AI-generated recommendations for similar control objectives

  • Release version: Zurich
  • Updated July 31, 2025
  • 3 minutes to read
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    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-generated recommendations for similar control objectives

    The AI-generated recommendations for similar control objectives feature in the Zurich release enables compliance managers and analysts to efficiently identify, deduplicate, and rationalize similar control objectives within their compliance library. By leveraging advanced AI technologies such as generative AI and Predictive Intelligence, the framework delivers actionable, contextual recommendations directly within the user interface, helping streamline compliance management processes and maintain a clean compliance library.

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    Key Features

    • Configurable Recommendations and Actions: Allows definition and customization of recommendations and follow-up actions for various record types, enabling users to act within their workflow seamlessly.
    • Intelligent Recommendations: Utilizes generative AI and machine learning models to generate relevant, predictive recommendations that improve over time.
    • Scalable Design: Supports multiple recommendations per record type and provides flexible layout customization for administrators, ensuring consistency across diverse use cases.
    • Adoption Enablement: Designed for rapid integration with upstream products, featuring a user-friendly interface that empowers decision-makers with clear, actionable insights.
    • Role-based Access: Only users assigned specific roles (snrecotemplate.rationalizationprocesswriter and sngrcsharedgenai.compliancegenaiuser) can generate recommendations, ensuring secure and appropriate use.
    • Integration: Requires configuration of Now Assist for Integrated Risk Management (IRM) to generate recommendations effectively.

    Using the Recommendations

    Once generated, recommendations appear on a dedicated Recommendations page organized into sections:

    • Recommendation Control Objectives: Displays details such as the control objective’s name, parent, description, and supplemental guidance to support informed decision-making.
    • Evaluate Affected Associations: Lists impacted items (controls, policy exceptions, issues) and associated entities (policies, citations, etc.) affected by consolidating control objectives.
    • Response Actions: Enables users to accept duplicates, dismiss recommendations, retain a control objective as primary, or create a consolidated control objective using generative AI.

    A feedback side-panel tracks user interactions with recommendations to enhance transparency and continuous improvement.

    Benefits for ServiceNow Customers

    • Gain contextual visibility into similar control objectives for better compliance decisions.
    • Streamline compliance processes by automating identification and consolidation of redundant controls.
    • Customize recommendations and workflows to align with organizational policies and needs.
    • Improve productivity by integrating actionable recommendations directly into the workflow.
    • Leverage scalable AI-driven insights adaptable across various record types and compliance scenarios.

    The recommendations framework is designed to deliver actionable, AI-driven recommendations for similar control objectives directly within the user interface. It provides rich contextual information about similar control objectives, empowering users to make well-informed decisions and take follow-up actions seamlessly.

    The Control objective deduplication and rationalization feature is designed to help compliance managers and analysts streamline their compliance processes by identifying, deduplicating, and rationalizing similar control objectives within their compliance library. This feature leverages AI to automate the identification of redundant control objectives, helping to make it easier to maintain a clean and efficient compliance library.

    Highlights of the recommendation framework

    Configurable recommendations and actions
    • Enable you to define and configure recommendations for various record types.
    • Enable setup of follow-up actions, so you can act on recommendations directly within the workflow.
    Intelligent recommendations
    • Leverage advanced AI capabilities, including generative AI and Predictive Intelligence, to display relevant recommendations.
    • Continuously improve insights and recommendations by incorporating machine learning models and predictive scoring.
    Scalable design
    • Support the display of multiple recommendations for a single record type.
    • Provide flexibility for administrators to customize the layout and structure of the recommendation panel according to business needs.
    • Adapt to a variety of record types and recommendation techniques, confirming consistency and scalability across use cases.
    Adoption enablement
    • Designed for rapid integration and adoption across upstream products.
    • Offer a user-friendly, intuitive interface that empowers decision-makers with clear, actionable insights.

    Key benefits

    • Contextual visibility into recommendations for better decision-making.
    • A scalable, configurable framework adaptable to various use cases and record types.
    • Faster adoption for products looking to leverage AI/ML-based recommendations.
    • Customizable workflows and logic to meet specific organizational processes.
    • Improved user productivity with actionable recommendations and clear next steps built directly into the interface.
    Note:
    Only users with sn_reco_template.rationalization_process_writer and sn_grc_shared_genai.compliance_gen_ai_user can see the option to generate recommendations for similar control objective. This role must be manually assigned to a compliance user.

    To generate recommendations for a control objective, configure Now Assist for Integrated Risk Management (IRM). See Configure Now Assist for Integrated Risk Management (IRM).

    Viewing recommendations

    After generating recommendations for similar control objectives, the Recommendations page displays the following sections:
    • Recommendation
    • Control objectives
    • Description
    • Response actions
    • Evaluate affected associations
    The following tables provide more information on the sections and the related lists that are associated with the recommendations.
    Table 1. Recommendations
    Field Description
    Control objectives Details of the control objective.

    For example, the name of the control objective and parent.
    Last refreshed Date and time the recommendations were last generated or refreshed.
    Note:
    For more information about control objectives, see Structural overview of Policy and Compliance Management.
    Table 2. Details of the control objective
    Field Description
    Description Description and a summary of the control objective.
    Supplemental guidance Additional guidance on how to address the control objective.
    Table 3. Evaluate affected associations
    Field Description
    Impacted Items (Controls, Policy exceptions, Issues, and more) Related lists containing items directly affected by the consolidation of new control objectives.
    Associated Items (Entities, Entity type, policies, citations, control objectives and more Related lists containing all associations from accepted control objectives in a consolidated view.
    Table 4. Response Actions
    Action Description
    Accept as duplicate Acceptance of a recommendation as a duplicate by selecting Accept as duplicate and then selecting Confirm.
    Dismiss Removal of a recommendation from future suggestions by selecting Dismiss and then selecting Confirm.
    Retain as primary Retention of the control objective as the main one by selecting Retain as primary and then selecting Confirm.
    Create common control objective Generation of a consolidated control objective using generative AI after accepting duplicates by selecting Create.

    Feedback trail side-panel: The feedback side-panel displays the history of user interactions with recommended items. This can include what the user accepted, what they skipped or ignored, and what they dismissed.

    For more information on generating recommendations, see Use Recommendation of similar control objectives skill to generate suggestions.