Study guide
5.1 Business Terms & Copilot Customization in Customer Experience and Service
Microsoft Certified: Agentic AI Business Solutions Architect Expert AB-100
Track your progress as you read
Finished sections get a check mark.
1 Chapter 1: Requirements Analysis & Grounding Data
1.1 Assessing Agents for Task Automation, Analytics & Decision-Making 1.2 Grounding Data Evaluation: Accuracy, Relevance, Timeliness, Cleanliness & Availability 1.3 Structuring Enterprise Business Data for AI System Interoperability
2 Chapter 2: AI Strategy, Cloud Adoption Framework & CoE
2.1 Cloud Adoption Framework (CAF) for Azure AI Strategy & Roadmap 2.2 Multi-Agent Architecture: Microsoft 365 Copilot, Copilot Studio & Foundry 2.3 Microsoft AI Center of Excellence (CoE) & Solution Governance 2.4 Developing Use Cases for Prebuilt Agents & the Microsoft Agent Catalog 2.5 Solution Rules & Constraints for Copilot Studio, Microsoft Foundry & Foundry Tools
3 Chapter 3: Model Selection, Prompt Engineering & Dynamics AI
3.1 Model Selection Strategy, Custom Models & Small Language Models (SLMs) 3.2 Enterprise Prompt Engineering Guidelines & Prompt Libraries 3.3 Cross-App AI Strategy for Multi-App Dynamics 365 Solutions
4 Chapter 4: ROI, TCO, Build vs Buy & Model Routing
4.1 ROI Criteria & Total Cost of Ownership (TCO) for AI Solutions 4.2 Comprehensive Financial ROI Analysis for Business Processes 4.3 Build vs Buy vs Extend Architectural Decision Framework 4.4 Dynamic Model Router Architecture & Cost-Performance Optimization
5 Chapter 5: Dynamics 365 AI & Contact Center Design
5.1 Business Terms & Copilot Customization in Customer Experience and Service 5.2 Connectors & Data Flow Design for Copilot in Dynamics 365 Sales 5.3 Agent Integration with Dynamics 365 Contact Center Channels
6 Chapter 6: Agent Archetypes & Copilot Studio Core
6.1 Designing Task Agents, Autonomous Agents & Prompt-and-Response Agents 6.2 Copilot Studio Topic Design, Trigger Phrases & Graceful Fallbacks 6.3 Orchestration Selection: Standard NLP vs CLU vs Generative AI Orchestration
7 Chapter 7: Copilot Studio Advanced Actions & Power Platform
7.1 Designing Agent Flows & Prompt Actions in Copilot Studio 7.2 Power Apps Canvas App Integration, Generative Pages & Agent Feeds 7.3 Power Platform Well-Architected Framework for AI Workloads 7.4 Proposing Microsoft Power Platform AI Features & the AI Hub
8 Chapter 8: Agent Extensibility, MCP & Computer Use
8.1 Microsoft Foundry Custom Models & Foundry Tools Selection 8.2 Agent Extensibility via Model Context Protocol (MCP) in Copilot Studio 8.3 Computer Use Automation & Web UI Interactions in Copilot Studio
9 Chapter 9: Advanced Behaviors & Prebuilt App Orchestration
9.1 Agent Behaviors: Multi-Step Reasoning, Voice Mode & M365 Agent Optimization 9.2 Orchestrating Prebuilt AI in Dynamics 365 F&O and M365 Copilot for Sales/Service 9.3 Orchestrating Prebuilt Copilot AI Features in Dynamics 365 Customer Experience & Service 9.4 Proposing Microsoft 365 Agents for Business Scenarios 9.5 Adding Knowledge Sources to In-App Help & Guidance in Dynamics 365 Finance & Supply Chain
10 Chapter 10: Telemetry, Monitoring & Agent Tuning
10.1 Monitoring Architectures: Azure Monitor, Application Insights & Studio Analytics 10.2 Telemetry Data Interpretation, Performance Metrics & Latency Tuning 10.3 User Feedback Analysis, Backlog Prioritization & Continuous Improvement
11 Chapter 11: Testing Strategy & Model Validation
11.1 Agent Testing Frameworks, Quality Metrics & Groundedness Benchmarks 11.2 Custom AI Model Validation, Safety Criteria & Regression Testing 11.3 Copilot-Assisted Test Case Generation & Cross-Dynamics End-to-End Testing 11.4 Validating Effective Copilot Prompt Practices
12 Chapter 12: ALM for Grounding Data & Copilot Studio Agents
12.1 ALM for Grounding Data, Vector Indexes & Knowledge Synchronization 12.2 ALM for Copilot Studio Agents, Connectors & Solution Packaging 12.3 Environment Strategy & Automated Deployment Pipelines for Studio Agents
13 Chapter 13: ALM for Foundry Agents & Dynamics 365 AI
13.1 ALM for Microsoft Foundry Agents Service & Model Registries 13.2 ALM for Custom AI Models, Fine-Tuning Artifacts & Deployment Slots 13.3 ALM for Dynamics 365 AI in Finance, Supply Chain & Customer Engagement
14 Chapter 14: Agent Identity, Security & Access Controls
14.1 Agent Identity Architecture, Microsoft Entra ID & Authentication Flows 14.2 Access Controls, Role-Based Security & Permissions on Grounding Data 14.3 Secure Data Processing & Grounding Data Isolation
15 Chapter 15: Model Security & Threat Mitigation
15.1 Model Security Architecture & Defense Against Vulnerabilities 15.2 Mitigating Prompt Manipulation, Jailbreaks & Indirect Prompt Injections 15.3 Azure AI Content Safety, Prompt Shields & Guardrails
16 Chapter 16: Responsible AI, Governance, Residency & Audit
16.1 Microsoft Responsible AI Standard: Principles, Assessment & Implementation 16.2 Data Residency, Sovereign Boundaries & Cross-Border Compliance 16.3 Comprehensive Audit Trails, Model Decision Logging & Microsoft Purview 16.4 Designing Governance for Agents: Environment Strategy, Data Policies & Agent Inventory
5.1 Business Terms & Copilot Customization in Customer Experience and Service
Key Takeaways Explain This Topic AI
Defining business terms and glossary definitions in Dynamics 365 Customer Insights and Dataverse resolves semantic ambiguities (such as distinguishing operational churn from predictive churn risk or ARR from contracted revenue) and grounds Copilot responses in verified corporate taxonomy.
Customizing Copilot in Dynamics 365 Customer Service leverages Power Platform AI Prompts grounded in Dataverse metadata, timeline activities, and case resolution attributes to automate case summaries, contextual email drafting, and live conversation wrap-ups.
Grounding integrity requires segregating authoritative internal knowledge sources (Dataverse KnowledgeArticle records, curated SharePoint libraries, private Azure AI Search indexes) from public web sources, enforcing freshness dates (validfrom and validto), and applying lifecycle filters.
Role-based experience customization guarantees that Copilot generative outputs strictly respect Dataverse security roles, record ownership hierarchies, and Field-Level Security (FLS) profiles, preventing unauthorized data disclosure.
Last updated: September 2026
5.1 Business Terms & Copilot Customization in Customer Experience and Service
Quick Answer: Grounding Copilot in Dynamics 365 Customer Experience and Customer Service requires mapping enterprise domain vocabulary to Dataverse metadata and Customer Insights business glossaries. Architects customize generative summaries, contextual email drafting, and live conversation wrap-ups using Power Platform AI Prompts, while enforcing strict knowledge base freshness ( validfrom / validto ) and Dataverse Field-Level Security (FLS) to ensure compliant, hallucination-free service operations.
Deploying generative AI within enterprise customer experience and service operations introduces complex architectural challenges. Large language models (LLMs) possess vast generalized linguistic capabilities but lack contextual awareness of proprietary corporate jargon, industry metrics, customer entitlement hierarchies, and internal support policies. When service representatives ask Copilot in Dynamics 365 Customer Service or Customer Insights to summarize customer history, resolve escalations, or draft customer communications, the AI must reason over enterprise-specific business terms with deterministic accuracy.
Without explicit semantic grounding, foundation models make probabilistic inferences regarding enterprise terminology—for instance, confusing an operational account cancellation with a machine-learning-derived churn propensity score, or conflating Annual Recurring Revenue (ARR) with Total Contract Value (TCV). Solution architects must configure formal business glossaries, enrich Dataverse metadata schemas, establish authoritative knowledge grounding boundaries, and apply role-based security controls across the service lifecycle.
1. Enterprise Domain Vocabulary & Business Terms in Customer Experience
Enterprise organizations maintain distinct vocabularies that vary across lines of business. Dynamics 365 Customer Insights (spanning Customer Insights - Data and Customer Insights - Journeys) and Dynamics 365 Customer Service rely on unified customer profiles and interaction records. Grounding Copilot in these systems requires establishing a centralized semantic layer. +-----------------------------------------------------------------------------+
- | Enterprise Semantic Grounding Pipeline |
- +-----------------------------------------------------------------------------+
- | Enterprise User Prompt: "Identify High-Value Tier-1 accounts at churn risk" |
- | | |
- | v |
- | [Customer Insights Business Glossary / Dataverse Synonym Dictionary] |
- | - "High-Value Tier-1" --> Segment: msevtmgt_tier1_arr_threshold (ARR > $1M)|
- | - "Churn Risk" --> AI Model: msdiv_predictive_churn_score > 0.75 |
- | | |
- | v |
- | [Semantic Mapping & Relationship Graph Traversal] |
- | - Table: contact (contactid) <--- 1:N ---> Table: incident (ticketid) |
- | - Relationship: account_customer_contacts (accountid = parentcustomerid) |
- | | |
- | v |
- | [Dataverse Security & Field-Level Security (FLS) Filter] |
- | - Filter out masked PII / Restricted Financial Fields |
- | | |
- | v |
- | [Grounding Prompt Assembly & Generative Response] |
- | - Structured Context + Citations delivered to Copilot Desktop Interface |
- +-----------------------------------------------------------------------------+
- Resolving Semantic Ambiguities
In Customer Insights - Data, business terms provide human-readable definitions and formulaic context for unified customer attributes, calculated measures, and AI-driven predictive models. Consider the following common domain ambiguities that architects must resolve:
Customer Churn vs. Churn Propensity: Operational churn in Dynamics 365 Customer Service indicates an account or subscription has reached an inactive state ( statecode = 1, statuscode = 2 ). In contrast, churn risk in Customer Insights represents a probabilistic predictive score ( churn_score > 0.70 ) generated by an automated classification model. Defining these distinct entities in the business glossary prevents Copilot from telling a service representative that an active customer has already churned.
Annual Recurring Revenue (ARR) vs. Invoiced Revenue: Sales and finance frequently calculate revenue differently. ARR refers to the normalized annual value of active recurring subscription contracts, whereas invoiced revenue reflects historical cash received via ERP integration. The business glossary maps "ARR" directly to the calculated measure measure_customer_normalized_arr rather than the standard transactional invoice table.
Account Priority / Service Tier: An enterprise may designate accounts as "Gold", "Platinum", or "Strategic Enterprise". The glossary maps these labels to explicit entitlement matrices ( entitlement table in Dataverse) so that Copilot surfaces correct SLA resolution commitments (e.g., 2-hour response for Platinum versus 8-hour response for Standard).
Configuring the Business Term Glossary
Architects configure business terms within the Customer Insights admin experience and Dataverse semantic index. Each business term entry requires:
Term Name and Standard Aliases: The primary business noun (e.g., "Customer Lifetime Value") and common shorthand or acronyms (e.g., "LTV", "CLV", "Historical Margin").
Conceptual Definition: Natural language documentation explaining the business meaning, calculation methodology, and operational constraints.
Underlying Dataverse Mapping: Direct binding to specific entities, attributes, measures, or segments (e.g., binding "LTV" to customer_profile.calculated_lifetime_value ).
Synonym Dictionary: Variations in user phrasing ("spend to date", "cumulative purchases", "total account revenue") that trigger the term definition during semantic parsing.
2. Extending Copilot Prompts with Dataverse Metadata & Schema Binding
Copilot interactions within Dynamics 365 Customer Service do not query the underlying database using naive keyword search; they leverage the Semantic Index for Dataverse and natural-language-to-FetchXML translation. For the generative engine to synthesize accurate queries and summaries, the underlying Dataverse schema must be enriched with domain-specific metadata.
Dataverse Metadata Architecture for Copilot Grounding
The Dataverse metadata catalog serves as the foundational schema reference for Copilot. If custom tables and attributes lack descriptions, Copilot is forced to guess their intent based solely on technical schema names (e.g., new_carrval vs. new_totcntval ). Architects must enforce metadata hygiene:
Schema Component Technical Example Copilot Semantic Enrichment Requirement
Table Display Name msdyn_customerasset Set Display Name to "Customer Asset (Installed Equipment)". Provide Description explaining that this table tracks hardware deployed at customer physical sites.
Column Descriptions cr841_arr_value Add description: "Annual Recurring Revenue for active software licenses in USD. Excludes one-time implementation fees."
Choice Column Labels statuscode (Custom) Ensure choice values have explicit labels (e.g., "Awaiting Customer Telemetry" rather than "Pending - Code 3").
Relationship Aliases incident_customer_contacts Define relationship metadata clearly: "Relates a Service Case to the primary Contact reporting the incident."
Search Synonyms Dataverse Search Index Register synonyms: Map "warranty claim", "RMA", and "hardware defect" to the incident table with casetypecode = 3 .
Traversing Relational Schema Graphs
Service interactions rarely involve a single table. A customer case connects to an account, a primary contact, a service level agreement ( sla ), customer assets, and multiple timeline activities (emails, phone calls, portal messages). When Copilot constructs a prompt payload, it traverses these relationships based on Dataverse metadata definitions:
1:N (One-to-Many): One Account has many Service Cases ( account_incidents ). Copilot uses this relationship to evaluate customer case volume history and identify chronic product failures.
N:1 (Many-to-One): Many Service Cases link to one Entitlement record ( incident_entitlement ). Copilot checks this relationship to verify whether a requested service is covered under the customer's current warranty.
N:N (Many-to-Many): Competitors to Opportunities, or Service Incidents to Knowledge Articles ( incident_knowledgearticles ). Copilot evaluates past resolutions to recommend relevant articles for active tickets.
Important
Exam Tip: If Copilot in Customer Service produces hallucinated or empty responses when asked about custom entity relationships, verify that Dataverse Search is enabled for the entity and that the relevant lookup columns and relationships are explicitly added to the Quick Find View and Dataverse Search indexing profile. Copilot cannot reason over tables excluded from the Dataverse search index.
3. Customizing Copilot in Dynamics 365 Customer Service Workflows
Dynamics 365 Customer Service provides pre-built Copilot capabilities designed to streamline service representative productivity. Architects can tailor these capabilities using Power Platform AI Prompts (built on the AI Builder prompt engine) and Customer Service Admin Center configurations. +-----------------------------------------------------------------------------+
- | Copilot in Dynamics 365 Customer Service Customizations |
- +-----------------------+-----------------------+-----------------------------+
- | 1. Case Summaries | 2. Contextual Emails | 3. Live Wrap-Ups |
- +-----------------------+-----------------------+-----------------------------+
- | - Timeline extraction | - Tone adjustment | - Omnichannel conversation |
- | - Sentiment tracking | - Knowledge grounding | transcript synthesis |
- | - Root cause tagging | - SLA timeline notice | - Action item extraction |
- | - Resolution steps | - Dynamics 365 token | - Automated note creation |
- | synthesis | binding | attached to incident |
- +-----------------------+-----------------------+-----------------------------+
- |
- v
- +-----------------------------------------------------------------------------+
- | 4. Knowledge Base Drafting (Knowledge-Centered Service - KCS) |
- | - Converts resolved high-impact incident threads into draft articles |
- | - Auto-populates Title, Keywords, Issue Description, and Resolution Steps |
- | - Enforces corporate governance workflow: Draft -> Review -> Published |
- +-----------------------------------------------------------------------------+
- 3.1 Case Summarization Customization
When a service representative opens an incident record, Copilot generates an on-demand case summary. The standard summary synthesizes the case title, description, and recent timeline records.
Architectural Customization: Architects customize the summarization prompt template by modifying the system prompt in the Power Platform AI Prompts gallery. The prompt can be instructed to prioritize specific timeline activities (e.g., "Prioritize technician notes from Field Service work orders over automated email notifications") and enforce a structured output format:
Customer Problem & Impact: Core technical defect and business interruption severity.
Troubleshooting History: Chronological steps attempted by previous Tier-1/Tier-2 agents.
Pending Customer Actions: Items awaiting customer confirmation or telemetry logs.
Recommended Resolution Path: Suggested next steps grounded in matching knowledge articles.
3.2 Contextual Email Generation
Copilot enables service agents to generate customer email replies with a single click. Customizing this capability involves:
Tone & Persona Configuration: Enforcing corporate brand voice (e.g., "Empathetic and professional for high-severity enterprise cases; concise and instructional for standard requests").
Dynamic Context Injection: Grounding the draft email with specific Dataverse tokens, such as incident.ticketnumber , contact.firstname , and entitlement.sla_remaining_hours .
Knowledge Insertion Guardrails: Forcing the email generation prompt to incorporate exact resolution steps from an approved, verified knowledge base article rather than generating speculative troubleshooting advice.
3.3 Live Conversation Wrap-Ups
During omnichannel live chat or voice sessions, Copilot monitors the conversation transcript in real time. Upon session termination, Copilot automatically drafts a wrap-up summary:
Session Categorization: Maps the customer's dialogue to standard Dataverse categorization choice codes ( incident.casetypecode , incident.subjectid ).
Sentiment Trajectory: Records whether customer sentiment evolved from negative to positive across the conversation.
Action Item Extraction: Identifies commitments made by the representative (e.g., "Dispatch replacement optical transceiver by 5:00 PM EST") and automatically generates corresponding task or followup records in Dataverse linked to the parent case.
3.4 Knowledge Base Article Drafting (KCS Integration)
To support Knowledge-Centered Service (KCS) methodologies, architects configure Copilot to draft knowledge base articles directly from resolved cases. When a representative marks a complex case as resolved, Copilot inspects the incident timeline, identifies the verified root cause and working resolution, and generates a structured draft in the knowledgearticle table. The drafted article populates:
title : Problem description formatted according to enterprise knowledge conventions.
keywords : Extracted technical entities, error codes, and hardware model numbers.
content : Formatted HTML containing distinct "Problem Definition", "Environment", "Root Cause", and "Resolution" sections.
statuscode : Initialized to Draft (enforcing human review before publication).
4. Grounding Configuration & Knowledge Lifecycle Management
A critical responsibility of the Solution Architect is defining the boundary between verified enterprise knowledge and unverified or public data sources. +-----------------------------------------------------------------------------+
- | Knowledge Grounding Hierarchy |
- +-----------------------------------------------------------------------------+
- | [Tier 1: Dataverse Knowledge Base] |
- | - Published articles only (statuscode = 3 / Published) |
- | - Strict Date Filtering: validfrom <= CURRENT_DATE <= validto |
- | - Scoped by Product Taxonomy & Entitlement Tier |
- | | |
- | v |
- | [Tier 2: Internal Curated SharePoint & Azure AI Search] |
- | - Engineering Technical Bulletins, Standard Operating Procedures (SOPs) |
- | - Authenticated access with Microsoft Entra ID permissions |
- | | |
- | v |
- | [Tier 3: Public External Documentation (Optional / Restricted)] |
- | - Public product manuals, regulatory disclosures |
- | - Strict domain whitelisting (e.g., https://learn.contoso.com) |
- | - Disallowed for confidential customer account inquires |
- +-----------------------------------------------------------------------------+
- Internal vs. External Knowledge Sources
Dataverse Knowledge Base (Primary Authority): The native repository of structured support articles ( knowledgearticle ). Provides granular lifecycle management, multi-language translation, and version control.
Curated SharePoint Online Sites: Internal document repositories containing technical repair manuals, SOPs, and engineering change notices (ECNs). Copilot connects to SharePoint via Microsoft Graph connectors, respecting document-level access permissions.
Azure AI Search (Custom Semantic Store): For complex unstructured data estates (e.g., telemetry logs, historical PDF manuals), architects deploy Azure AI Search with vector embeddings, connecting to Copilot Studio or Customer Service via custom plugins.
Public Web Sources (Whitelisted): Restricted public domains. Architects must evaluate the risk of data drift or out-of-date documentation before enabling public web grounding.
Knowledge Freshness and Validity Dates
Serving out-of-date service policies (such as an expired warranty terms document or a deprecated firmware upgrade procedure) introduces severe legal and operational liabilities. Architects enforce temporal grounding rules:
validfrom and validto Attributes: Every Dataverse knowledgearticle possesses temporal validity fields. The Copilot grounding search filter must enforce the logical condition:
validfrom <= UtcNow() AND (validto >= UtcNow() OR validto == null)
Lifecycle State Filtering: Copilot must strictly filter on statecode = 3 ( Published ). Records in Draft , Needs Review , In Review , or Archived states must be excluded from generative grounding to prevent unapproved policies from reaching customers.
Knowledge Freshness Cadence: Organizations establish automated review cycles (e.g., 90-day review intervals). Power Automate flows monitor modifiedon timestamps, automatically transitioning articles past their review threshold to Needs Review , which temporarily removes them from Copilot grounding until re-certified by a knowledge manager.
5. Role-Based Experience Customization & Dataverse Security
Copilot in Dynamics 365 Customer Service is not an omniscient system that ignores enterprise security boundaries. It operates under the authenticated security context of the logged-in service representative.
Dataverse Security Model Inheritance
Copilot strictly inherits the Dataverse role-based access control (RBAC) model:
Business Unit & Hierarchy Security: A service representative assigned to the "North America Commercial Support" Business Unit cannot query or receive Copilot summaries regarding cases belonging to the "Government Defense Support" Business Unit unless explicit cross-unit sharing privileges exist.
Record-Level Ownership: If an agent's security role restricts case access to user-owned records ( User level depth), Copilot will only summarize cases owned by that specific user.
Read Privileges on Knowledge: Representatives only receive Copilot answers grounded in knowledge articles matching their role's read permissions. Internal-only engineering articles marked with restricted access will not be surfaced to Tier-1 frontline agents.
Field-Level Security (FLS) and Data Masking
Many enterprise cases contain highly confidential attributes, such as payment card details, social security numbers, medical record identifiers, or executive escalation notes. These columns are secured using Field-Level Security (FLS) profiles: [Incoming Case Context Payload]
- |-- incident.title: "Billing Dispute - High Priority"
- |-- incident.customerid: "Contoso Pharmaceuticals"
- |-- incident.description: "Dispute regarding cloud infrastructure billing..."
- |-- incident.new_confidential_margin: [RESTRICTED - FLS SECURED] <--- Masked Column
- |-- incident.new_executive_notes: [RESTRICTED - FLS SECURED] <--- Masked Column
- [Copilot Prompt Assembly Engine]
- |-- Agent Security Context: "Tier-1 Frontline Representative"
- |-- FLS Evaluation: Agent lacks Read permission on new_confidential_margin & new_executive_notes
- |-- Action: Columns completely omitted from model prompt context
- [Copilot Generative Output]
- |-- Result: Accurate billing dispute summary without leaking executive notes or profit margins
When Copilot constructs its grounding prompt, it evaluates the agent's active FLS profile. If the representative lacks Read access to new_executive_notes , that field is stripped from the prompt payload.
If an agent prompts Copilot: "What do the executive notes say about this case?", Copilot gracefully declines, stating that the requested information is restricted or unavailable, preventing unauthorized data exfiltration.
Administrative Enablement in Customer Service Admin Center
Solution architects manage Copilot feature rollouts through the Customer Service Admin Center (CSAC) . Capabilities can be enabled globally or targeted to specific security roles:
Copilot for Case Summaries: Enabled for Tier-1, Tier-2, and Escalation Supervisors.
Copilot for Email Drafting: Enabled for all customer-facing service roles; optional supervisor approval workflows can be mandated for newly onboarded agents.
Copilot for Live Conversation Wrap-Ups: Configured across Omnichannel digital and voice channels.
Data Protection & Tenant Boundary Controls: Ensuring that Copilot prompts and customer interactions are not used to train global foundation models and stay within the sovereign Azure geography.
6. Architectural Comparison: Standard vs. Enterprise-Grounded Copilot
Dimension Out-of-the-Box (OOB) Copilot Enterprise-Grounded Custom Copilot
Vocabulary & Semantics Relies on generic LLM linguistic training; susceptible to acronym and domain confusion Grounded in Customer Insights Business Terms, Dataverse glossaries, and custom synonym dictionaries
Schema Understanding Reads basic table and column labels; cannot interpret technical prefixes (e.g., cr841_ ) Leverages semantic metadata descriptions, custom relationship graphs, and Quick Find Search indexing profiles
Case Summaries Generic chronological summary of last 10 timeline records Tailored Power Platform AI Prompt template extracting Problem, Troubleshooting, Root Cause, and Next Actions
Knowledge Grounding Unfiltered search across all published and unpublished knowledge articles Strict freshness enforcement ( validto >= Today ), statuscode = Published , and product taxonomy scoping
Data Security & Privacy Standard Dataverse table permissions Full enforcement of Dataverse RBAC, Business Unit hierarchies, and Field-Level Security (FLS) masking
Knowledge Contribution Manual article creation from closed tickets Knowledge-Centered Service (KCS) workflow auto-drafting structured HTML articles directly into review queues