI Use:** Governs model approvals, bias mitigation, human oversight, and transparency obligations, such as disclosing use of third-party AI models. * **Governance and Change Management:** Establishes enterprise guardrails, change control, rollout processes, and incident response plans to manage AI deployment safely. ## AI Policy Stakeholders * **Policy Setters:** CIO/CTO align AI strategy with enterprise goals; CISO defines security standards; CDO oversees ethical data governance; Privacy Officer and legal teams ensure regulatory compliance. * **Internal Governance and Oversight:** AI Governance Committee and Data Governance Council establish guardrails and approve AI use cases. AI Stewards enforce responsible AI use, monitor risks like bias, and ensure regulatory adherence. * **Implementation and Operations:** Now Assist admins configure AI capabilities; platform owners and ServiceNow admins manage deployments in compliance with governance policies; AI developers build and integrate AI features aligned with standards. ## AI Governance Tools * **AI Control Tower:** Central hub for AI strategy, governance, and analytics providing enterprise-wide visibility, automated asset inventory, compliance alerts, and governance checks. * **AI Admin Hub:** Enables policy configuration, data handling enforcement, and compliance monitoring at the skill level. Facilitates collaboration between admins, AI stewards, and business stakeholders for policy execution. ## Practical Benefits for ServiceNow Customers By leveraging the ServiceNow AI Platform's governance framework, customers can confidently deploy AI solutions that meet stringent data security, privacy, and compliance requirements while maintaining ethical standards. Defined roles and specialized tools help streamline AI management, minimize risks such as bias or data exposure, and support organizational accountability and transparency throughout AI lifecycle management. As organizations increasingly adopt AI to drive efficiency, innovation, and customer experience, AI governance becomes essential to ensure responsible use, regulatory compliance, and alignment with enterprise goals. The ServiceNow AI Platform provides a comprehensive governance framework through key roles and applications that work together to manage AI across its life cycle. ## AI policy considerations {#sn-ai-impl-governance__section_w2c_nny_1hc} The following policy considerations shape how AI is deployed, monitored, and maintained across the enterprise. Data security and privacy : AI systems must comply with strict data handling protocols to protect sensitive information. This includes the following: * Data classification rules for personally identifiable information, protected health information, and financial data. * Encryption standards for data in transit and at rest. * Data residency and sovereignty restrictions, which determine where data can be stored and processed. * Retention and deletion policies that govern how long data is kept and when it must be purged. {#sn-ai-impl-governance__ul_hmk_tny_1hc} Admins can configure ServiceNow Otto for Data Privacy to mask sensitive fields and control what is shared with third-party models. For details, see [Data Privacy for ServiceNow Otto](https://www.servicenow.com/docs/access?context=now-assist-for-data-privacy-landing&version=brazil&pubname=brazil-platform-security&ft:locale=en-US). Compliance and regulations : AI deployments must adhere to a range of regulatory frameworks, including: * HIPAA, PCI DSS, GDPR, CCPA, and FedRAMP, depending on the industry and geography. * Third-party/vendor risk management, especially when external models or services are used. * Logging and traceability requirements help ensure accountability in AI decisions. {#sn-ai-impl-governance__ul_qm2_k4y_1hc} Legal reviews are often required before publishing documentation or releasing features, particularly when consolidating overlapping control objectives or addressing model transparency. Responsible AI use : To ensure ethical and effective AI, organizations should enforce the following: * Model approval and usage guidelines, including naming conventions and branding policies for AI agents. * Bias and fairness safeguards, with AI Stewards evaluating risks like hallucination or exposure of sensitive data. * Human oversight requirements, ensuring AI augments rather than replaces human judgment. * Transparency obligations, such as disclosing the use of third-party models like Azure OpenAI in product documentation. {#sn-ai-impl-governance__ul_lry_3wy_1hc} Governance and change management : AI governance is supported by structured oversight and change control processes: * Definition, review, and approval of enterprise-wide guardrails and new use cases. * Change control and rollout processes ensure that AI features are deployed safely and predictably. * Incident response and escalation plans are in place to address issues such as data breaches or model failures. {#sn-ai-impl-governance__ul_yjh_jxy_1hc} ## AI policy stakeholders {#sn-ai-impl-governance__section_chj_f3y_1hc} The following groups set and execute AI policy in an organization: Policy setters : The Chief Information Officer (CIO) or Chief Technology Officer (CTO) sets the overall technology strategy, ensuring AI initiatives align with enterprise architecture and innovation goals. The Chief Information Security Officer (CISO) establishes data security and encryption standards to safeguard sensitive information across AI workflows. The Chief Data Officer (CDO) oversees data usage and governance, ensuring that AI systems handle data ethically and in accordance with organizational policies. Meanwhile, the Chief Privacy Officer and legal teams are responsible for regulatory compliance, ensuring that AI deployments meet requirements such as GDPR, HIPAA, and other jurisdictional or industry-specific standards. Together, these leaders form the foundation of AI governance, guiding implementation teams and administrators in deploying AI responsibly and securely. Internal governance and oversight : Governance and oversight of AI is led by structured groups that define and enforce responsible use. An AI Governance Committee and Data Governance Council set enterprise-wide guardrails for AI, including standards for privacy, fairness, and compliance, and are responsible for reviewing and approving new AI use cases. Supporting these bodies, the AI Steward ensures that AI is used responsibly across workflows, overseeing data quality, managing risks such as bias or data exposure, and monitoring adherence to regulatory requirements. Additionally, AI Stewards monitor regulatory compliance, assess performance and user feedback, and work with admins and developers to optimize AI automation while minimizing risk. Implementation and operations : Implementation and operations teams are responsible for securely deploying and managing AI features in alignment with governance policies. The Now Assist admin configures and manages AI capabilities, ensuring that AI features are properly mapped to workflows and governed according to enterprise standards. Platform owners and ServiceNow admins oversee the deployment process, making sure that all configurations comply with established policies and technical requirements. Meanwhile, AI developers build, extend, and integrate AI features into business workflows, working closely with admins and platform teams to deliver scalable, compliant, and effective AI solutions. Together, these roles translate governance policies into secure, functional AI implementations. For more information about AI governance user roles, see [AI Risk and Compliance roles](https://www.servicenow.com/docs/C73f1rVLRVb8EHEa0pwLZQ "The AI Risk and Compliance application installs the essential role to perform respective day-to-day operational tasks for managing AI systems across the enterprise.") and [Assign the data steward role](https://www.servicenow.com/docs/bEcrQCPO29XoR2hr7ccBDQ "Select a data steward to make decisions about data sharing with ServiceNow in Now Assist applications."). For more resources about AI governance, see the following best practices topics: * [AI Governance White Paper](https://mynow.servicenow.com/now/best-practices/assets/ai-governance-white-paper) * [Technical Governance Foundation Template](https://mynow.servicenow.com/now/best-practices/assets/technical-governance-foundation-template) * [Governance in operational phase: Roles and responsibilities](https://mynow.servicenow.com/now/best-practices/assets/governance-in-operational-phase-roles-and-responsibilities) {#sn-ai-impl-governance__ul_wwz_znv_bhc} ## AI governance tools {#sn-ai-impl-governance__section_r51_ghy_1hc} AI governance is specified in the following tools: AI Control Tower : The AI Control Tower functions as the central hub for AI strategy, governance, and analytics. It offers enterprise-wide visibility into AI assets, usage patterns, and compliance status, enabling organizations to maintain oversight and accountability. Through automated discovery and inventory of approved AI assets, it streamlines asset management while embedding governance checks and compliance alerts to ensure that all AI deployments remain secure and aligned with organizational policies. : For more information, see [AI Control Tower](https://www.servicenow.com/docs/GtzcVtTj4bnKFuufQ7ld1A "Gain visibility into your organization's AI footprint, manage the lifecycle of AI investments, govern risk and compliance, and measure the business impact of AI with AI Control Tower."). AI Admin Hub : The AI Admin Hub is key to managing AI governance by configuring policies, enforcing data handling rules, and ensuring compliance with security and privacy standards. Admins oversee provider policies at the skill level, track usage analytics like success rates and adoption, and collaborate with AI stewards and business SMEs to align AI with organizational goals. They also connect governance committees with technical teams to support smooth policy execution. : For more information, see [Overview tab in AI Admin Hub](https://www.servicenow.com/docs/6Ih6Rt3WuqdbYrEqsg26oQ "The AI Admin Hub console provides quick and effortless access to the important information that you need to set up, configure, and monitor ServiceNow Otto applications and features.").