Saturday, 26 July 2025

Microsoft Purview Administration Training Syllabus

 


Microsoft Purview Administration Training Syllabus


1Introduction to Microsoft Purview

  • Overview of Microsoft Purview platform

  • Unified data governance concepts

  • Key features and business benefits

  • Purview architecture and integration with Azure/M365

2. Setting up Purview

  • Account creation and environment setup

  • Navigating the Purview portal

  • Understanding cost, versions, and account inspection

  • ARM Templates and deployment practices

3. Data Discovery and Cataloging

  • Registering data sources

  • Catalog browsing and building data hierarchies

  • Glossary creation and management

  • Purview Insights for data visibility

4. Data Classification and Lineage

  • Techniques for data discovery and classification

  • Creating and using classification labels

  • Automated data classification and scanning

  • End-to-end data lineage mapping

5. Implementing Data Governance Policies

  • Data governance policy definition

  • Managing access and permissions

  • Data quality and integrity controls

  • Setting up compliance and regulatory frameworks

6. Information Protection and Sensitivity Labels

  • Data sensitivity concepts and implementation

  • Creating and managing sensitivity labels

  • Document fingerprinting, trainable classifiers, and exact data match (EDM)

7. Data Loss Prevention (DLP) and Insider Risk

  • DLP policy setup for workloads and endpoints

  • Monitoring and responding to DLP alerts and reports

  • Insider risk management best practices

8. Compliance and Regulatory Management

  • Compliance Manager overview

  • Setting up compliance controls and reporting

  • Audit, eDiscovery, and communications compliance

9. Advanced Features and Automation

  • REST APIs and PowerShell for Purview

  • Integration with other Azure services

  • Automating scans and data governance tasks

10. Monitoring, Troubleshooting, and Best Practices

  • Monitoring Purview environment health

  • Troubleshooting common operational issues

  • Real-world scenarios and case studies

11. Hands-On Labs and Practice

  • Data discovery and classification exercises

  • Policy creation and enforcement

  • Compliance configuration scenarios

  • Hands-on practice with portal and APIs

Ideal Audience:

  • IT and Data Governance Professionals

  • Cloud Solution Architects

  • Security/Admins and Compliance Officers

  • Database Administrators, Data Stewards

Prerequisites:

  • General knowledge of data governance concepts

  • Familiarity with Microsoft Azure and Microsoft 365 basics





 

DLP vs AIP in M365

This document provides a comparison between Data Loss Prevention (DLP) and Azure Information Protection (AIP) within the Microsoft 365 ecosystem. It outlines the core functionalities of each service, their key differences, and how they can be used together to provide a comprehensive data protection strategy. Understanding the nuances of DLP and AIP is crucial for organizations seeking to safeguard sensitive information and comply with regulatory requirements.

Data Loss Prevention (DLP)

DLP in Microsoft 365 is a suite of tools and policies designed to prevent sensitive information from leaving the organization's control. It focuses on identifying, monitoring, and protecting data in transit, at rest, and in use. DLP policies can be configured to detect specific types of sensitive information, such as credit card numbers, social security numbers, or protected health information (PHI), and take actions to prevent its unauthorized disclosure.

Key Features of DLP

  • Sensitive Information Types (SITs): DLP uses pre-defined and customizable SITs to identify sensitive data based on patterns, keywords, and other criteria. Microsoft provides a wide range of built-in SITs, and organizations can create their own to meet specific needs.
  • Policies: DLP policies define the rules and actions to be taken when sensitive information is detected. These policies can be applied to various locations, including Exchange Online, SharePoint Online, OneDrive for Business, Microsoft Teams, and endpoint devices.
  • Actions: When a DLP policy detects sensitive information, it can take various actions, such as:
    • Blocking: Preventing the user from sending or sharing the sensitive information.
    • Alerting: Notifying administrators or users about the policy violation.
    • Auditing: Logging the event for future investigation.
    • User Education: Displaying a policy tip to educate users about the organization's data protection policies.
    • Encryption: Encrypting the sensitive information to protect it from unauthorized access.
  • Reporting and Analytics: DLP provides reporting and analytics capabilities to track policy violations, identify trends, and assess the effectiveness of data protection measures.
  • Endpoint DLP: Extends DLP capabilities to endpoint devices, allowing organizations to monitor and protect sensitive data on laptops, desktops, and other devices.

Use Cases for DLP

  • Preventing accidental disclosure of sensitive data: DLP can prevent employees from accidentally sending sensitive information in emails or sharing it on public websites.
  • Complying with regulatory requirements: DLP can help organizations comply with regulations such as GDPR, HIPAA, and PCI DSS by protecting sensitive data and preventing its unauthorized disclosure.
  • Protecting intellectual property: DLP can help organizations protect their intellectual property by preventing employees from sharing confidential documents or trade secrets with unauthorized parties.
  • Monitoring data usage: DLP can provide insights into how sensitive data is being used within the organization, helping to identify potential risks and vulnerabilities.

Azure Information Protection (AIP)

AIP, now integrated into Microsoft Purview Information Protection, is a cloud-based solution that helps organizations classify, label, and protect sensitive data. It focuses on applying persistent protection to data, regardless of where it is stored or shared. AIP uses labels to classify data based on its sensitivity and then applies protection measures, such as encryption and access restrictions, based on the assigned label.

Key Features of AIP

  • Classification and Labeling: AIP allows organizations to classify data based on its sensitivity and apply labels to documents and emails. Labels can be applied manually by users or automatically based on content analysis.
  • Protection: AIP applies persistent protection to data based on the assigned label. This protection can include encryption, access restrictions, and usage rights.
  • Rights Management: AIP uses Azure Rights Management (Azure RMS) to control who can access and use protected data. Azure RMS allows organizations to define specific usage rights, such as the ability to view, edit, print, or forward a document.
  • Data Discovery: AIP can discover sensitive data across various locations, including on-premises file servers, SharePoint Online, and OneDrive for Business.
  • Tracking and Revocation: AIP allows organizations to track who is accessing protected data and revoke access if necessary.

Use Cases for AIP

  • Protecting sensitive data from unauthorized access: AIP can encrypt sensitive documents and emails, preventing unauthorized users from accessing the data.
  • Controlling data usage: AIP can restrict how users can use protected data, such as preventing them from printing or forwarding a document.
  • Ensuring data compliance: AIP can help organizations comply with regulatory requirements by protecting sensitive data and controlling its usage.
  • Sharing sensitive data securely: AIP allows organizations to share sensitive data with external parties while maintaining control over the data.