Description

The V-Commerce App is a modular, composable B2B e-commerce platform designed to support multi-channel sales (Web, Mobile, Salesforce) using a headless architecture. It provides seamless integration of modular microservices like Catalog, Cart, Checkout, Pricing Engine, and Order Management, along with real-time analytics. The platform interfaces with enterprise systems like Salesforce CRM and SAP S/4HANA ERP to enable enhanced customer engagement, product configuration, and streamlined order lifecycle management.

Actors

• Business Buyer/User: Places B2B orders, configures products, checks prices, and tracks orders.

• Sales Representative (Salesforce User): Assists clients, checks order status, and handles CRM interactions.

• E-Commerce Admin: Manages catalog, pricing rules, and monitors system analytics.

• ERP System (SAP S/4HANA): Manages inventory, logistics, invoicing, and financials.

• CRM System (Salesforce): Handles customer data, opportunity tracking, and sales engagement.

• Azure Microservices (via APIs): Serve front-end with data and business logic in real time.

Preconditions

• The buyer is authenticated (via SSO or standard login).

• The catalog is synced with ERP (SAP S/4HANA).

• Product configurations, pricing, and inventory are up to date.

• CRM and ERP integrations are operational.

• APIs and microservices are deployed and functioning.

Flow of Events

Main Flow:

1. Product Browsing & Configuration:

   • Buyer browses products via web or mobile interface.

   • Product configurator loads real-time options (variants, specs).

2. Pricing & Cart Operations:

   • Pricing engine dynamically calculates discounts and terms.

   • Buyer adds products to the cart; real-time validation occurs.

3. Checkout & Order Placement:

   • Buyer proceeds to checkout.

   • Address, delivery, and payment info is entered/selected.

   • Order submitted and confirmed via order microservice.

4. CRM & ERP Sync:

   • Order is pushed to Salesforce (for customer tracking) and SAP (for fulfillment).

   • ERP triggers inventory management and invoicing.

5. Post-Order Monitoring:

   • Buyer can track the order status via web/mobile.

   • Admin monitors transactions and trends in Azure Synapse Analytics.

Postconditions

• Order is successfully created and logged in CRM and ERP.

• Customer receives confirmation.

• Inventory is reserved.

• Analytics dashboards are updated in real-time.

Benefits

• Scalable, composable architecture enables rapid feature rollout.

• Seamless omnichannel experience across web, mobile, and Salesforce.

• Real-time product configuration and pricing personalization.

• Improved operational efficiency through ERP/CRM automation.

• Insightful sales and customer behavior analytics using Synapse.

• Reduced integration cost via serverless (Azure Functions + Kafka).

Tools & Technology Used

• Azure Web Services: Hosting frontend and APIs.

• Azure Functions: Stateless, scalable microservices (cart, catalog, etc.).

• Apache Kafka: Event streaming for real-time updates across modules.

• Azure Synapse Analytics: Data warehousing and business intelligence.

• Salesforce: CRM integration for customer and opportunity tracking.

• SAP S/4HANA: ERP backend for inventory, order fulfillment, invoicing.

Description:

This use case describes how Robotic Process Automation (RPA) is used to automatically extract customer details from incoming emails and update them in a CRM system. When a new email arrives in a designated Office 365 Outlook inbox, the RPA workflow parses key details such as the customer's Name, Email, and Phone Number from the message body. It then either creates a new Contact/Lead or updates an existing record in Salesforce CRM, ensuring accurate and timely data capture for future engagement.

Actors:

• Primary Actor: RPA Bot (Power Automate Flow)

• Supporting Systems:

  • Microsoft Outlook (Office 365)

  • Salesforce CRM

• Human Users (Indirect):

  • Sales Representatives

  • Customer Service Agents

  • Marketing Team

Preconditions:

• Office 365 Outlook inbox is configured and accessible.

• Salesforce CRM account is active with permissions to create/update Contacts or Leads.

• Power Automate account is set up with access to both Outlook and Salesforce connectors.

• Email templates or structure follow a predictable format for parsing customer details.

• Authentication between services (Outlook, Power Automate, Salesforce) is configured.

Flow of Events:

1. Trigger: A new email is received in the Office 365 Outlook inbox.

2. Bot Action: Power Automate flow is triggered automatically.

3. Email Parsing:

   • Extract sender name, email address, and phone number from the email body.

   • (Optional) Use AI builder for entity recognition if email format is unstructured.

4. CRM Lookup: Check if a Contact or Lead already exists in Salesforce using the email address.

5. Update/Create Logic:

   • If found, update the existing Contact/Lead with any new details.

   • If not found, create a new Lead or Contact entry in Salesforce.

6. Logging: Record the transaction in a log (e.g., Excel/SharePoint list or internal Salesforce field).

7. (Optional) Notification: Send a Teams/email notification to the sales team for each update.

Postconditions:

• Customer details are correctly stored or updated in Salesforce CRM.

• No manual effort was required for data entry.

• A transaction log is maintained for audit or tracking.

• Sales and customer service teams have real-time visibility of new leads.

Benefits:

• Saves time and reduces human error in data entry.

• Ensures Salesforce is always updated with latest customer data.

• Enables faster lead engagement and customer follow-up.

• Allows sales teams to focus on relationship-building instead of data processing.

• Enhances operational efficiency using existing Microsoft ecosystem.

Tools & Technology Used:

• Microsoft Outlook - Source of incoming emails

• Office 365 Account - Secure access to Outlook and Power Automate

• Power Automate - RPA platform to automate the end-to-end workflow

• Salesforce CRM - Destination system for customer data

• (Optional) AI Builder - Used for unstructured email parsing

• SharePoint / Excel / Teams - Used for notifications or logs

Description

The Digital Service Estimator is a low-code application designed to streamline the effort and cost estimation process for digital services. It supports both predictive ROM (Rough Order of Magnitude) estimation and detailed estimations across various service areas such as Program Management, Solution Design, Infrastructure Setup, Data Migration, Testing, Go-live Support, and Managed Services.

The tool integrates AI-driven keyword extraction to generate instant ROM estimates based on opportunity briefs or inputs. It also employs a rule-based engine for generating structured, detailed cost and effort breakdowns, leveraging historical data, reusable estimation assets, resource profiles, and rate cards. Estimation outputs can be visualized and shared via integrated Power BI dashboards.

Actors

• Estimator / Pre-sales Consultant – primary user responsible for preparing estimates.

• Delivery Manager / SME – provides reusable assets, templates, and validates detailed estimates.

• Sales / Account Manager – reviews high-level ROM and estimation results for client proposal.

• AI Model (via AI Builder) – extracts key entities and parameters from briefs or RFPs.

• Dataverse – acts as the centralized data store for assets, profiles, and estimation logic.

Preconditions

• The Estimator app is deployed and accessible via Power Apps.

• Required reusable estimation assets, resource profiles, and rate cards are populated in Dataverse.

• AI Builder is trained to extract relevant keywords (e.g., service types, volumes, durations) from textual input.

• Rule-based estimation logic is configured and mapped to service categories.

Flow of Events

1. User logs in to the Power Apps-based Estimator interface.

2. Inputs a brief (e.g., opportunity description or client RFP text).

3. AI Builder analyzes the text and extracts service keywords, expected timelines, volumes, etc.

4. ROM Estimator module generates a high-level estimation based on AI-extracted inputs.

5. User optionally switches to Detailed Estimation mode.

6. User selects applicable service areas (e.g., Infra, Migration, Testing).

1. The rule engine fetches reusable estimation templates and populates effort/cost based on input parameters and resource rates.

2. User reviews/modifies outputs, adds assumptions, and submits for review if required.

3. Finalized estimation is visualized in Power BI and can be exported to PDF/Excel.

4. Results are stored in Dataverse for tracking and reuse in future estimations.

Postconditions

• A completed, AI-assisted estimation is generated and stored.

• Estimation artifacts (ROM and detailed) are available for download and internal review.

• Data is logged for future analytics and continuous improvement of estimation logic.

Benefits

• Reduces estimation cycle time from days to hours using AI-driven ROM estimation.

• Improves estimation accuracy with rule-based detailed costing and reusable assets.

• Standardizes estimation process across teams and engagements.

• Ensures historical estimations and assumptions are centrally stored and auditable.

• Enhances visibility for sales, delivery, and finance teams through Power BI dashboards.

Tools & Technology Used

• Power Apps – to build the Estimator user interface.

• AI Builder (Power Platform) – for keyword/entity extraction and predictive estimation.

• Microsoft Dataverse – as a centralized data repository for assets, profiles, templates, and rate cards.

• Power BI – for visualization and reporting of estimation results.

• Power Automate (optional) – to automate notifications, approvals, or versioning of estimation outputs.

Description:

This use case describes an AI-powered Predictive Maintenance Dashboard that allows users to upload IoT sensor data and receive intelligent insights into potential equipment failures. The system leverages machine learning models to analyze data in real time, generate predictive reports, and provide actionable alerts. Secure access control and user management are ensured through Azure AD B2C.

Actors:

• Maintenance Engineer – Uploads sensor data, views predictions, and schedules maintenance.

• Operations Manager – Reviews predictive reports and alerts for strategic planning.

• System Admin – Manages user access, roles, and system configurations.

• AI Model (Cognitive Services) – Processes sensor data and returns failure predictions.

Preconditions:

• The user is authenticated via Azure AD B2C.

• Valid sensor data (CSV/JSON) is available for upload.

• The AI model is trained and deployed via Cognitive Services or a custom Azure Function.

• Cosmos DB and other services are properly configured.

Flow of Events:

1. Login & Access - User logs into the web app using Azure AD B2C authentication.

2. Upload Data - User uploads sensor data through the dashboard (via Azure Static Web App frontend).

3. Data Processing - The uploaded data is sent to an Azure Function for validation and pre-processing.

4. Prediction Engine - The Azure Function invokes a trained AI model (via Cognitive Services or a custom model) to analyze data and detect failure risks.

5. Result Storage & Retrieval - Prediction results and insights are stored in Cosmos DB.

6. Visualization - Results are rendered on the dashboard with charts, failure probability, and equipment health trends.

7. Report Generation - Users can generate downloadable maintenance reports (PDF/CSV) from the dashboard.

8. Notification (Optional) - Alerts for critical equipment risks can be triggered via web-hook or email.

Postconditions:

• Predictions and reports are stored securely in the system.

• Maintenance engineers have actionable insights to plan preventive actions.

• System logs the activity for future auditing.

Benefits:

• Reduced equipment downtime through proactive planning.

• Minimized unexpected failures and maintenance costs.

• Centralized access to all maintenance intelligence and history.

• Scalable architecture using Azure's cloud-native services.

• Secure and role-based user access.

Tools & Technologies Used:

• Azure Static Web Apps – Frontend hosting for the dashboard.

• Azure Functions (Python) – Serverless compute for data processing and ML inference.

• Azure Cosmos DB – NoSQL storage for prediction results and metadata.

• Azure Cognitive Services – AI services for prediction models (if applicable).

• Azure Active Directory B2C – User authentication and secure access management.

• Python – Backend processing, data handling, and AI integration.

Description

The Regulatory Intelligence Bot is a smart assistant designed to continuously monitor and process global and local telecom regulatory updates. It uses AI to summarize, prioritize, and alert compliance teams about relevant changes. The bot alleviates the burden of regulatory overload, reduces delay in responding to critical updates, and minimizes the risk of non-compliance.

As regulatory landscapes become more complex and frequent, this solution ensures proactive compliance management using automation, AI, and cloud technologies.

Actors

• Primary Actors:

  • Compliance Officer

  • Legal Analyst

  • Regulatory Affairs Manager

• Supporting Systems:

  • Azure Logic Apps

  • Azure Bot Service

  • Azure Cosmos DB

  • OpenAI (for NLP-based summarization and classification)

Preconditions

• Access to regulatory data sources (e.g., websites, documents, RSS feeds).

• Azure infrastructure is set up and configured.

• Compliance team is onboarded to the bot interface (Teams, Web App, or email).

• Necessary permissions and APIs for data scraping/parsing are configured.

Flow of Events (Main Scenario)

1. Trigger:
   Azure Logic Apps runs on a schedule to fetch regulatory content from multiple telecom regulatory authorities.

2. Ingestion:
   Documents (PDFs, HTML pages, RSS feeds,  JSON files) are stored in Azure Blob Storage.

3. Processing:
   Azure Functions trigger OpenAI services to:

   • Summarize content.

   • Classify by region, topic, urgency.

   • Extract key compliance actions.

4. Storage:
   Structured summaries and metadata are saved in Azure Cosmos DB.

5. Notification & Action:

   • Azure Bot Service and Power Automate send proactive alerts to compliance users (via Teams, email, or portal).

   • Users can ask questions to the bot (e.g., “What changed in EU telecom regulation today?”) and get intelligent responses.

6. Review & Decision:
   Compliance teams review the insights and assign tasks/actions if needed.

Postconditions

• All relevant telecom regulation updates are tracked and summarized.

• Alerts and recommendations are sent in near real-time.

• Compliance teams take timely action with reduced manual effort.

• Historical updates are stored and searchable.

Benefits

• Reduces regulatory overload and manual tracking.

• Speeds up response time to new regulations.

• Lowers the risk of missing critical compliance updates.

• Builds a historical repository of regulatory changes.

• Empowers compliance teams with AI-driven insights.

Tools & Technology Used

• Azure Logic Apps - Schedule and orchestrate regulatory data ingestion

• Azure Functions - Process, transform, and analyze ingested content

• OpenAI API - Summarize, classify, and extract regulatory actions

• Azure Cosmos DB - Store structured insights with fast querying

• Azure Blob Storage - Store raw documents and web content

• Azure Bot Service - Enable conversational access to the insights

• Azure Web Apps - Front-end portal for search and review

• Power Automate - Trigger notifications and approval workflows

Description

This use case enables organizations to represent every key product, module, or component as a digital twin. Each twin is enriched with metadata such as current lifecycle phase, responsible owner, and geographical region. The system actively tracks these assets through their entire lifecycle — from development to retirement — and issues real-time alerts on critical lifecycle events such as:

• End-of-Life (EOL) risk,

• Quality issue recurrence, or

• Scheduled upgrade windows.

It provides stakeholders with real-time visibility, proactive alerting, and workflow automation, allowing informed decision-making and faster responses throughout the product lifecycle.

Actors

• Product Manager: Monitors lifecycle phases and risks; initiates lifecycle transitions.

• Quality Engineer: Analyzes recurring quality issues and responds to alerts.

• Manufacturing Lead: Coordinates production updates based on lifecycle status.

• IT Administrator: Manages digital twin infrastructure and integrations.

• Support Engineer: Acts on alerts regarding upgrade needs or obsolescence.

• Automation System (Power Automate): Triggers notifications and workflows.

Preconditions

• Digital representations (twins) of all products/modules are created and registered in Azure Digital Twins.

• Lifecycle metadata (phase, region, owner) is populated and synced from Dataverse.

• Rules and thresholds for lifecycle events (e.g., EOL, upgrade window) are defined.

• Integration between Azure Digital Twins, Power BI, Power Automate, and Dataverse is configured.

Flow of Events

1. Initialization:

   • Digital twins are modeled in Azure Digital Twins with metadata from Dataverse.

   • Lifecycle rules and alerts are defined in the system.

2. Monitoring:

   • Twins continuously track lifecycle stages in real-time.

   • Metadata changes (e.g., ownership transfer, phase shift) are logged automatically.

3. Event Detection:

   • Events such as EOL proximity, repeated quality issues, or missed upgrade windows are detected.

4. Notification & Response:

   • Power Automate triggers alerts to relevant actors via email, Teams, or dashboards.

   • Support or Product Managers initiate corrective or transition actions.

5. Analysis & Optimization:

   • Power BI dashboards visualize lifecycle distribution, bottlenecks, or risks.

   • Teams use insights to make proactive decisions (e.g., schedule upgrades, prioritize redesigns).

Postconditions

• Lifecycle transitions are proactively managed.

• Product risks and upgrade windows are addressed in time.

• Quality and support issues are resolved faster due to early alerts.

• Accurate and real-time visualization of product status across lifecycle.

Benefits

• Improved traceability of product lifecycle stages across global teams.

• Early warning system reduces operational disruptions.

• Enables predictive and preventive decision-making.

• Reduces cost associated with late upgrades or EOL failures.

• Supports collaborative planning among R&D, manufacturing, and support.

Tools & Technology Used

• Azure Digital Twins: Core platform for modeling and monitoring digital twins.

• Power BI: Visualizes lifecycle data, events, and performance metrics.

• Power Automate: Automates alerts, escalations, and workflow triggers.

• Dataverse: Central metadata repository linked to product and lifecycle data.

Description

The Lean Product Architecture Evaluation Tool is designed to optimize an organization’s product portfolio by identifying redundant products, unnecessary variants, and outdated configurations. By applying LEAN scoring principles, it recommends actions such as consolidation, simplification, and rationalization. The tool enables cost-saving simulations and risk assessments to support data-driven decision-making in product lifecycle management.

Actors

• Product Manager: Primary user responsible for analyzing product data and making rationalization decisions.

• Business Analyst: Assists in interpreting outputs, risk assessments, and cost-saving simulations.

• Engineering Lead: Evaluates feasibility of removing or consolidating configurations from a technical standpoint.

• Executive Leadership: Uses summarized insights (e.g., in Power BI) to guide strategic portfolio decisions.

Preconditions

• Product portfolio data is available in Excel or structured format (including configurations, sales volumes, support status, etc.).

• Users have access to the evaluation tool via Streamlit UI.

• LEAN scoring criteria (e.g., cost-to-maintain, usage frequency, variant overlap) are defined or pre-configured.

Flow of Events

Basic Flow:

1. User Uploads Product Data: Excel file is imported via the Streamlit interface.

2. LEAN Scoring Applied: The tool automatically evaluates each product using defined LEAN criteria (e.g., usage, complexity, cost).

3. Duplication & Variant Analysis: Identifies overlapping products, redundant features, or low-value configurations.

4. Rationalization Recommendations: Suggests consolidation or discontinuation of products/variants.

5. Simulations Executed: Users view cost-saving projections and associated risk assessments for each proposed change.

6. Export Results: Results can be downloaded in Excel or viewed as dynamic dashboards in Power BI.

7. Management Review: Insights shared with executive teams via Power BI dashboards for approval and implementation planning.

Postconditions

• A prioritized list of product rationalization opportunities is generated.

• Simulated financial impact and risk analysis are available for each recommendation.

• Final report is exported or visualized in Power BI for stakeholder communication.

Benefits

• Reduces operational and maintenance costs by eliminating unnecessary product complexity.

• Increases focus on high-performing and value-generating products.

• Enhances transparency and data-driven decision-making in product strategy.

• Improves alignment between product, engineering, and business teams on lifecycle decisions.

Tools & Technology Used

• Python (Streamlit): Front-end interface for data upload, analysis, and LEAN scoring logic.

• Pandas, NumPy: Data processing and computation engine behind scoring and simulations.

• Excel Import/Export: Seamless integration with existing business data sources and outputs.

• Power BI: Dashboard visualization and executive-level reporting.

Description

This use case outlines how the system evaluates the impact of a proposed product or component change across the supply chain, inventory, and compliance domains. By leveraging AI models trained on historical change management data, the system automatically classifies the impact as Low, Medium, or High risk. It then generates a structured recommendation report identifying the required documentation, stakeholder approvals, and actions needed prior to product rollout.

Actors

• Product Manager – Initiates the product/component change request.

• Engineering Lead – Reviews technical impact details.

• Compliance Officer – Validates compliance obligations.

• Supply Chain Manager – Evaluates impact on logistics and vendors.

• AI Estimator System (Power Platform) – Performs automated impact analysis and report generation.

Preconditions

• The change request must be formally submitted through Power Apps with all mandatory fields (product ID, component details, proposed changes, justification, etc.).

• Historical change data must be available in Dataverse for AI training and inference.

• AI model is trained and published in Azure AI Builder.

• Necessary Power Automate workflows are in place to trigger the AI estimation and notification process.

Flow of Events

Main Flow

1. Product Manager submits a product change request via Power Apps.

2. The request data is saved to Dataverse.

1. Power Automate triggers the AI model built in Azure AI Builder to analyze the impact using historical patterns.

1. The AI model classifies the change as Low, Medium, or High risk.

2. Based on the risk level:

   • A recommendation report is generated including suggested documentation, compliance checkpoints, and stakeholders to involve.

   • Notifications are sent to relevant actors.

3. Product Manager and relevant stakeholders review the report.

4. If accepted, the change process proceeds to formal approval and rollout planning.

Alternate Flow

• If the AI model returns insufficient confidence or flags missing data, the request is redirected for manual review.

• The Product Manager is notified to provide additional information.

Postconditions

• The change request is classified and documented with a corresponding AI-generated impact report.

• Stakeholders are notified and aligned based on the risk level.

• The product change process is either escalated, approved, or paused based on AI recommendations and human review.

Benefits

• Reduces manual effort in evaluating complex change impacts.

• Improves decision-making speed and accuracy using AI-driven insights.

• Ensures better compliance by auto-recommending documentation and stakeholder reviews.

• Standardizes the evaluation process across teams.

Tools & Technology Used

• Power Apps – Frontend interface for submitting change requests.

• Dataverse – Central data repository for change request metadata and historical logs.

• Azure AI Builder – Trained model that evaluates impact and classifies risk.

• Power Automate – Orchestration of workflows between components (data input, AI prediction, report generation, notification).

Description

A secure and scalable Product Information Management (PIM) system designed to centrally manage product data, enabling administrators to add, update, delete, and retrieve product information through a web interface. The system ensures authenticated access, real-time updates, and seamless access to a centralized product catalog for downstream systems or users. This solution supports enterprise-grade performance, security, and extensibility via cloud-native services.

Actors

• Admin User: Responsible for managing product data (create, update, delete, view).

• Authenticated Consumer (optional): A user or system that accesses the product catalog in read-only mode (e.g., e-commerce frontend, internal product team).

• Azure AD B2C: Manages secure identity and access management for admin users.

• Azure Functions (API Layer): Handles CRUD operations on product data.

• Cosmos DB: Stores structured product information in a NoSQL format.

Preconditions

• The admin user must be authenticated via Azure AD B2C.

• The web interface (React app) must be deployed and connected to API endpoints.

• Cosmos DB is provisioned and contains the relevant schema for product data.

• All Azure Functions are deployed and properly secured behind Azure API Management.

Flow of Events

Basic Flow (Add/Update/Delete Product)

1. Admin logs in securely via Azure AD B2C on the frontend.

2. Admin navigates to the product management dashboard.

3. Admin selects an action:

   • Add Product: Enters new product details via form and submits.

   • Update Product: Edits details of an existing product and saves changes.

   • Delete Product: Selects a product and confirms deletion.

4. Frontend sends a secure API call to the corresponding Azure Function endpoint.

5. Azure Function authenticates the request and performs the operation in Cosmos DB.

6. The result (success/failure) is returned to the frontend.

7. Admin receives confirmation and the UI reflects the latest catalog state.

Alternative Flow (Read-Only Access)

1. Authenticated consumer queries the product catalog via API or web interface.

2. Azure Function retrieves data from Cosmos DB and returns it securely.

3. UI renders the product list or search results.

Postconditions

• Product data is accurately stored and updated in Cosmos DB.

• Audit logs or monitoring (via Azure Monitor) capture key actions for traceability.

• Admin users can consistently perform CRUD operations via a secure interface.

• The product catalog reflects real-time updates across channels.

Benefits

• Centralized Control: Single source of truth for product information.

• Secure Access: Enforced via Azure AD B2C and protected endpoints.

• Scalable Architecture: Built on serverless Azure Functions and Cosmos DB.

• Extensible: Easy to integrate with external APIs or business systems.

• Cost-Efficient: Pay-as-you-go model via Azure Function consumption plan.

• User-Friendly: Modern frontend using React and Azure Static Web Apps.

Tools & Technologies Used

• Frontend: React hosted on Azure Static Web Apps

• Backend/API Layer: Python-based Azure Functions

• Database: Azure Cosmos DB (NoSQL)

• Security & Authentication: Azure AD B2C

• API Exposure & Governance: Azure API Management

• Monitoring & Logging: Azure Monitor (optional enhancement)

Description

The Product Lifecycle Compliance Checker is an automated solution designed to continuously audit product-related data stored in enterprise systems. It ensures that product master records, associated documentation, and configuration data adhere to internal PLM (Product Lifecycle Management) standards and regulatory policies. The system identifies non-compliance—such as missing documents, expired certificates, invalid data owners, or policy conflicts—and initiates alerts and summary reports for lifecycle managers. These managers can then act via correction workflows to maintain data integrity throughout the product lifecycle.

Actors

• Lifecycle Manager: Receives compliance summary reports and manages resolution workflows.

• System Administrator: Configures rules, triggers, and monitors system health.

• Compliance Auditor: Reviews flagged items and validates audit outcomes.

• Automated Agent (Power Automate): Executes scheduled compliance checks, sends alerts and reports.

Preconditions

• Product master data, document attachments, and configuration records are stored in Dataverse.

• Audit rules and PLM compliance policies are defined and accessible.

• Microsoft Power Automate flows are configured to run periodically.

• Azure Cognitive Search index is created for relevant metadata and document text.

• Users have access to Outlook to receive notifications.

Flow of Events

1. Trigger: Power Automate runs on a schedule (e.g., daily/weekly).

2. Data Collection: Pulls product records and associated metadata from Dataverse.

3. Indexing: Azure Cognitive Search scans documents for keyword/policy compliance.

4. Validation: System applies rules to check for:

   • Missing or expired documents

   • Invalid or unassigned data owners

   • Conflicts with PLM naming conventions or approval workflows

5. Flagging: Non-compliant records are logged and flagged for review.

6. Reporting: Summary report is generated and emailed via Outlook to lifecycle managers.

7. Action: Lifecycle managers initiate corrective workflows from the summary dashboard (manual or automated).

Postconditions

• All flagged compliance issues are tracked and traceable.

• Managers are informed about areas needing correction.

• Updated records reflect improved data governance.

• Historical audit logs are stored for traceability.

Benefits

• Automates tedious manual audits, saving time and reducing errors.

• Improves PLM data accuracy and readiness for audits or certifications.

• Enhances visibility into compliance health across the product lifecycle.

• Encourages accountability with automated notifications and ownership checks.

Tools & Technology Used

• Microsoft Power Automate – Orchestrates scheduled compliance checks and report flows.

• Microsoft Dataverse – Stores structured product data and document references.

• Azure Cognitive Search – Enables semantic search and validation of unstructured attachments.

• Microsoft Outlook – Distributes compliance reports and alerts to stakeholders.

Description

A comprehensive Zero Trust-based cybersecurity solution designed for a telecom operator’s digital service portal, network infrastructure, and core systems. The solution enforces multi-layered security, secure identity, data governance, and threat protection while ensuring full alignment with GDPR, ISO 27001, and NIS2 regulations. It integrates DevSecOps principles across CI/CD pipelines and containerized workloads, delivering real-time visibility, automated policy enforcement, and proactive threat detection and response.

Actors

• Security Architect – Designs and governs the Zero Trust model and compliance strategy.

• DevOps Engineer – Integrates DevSecOps and security automation into CI/CD pipelines.

• System Administrator – Manages infrastructure, user roles, and policy enforcement.

• Regulatory Compliance Officer – Ensures adherence to GDPR, ISO 27001, and NIS2.

• End Users (Telecom Customers) – Interact with the portal securely via verified identities.

• Threat Detection System (Automated) – Monitors, logs, and alerts on anomalies or threats.

Preconditions

• Telecom operator has an online customer-facing portal and internal operational network.

• Need for strong security posture due to regulatory and business requirements.

• Identity management system is in place but not fully Zero Trust–compliant.

• Development and deployment pipelines are containerized (Docker/Kubernetes).

• Compliance mandates from GDPR, ISO 27001, and NIS2 are active.

Flow of Events

1. System Hardening & Perimeter Security:

   • Azure Firewall and Microsoft Defender for Cloud are configured to enforce baseline protections.

   • Network segmentation, micro-perimeters, and rules established.

2. Identity & Access Control:

   • Azure AD B2C authenticates users and enforces conditional access policies.

   • Integration with Key Vault for managing credentials and secrets.

3. Data Protection & Governance:

   • Azure Purview classifies sensitive data and ensures data residency and access policies.

   • All customer and operational data encrypted in transit and at rest.

4. Threat Detection & Response:

   • Microsoft Sentinel collects logs and alerts from all security layers.

   • Behavioral analytics and automated playbooks respond to incidents.

5. DevSecOps Integration:

   • CI/CD pipelines secured with image scanning and policy enforcement.

   • Containerized workloads scanned and monitored continuously.

   • Secrets and keys managed via Azure Key Vault, integrated with pipelines.

6. Monitoring & Compliance:

   • Continuous assessment of system against ISO 27001 controls.

   • Real-time dashboards and audit trails for NIS2 compliance.

Postconditions

• A fully operational and continuously monitored Zero Trust security architecture.

• All users are authenticated and authorized through a central identity platform.

• All sensitive data is governed and protected under defined policies.

• Development pipelines are integrated with security checks and policy gates.

• The solution meets or exceeds regulatory compliance standards.

Benefits

• Security: Strong perimeter, endpoint, identity, and data protection.

• Compliance: Conforms to GDPR, ISO 27001, and NIS2 without manual overhead.

• Scalability: Modular security architecture adaptable to new services or regions.

• Resilience: Real-time detection, auto-response, and minimal attack surface.

• Productivity: Automated DevSecOps ensures faster yet safer deployments.

Tools & Technology Used

• Microsoft Sentinel - SIEM & SOAR for centralized logging, alerting, and automated incident response

• Microsoft Defender Suite - Endpoint, cloud, container, and identity protection

• Azure AD B2C - Secure identity and access management for customers

• Azure Key Vault - Secure secrets, keys, and certificates management

• Azure Firewall - Network traffic filtering and segmentation

• Azure Purview - Data classification, governance, and privacy controls

• Azure DevOps / GitHub Actions - Secure CI/CD with integrated security scanning

Description:

This use case outlines the implementation of a modern, secure, and scalable data architecture using Azure-native services to support end-to-end data operations — from ingestion and storage to transformation, governance, and reporting. The solution organizes data in layered formats (Bronze, Silver, Gold) and ensures regulatory compliance, access control, and BI enablement.

Actors:

• Data Engineers – design and build data pipelines, transformations

• Data Architects – define the architecture and governance frameworks

• BI Analysts / Power Users – consume curated data for insights and reporting

• Data Stewards – manage data cataloging, quality, and compliance

• Security Admins – enforce data access policies and auditing

• Azure Platform Team – maintain infrastructure, monitor services

Preconditions:

• Azure subscription and access to required services

• Initial connectivity with on-premise/cloud data sources

• Data classification, compliance, and security requirements defined

• User roles and responsibilities established

• Governance framework scoped (e.g., lineage, catalog, DQ rules)

Flow of Events:

1. Data Ingestion

   • Azure Data Factory (ADF) ingests data from multiple sources (databases, APIs, flat files, etc.) into Azure Data Lake Storage Gen2 (ADLS Gen2) Bronze layer (raw zone)

2. Data Storage & Organization

   • Data is stored in hierarchical, zone-based storage within ADLS Gen2:

     1. Bronze: raw, ingested data

     2. Silver: cleansed and validated data

     3. Gold: curated and transformed data ready for analytics

3. Data Processing & Transformation

   • Azure Databricks processes data using Spark notebooks and Delta Lake for efficient transformations (Bronze → Silver → Gold)

4. Data Governance & Cataloging

   • Microsoft Purview is used to catalog datasets, track lineage, assign classifications, and ensure compliance policies are enforced

5. Analytics & Reporting Access

   • Transformed Gold layer data is loaded into Synapse Analytics

   • Power BI and other tools connect to Synapse for interactive dashboards and reporting

6. Security & Monitoring

   • Role-based access control (RBAC) and data masking implemented

   • Audit and monitoring logs configured via Azure Monitor and Purview scans

Postconditions

• Data is ingested, transformed, and governed with traceability

• Gold data is available for secure BI/SQL access

• Data lineage, catalog, and access controls are in place

• Auditable compliance with internal and external policies achieved

Benefits

• Scalability: Supports large-scale data workloads across various domains

• Security: Role-based access and governance reduce data breach risks

• Compliance: Tracks lineage and classification for regulatory needs (e.g., GDPR)

• Reusability: Layered data approach enables reuse across business units

• Operational Efficiency: Automation through ADF and Databricks reduces manual work

• Improved Decision Making: Timely access to curated data enhances analytics outcomes

Tools & Technology Used

• Azure Data Factory (ADF) – Data orchestration and pipeline management

• Azure Data Lake Storage Gen2 (ADLS Gen2) – Centralized data lake with hierarchical namespace

• Azure Databricks – Distributed data processing and transformation

• Azure Synapse Analytics – SQL-based querying, data warehousing, BI interface

• Microsoft Purview – Data cataloging, lineage tracking, and compliance governance