Xanadu |
No updates for this release. |
Yokohama |
- AI model providers
- Explore AI model providers
- Enable choice for third party model providers powering ServiceNow® skills and agents.
- Yokohama Patch 3
- AI Governance
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- A single pane view of the AI inventory, its state, and its risk and compliance posture.
- Lifecycle to manage AI asset onboarding and deployment.
- Helps user oversee and manage AI Asset inventory's risk profile with regard to enterprise policies and global regulations, as defined by the user, with a focus on privacy, data governance, and ethical AI.
- AI Case management to oversee AI asset-related inquiries and cases, enabling faster response and improved tracking.
- Multi-instance management to synchronize AI asset inventory from sub-prod to prod instances to initiate governance early in the build process.
- Control settings to block only ''other'' skills in Now Assist AI deployment pending approvals.
- AI Governance
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- AI Steward role- Facilitate and coordinate governance activities between innovation, legal, security, risk and compliance teams.
- AI Asset inventory- Unified data model on the ServiceNow AI Platform to catalog AI Model, datasets, prompts, and other related artifacts including Now Assist and AI leveraging Generative AI Controller.
- AI skills Approvals- Review and approval flows for Now Assist skills and other related assets like AI Models and AI datasets deployed through Now Assist or generative AI Controller.
- AI Control Tower Workspace- Intuitive workspace to surface governance tasks, reports, inventory, and insights.
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Zurich |
- Zurich Patch 7
- Security and privacy tab
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- Measure whether your model's output or behavior potentially violates predefined LLM guardrail policies using the Data integrity incident detection chart.
- Review potential threats in AI agent output in Agent goal deviation, Output with PII detected, and Agentic output injection detection charts.
- Monitor MCP server access by AI Gateway with these new charts: Clients connecting to MCP servers, authorized access attempts, and failed access attempts.
- Data section on Configurations page
- Enable and set up data integrity incident detection, agent goal deviation, and output screening metrics to measure the integrity of your data model and potential threats in LLM output.
- Manage agentic AI system life cycles
- Create AI system assets to track and manage the complete life cycles of your agentic AI systems, from onboarding to deployment. Gain comprehensive insight into each agentic AI system and take any necessary actions to
successfully complete each life-cycle stage. By managing the life cycles of your agentic AI systems, you can extend their lifespans, reduce downtime, and optimize licensing costs.
- Define the use and purpose of an AI system
- Specify the intended use and purpose of an AI system. Provide insight into who is using the AI system, what the AI system is being used for, and how the AI system works and provides value. This information can help you
determine the benefits and risks that are associated with the AI system. For more information on classifying AI systems based on regulatory risk at intake by applying a configured Risk Assessment Methodology (RAM), see Assessment templates and risk assessment methodologies and Request an AI use case.
- Associate additional related AI asset types with AI systems
- Associate the following additional related AI asset types with your AI systems:
- If an AI system has an Asset type of Generative AI or Agentic AI, you can associate it with any of its supported components or subsystems.
- If an AI system has an Asset type of Agentic AI, you can associate it with any of its integrated AI tools.
- Create change and offboarding requests for additional AI asset types
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Create change requests for the following additional AI asset types:
- AI systems with an Asset type of Agentic AI
- Datasets
In addition, create offboarding requests for the following additional AI asset types:
- AI systems with an Asset type of Agentic AI
- AI models
- Datasets
- MCP servers
- Zurich Patch 4
- Security and privacy tab
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- Identify ServiceNow® AI assets that impact your security posture using the ServiceNow® AI security score and AI insights. AI insights highlight key metric changes, recommend next steps, enabling you to quickly understand the impacts and take action.
- Access and monitor security for AWS Bedrock agents running as privileged users, autonomous vs. supervised tools, and dormant agents.
- Monitor sensitive data detection, prompt injection, and offensive content metrics to help identify and mitigate AI-driven security and compliance risks before they impact workflows or expose sensitive information. AI
security tasks are created automatically from Dormant AI systems metrics to streamline your workflow and quickly resolve issues. You can also create AI security tasks directly from more areas, such as access issues and
privileged AI agents metrics.
- Review Autonomous vs. supervised systems metrics based on AI tools. Previously, the metrics were based on workflows.
- Show Access issues metrics for only those agents with issues. Previously, agents with issues and no issues were shown.
- See more details about agent access issues in the access map to help you troubleshoot quickly. For example, you can see the user ID of the user who executed the agent and the workflow and tool associated with the access
issue, if applicable.
- AI Gateway tab
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The AI Gateway tab shows the metrics at the MCP server level, listing all connected MCP servers along with the total number of transactions for each server and its success rate.
- Data section on Configurations page
- See a read-only view of your data privacy configuration for sensitive data patterns in the Data privacy page. Use this page as a quick reference when troubleshooting sensitive data charts.
- AI Task section on AI assets page
- Review all AI security tasks for your instance in the All Security Tasks page. You can also create an AI task on this page.
- Enhance control of AI asset life cycle through change and offboarding requests
- Enhance the management of deployed AI assets by using the change request workflow to make necessary edits to AI assets that have already undergone review and onboarding. Furthermore, facilitate the retirement of AI assets by
submitting an offboarding request, ensuring a structured and controlled process for removing assets that are no longer needed or have been superseded.
- Zurich Patch 1
- Health tab in AI Control Tower
- Monitor and evaluate the effectiveness of offensive content and prompt injection guardrails active on your AI assets.
- Zurich Early Availability
- AI strategy with Strategic Planning(Requires SPM)
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- Monitor and track your AI strategies and associated goals and targets.
- Track the costs of your AI projects, epics, and demands.
- Monitor key project risks, issues, decisions, actions, and changes.
- AI strategy with Goal Framework (Requires SPM)
- Monitor and track your AI strategies and associated goals and targets.
- AI connections
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- Discover and add AI agents and related models and tools to the AI inventory through integration with AWS Bedrock.
- Discover and add AI agents and related models and tools to the AI inventory through integration with Azure AI Foundry.
- Configure AI discovery setup and visibility of connections.
- Value tab
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- Gain insights into the value realized from AI skills and features. The Value insights dashboard page gives you insights into the estimated productivity gains as a result of using AI systems.
- Define and measure value relevant to your AI systems using customizable value templates.
- Perform calculations and approximations for the read and write time saved by users using AI systems by using data points and timestamps from the invoking records.
- Understand the key usage and performance indicators that help you evaluate the adoption of Now Assist in your organization.
- Provide insights on success rate visualization by department, country, and AI assets along with the indicators for task closure efficiency.
- Value templates
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- Create, manage, and use templates from a Global Template Repository across multiple AI assets, so that you can standardize and streamline your AI experience, and track usage of AI systems, use
cases, and skills more effectively.
- Enable users to edit, view, and create customized value templates by enabling a value template assignment experience in inventory records.
- Provide transparency to value calculations of each AI system through value templates.
- Perform calculations and approximations for the read and write time saved by users using AI systems by using data point and timestamps from the invoking records.
- Risk and compliance tab
- Display the risk classification of AI assets and the compliance posture for selected authority documents and policies through the Risk and compliance tab. This tab provides visibility into AI systems,
models, and datasets. The Risk overview section uses visual charts to categorize AI assets.
- Review adherence to frameworks in the Compliance overview section. For example, adherence to the NIST AI Risk Management Framework or the EU Artificial Intelligence Act, presenting scores based on citations and control
attestations that are set by the customer. You can filter data by authority documents or policies, view overall compliance percentages, and identify critical issues and AI cases tied to items deemed non-compliant by the
customer.
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- Build trust in the AI asset inventory to effectively manage AI-related risks across the enterprise.
- Drive enterprise-wide risk visibility by aggregating individual scores into a consolidated AI risk profile to support informed mitigation decisions.
- Display real-time residual risk scores on the home page to help practitioners identify high-risk AI assets and prioritize mitigation actions.
- Use enhanced impact assessment templates to help manage and oversee compliance with regulatory requirements.
- Perform bulk control attestations using Core UI to validate multiple controls across AI assets, improving efficiency for large-scale assessments.
- Adopt a proactive approach by leveraging comprehensive AI risk and compliance scoring across the entire AI asset inventory.
- AI cases tab
- Gain a centralized overview of all your AI asset cases and inquiries by using the AI cases. On the AI cases tab, you see a list of records that include the case details such as the status, priority,
owner, and timeline of your AI cases. You can monitor the progression of a case, stay informed about ongoing investigations, follow up on pending actions, and help to ensure timely resolutions. On this tab, you can also find
filtering and sorting options that help you to prioritize cases that require immediate attention.
- Use the enhanced home page to access a single, unified view of all AI-related cases and inquiries.
- Track, manage, and respond to AI-related cases more efficiently through centralized case visibility.
- Security and privacy tab
- Review your AI security health metrics in the Security and Privacy tab. Use the access map for a comprehensive overview of agentic workflows, tools, and agent details. The map helps to show how AI
agents interface with workflows and tools to accomplish a task. Additionally, you can analyze current AI access, investigate ongoing access issues, and review your AI usage metrics.
- Control which third-party models OEM by ServiceNow are enabled for Now Assist AI implementation and how they’re used.
- Specify additional details during AI asset creation
- Use the following fields to specify additional details about your AI systems, AI models, prompts, and datasets when you create AI assets:
- AI systems:
- Managed by
- License details
- Supported locations
- AI models:
- Managed by
- License details
- Supported locations
- Prompts: Managed by
- Datasets:
- Managed by
- Creation type
- Department
- Dataset creation date
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Australia |
- Australia Patch 2
- Publish ServiceNow agents to Microsoft Agent 365
- Publish the ServiceNow Agents to Microsoft Agent 365 ensuring the ServiceNow agents are sent to external registries.
-
- Service Graph Connectors for AI Control Tower
- AI Service Graph Connector for Databricks discover AI agents and import to AI Control Tower from Databricks
environment.
- Australia Patch 1
- Security & privacy tab in AI Governance
-
- Customize the AI asset security score by weighting
LLM guardrail categories that comprise
the score. The score formula was changed to an average across all AI assets. The score was renamed to the AI asset security score.
- Measure whether your model's output or behavior potentially violates predefined
LLM guardrail policies using the
Data integrity incident
detection chart.
- Review potential threats in AI agent output in
Agent goal deviation, output
with PII detected, and Agentic output injection
detection charts.
- Monitor MCP server access by AI Gateway with these new charts: Clients connecting to MCP servers, authorized access attempts, and failed access attempts.
- The Prompt injection, Offensive content, and Sensitive data tabs have been removed and replaced by the Access and Guardrails tabs. Metrics have been reorganized into those
two tabs.
- In Configurations, under Data, the Data privacy tab was renamed to Security & privacy. In that tab, the data leak detection
and anonymization section was renamed to sensitive data input and anonymization.
- Data section on Configurations page
- Enable and set up data integrity incident detection, agent goal deviation, and output screening metrics. These metrics measure the integrity of your data model and potential threats in LLM output.
- Manage agentic AI system life cycles
- Create AI system assets to track and manage the complete life cycles of your agentic AI systems, from onboarding to deployment. Gain comprehensive insight into each agentic AI system and take any necessary actions to
successfully complete each life-cycle stage. By managing the life cycles of your agentic AI systems, you can extend their lifespans, reduce downtime, and optimize licensing costs.
- Define the use and purpose of an AI system
- Specify the intended use and purpose of an AI system. Provide insight into who is using the AI system, what the AI system is being used for, and how the AI system works and provides value. This information can help you
determine the benefits and risks that are associated with the AI system. For more information on classifying AI systems based on regulatory risk at intake by applying a configured Risk Assessment Methodology (RAM), see, AI Risk and Compliance release notes
Assessment templatesand Risk assessment methodologies.
- Associate additional related AI asset types with AI systems
- Associate the following additional related AI asset types with your AI systems:
- If an AI system has an Asset type of Generative AI or Agentic AI, you can associate it with any of its supported components or subsystems.
- If an AI system has an Asset type of Agentic AI, you can associate it with any of its integrated AI tools.
- Create change and offboarding requests for additional AI asset types
-
Create change requests for the following additional AI asset types:
- AI systems with an Asset type of Agentic AI
- Datasets
In addition, create offboarding requests for the following additional AI asset types:
- AI systems with an Asset type of Agentic AI
- AI models
- Datasets
- MCP servers
- ServiceNow product tiers
- The ServiceNow AI Platform now brings you a new AI experience with three licensing tiers available:
- Foundation: AI basics to deliver insights
- Advanced: AI to boost productivity across relevant use cases
- Prime: Act autonomously with all AI assets and create your own
Depending on your entitlements, you will have access to certain application features, generative AI skills, agentic workflows, and AI agents.
- Early availability
- AI connections
- AI connections are created using AI Service Graph Connectors. AI connections are a combination of hyperscalers, AI apps, and agentic AI frameworks.
- The following AI Service Graph Connectors are available from March 2026
- Managed and unmanaged AI assets
- Managed assets benefit from AI Control Tower features such as governance, lifecycle management, value assessment, risk classification, security, and privacy. Unmanaged assets, on the other hand, do not have access to these AI Control Tower capabilities.
- AI Gateway
- AI Gateway offers MCP Global Clients, which can be used across all servers.
- A Gateway offers MCP Catalog to choose while adding MCP servers.
- MCP server can be added to an AI Asset inventory from AI Control Tower.
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