Secure sensitive data with Microsoft Purview Ninja training scenarios
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Welcome to the Microsoft Purview Information Protection and Data Loss Prevention Ninja Scenario guide: a comprehensive resource designed to unify the core principles of information protection with advanced data loss prevention strategies. This guide builds on established best practices by presenting five carefully selected real-world scenarios that matter most to our customers and users. Our objective is to simplify the resolution of common information security challenges, offering a structured path with clear, actionable guidance and references to deepen your understanding. Upon completion of this training, you won't only gain valuable insights but also earn a badge recognizing your expertise and commitment to safeguarding organizational data.
Prerequisites
- Basic knowledge of data protection and security
- Demonstrates an L100-200 level understanding of Microsoft Purview Information Protection and Microsoft Purview Data Loss Prevention.
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Modules in this learning path
Microsoft Purview Data Loss Prevention (DLP) protects sensitive data by identifying and classifying confidential information across platforms like email, Teams, SharePoint, and OneDrive. In this module, you explore how DLP uses built-in Sensitive Information Types (SITs), custom SITs, exact data match (EDM), fingerprinting, and trainable classifiers, while learning to test and tune detection policies for optimal results. You also gain insight into strategies for continuous improvement, ensuring policies adapt to evolving organizational and regulatory needs.
Labeling sensitive data ensures that confidential information is consistently classified and protected according to organizational policies, regardless of where it's stored or with whom it's shared. This is important because it helps prevent unauthorized access, supports regulatory compliance, and maintains data security during routine operations and collaborative activities.
Security Copilot in Microsoft Purview accelerates investigations by automating alert triage and providing AI-driven insights that help teams quickly uncover root causes and apply remediation actions. This integration streamlines review cycles, ensuring consistent and accurate data activity analysis while enabling information protection teams to focus on strategic security initiatives.
Preventing data loss through risky sharing or transfers is critical because unauthorized exposure of sensitive information can lead to severe legal, financial, and reputational consequences for organizations. By proactively securing data exchanges, companies protect their clients, maintain compliance with regulations, and reinforce trust in their business operations.
Safeguarding data during remote work means implementing security measures and policies to protect sensitive information as employees access and share data outside the traditional office environment. It involves monitoring and controlling data transfers, blocking unauthorized sharing, and ensuring compliance to minimize the risk of accidental or intentional data exposure.