Zurich |
- New in AI Control Tower in Zurich Patch 11:
- Activity Center
- Track and act on the governance work generated across AI Control Tower from a single workspace, including lifecycle tasks, security tasks, change and offboarding requests, and AI recommendations.
- Recommendations and AI insights
- Recommendations and AI insights direct your attention to the AI governance work that matters most, so you can resolve high-impact issues without searching for them. Act on recommendations from the Home page, an asset record,
or Activity Center.
- Monitor quality and safety for AI systems
- Evaluate the quality and safety of AI interactions across your portfolio using automated scoring, configurable metrics, and trend analysis for both ServiceNow and external AI systems.
- Trace connections
- Collect trace data for discovery, security, and monitoring from hyperscalers including AWS, Azure, and Google Cloud by configuring trace connections.
- Plan AI strategy, prioritize, and execute
- Track your AI portfolio from strategy to delivery with the Plan menu. Plan connects goal alignment, intake management, and execution tracking in a single workspace, giving portfolio managers and AI COE leads a current view
of AI investments.
- ServiceNow Otto in AI Governance
- Use ServiceNow Otto premium chat in AI Control Tower for a better conversational experience with unified search and chat capabilities, including integrated web search and file uploads.
- AI agent containment using kill switch protocol
- Deactivate and reinstate AI agents running in AWS Bedrock, AWS Bedrock AgentCore, GCP Vertex AI (limited support), and ServiceNow agents. Revoke AI agent session tokens through Okta.
- Discover your agent network with the map
- See AI models, MCP servers, and providers in the agent map for complete resource visibility across your enterprise, along with agents, agentic workflows, and other AI assets.
- Configure post-runtime security metrics
- System prompt leakage, threat monitoring, and sensitive data disclosure post-runtime metrics are now configured and active by default.
- Specify the asset state during AI asset creation
- Specify the asset state when you create AI assets manually. By specifying the state during initial asset creation, you can track and manage your assets more accurately throughout their life cycles. You can specify the asset
state in the Asset state field of the following forms:
- Add AI system asset
- Add AI model asset
- Add prompt asset
- Add dataset asset
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Review AI risk and compliance posture
- See regulatory risk classification, compliance posture, and aggregated risk posture for AI assets across your portfolio from the Govern tab. Review related cases, issues, and governance actions
associated with those assets.
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Track regulatory risk classification and compliance score
- See how AI systems, models, and datasets are categorized by regulatory risk, and track compliance scores against the priority frameworks configured in your environment.
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Review inherent and residual risk posture
- Compare inherent risk, residual risk, and control effectiveness for AI systems using the risk heat map, and identify concentrations of higher-risk assets across your portfolio.
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View governance records for AI assets
- Review assessments, risks, controls, attestations, issues, and policy exceptions for an AI asset directly from its Risk & Compliance tab, without leaving the asset record.
- AI Service Graph Connector for Anthropic connector
- Discover and import Anthropic AI Models and track usage data (per-user AI asset cost).
- AWS
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- Admins can now configure all AWS AI discovery services using a single credential page.
- Admins can enable automatic rotation of AWS access keys for AI SGC connections.
- Admins can discover multiple explicit AWS accounts by specifying a comma-separated list of account IDs.
- Microsoft
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- Unified single-page connection for Azure ML and AI services.
- Resource group discovery and storage for Azure Foundry assets.
- Certificate-based authentication for Azure and Copilot.
- Knowledge Base integration in configuration review.
- New in AI Control Tower (legacy) in 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
- 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
- New in AI Control Tower (legacy) in 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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View metrics at the MCP server level, including 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
- Manage deployment of AI assets using the change request workflow to update AI assets that have undergone review and onboarding. Facilitate the retirement of AI assets by submitting an offboarding request, ensuring a
structured and controlled process for removing assets that aren't needed or have been superseded.
- New in AI Control Tower (legacy) in 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.
- New in AI Control Tower (legacy) in 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 to apply consistent settings across AI assets and track usage 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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