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April 2021 Microsoft Security TechTalk

This webinar covers the principles of data classification in Microsoft 365 and how we use it to build a solid foundation for good data governance. The data classification process tags data by sensitivity and risk. When data is classified it becomes possible to secure it more effectively against loss, abuse or theft.

In this Microsoft Security TechTalk you’ll discover how data classification is put in practice using features like sensitivity labelling and the unified labelling platform, you will also learn how Data Loss Prevention (DLP) can be achieved using Microsoft Information Protection and Microsoft Cloud App Security. Finally, you’ll cover example scenarios that implement classification to achieve protection and governance strategies. You’ll come away with a new appreciation for the power of leveraging data classification within Microsoft’s platforms.

Agenda:

  1. Unified labelling in Microsoft 365
  2. Leveraging classifications for DLP
  3. Scenarios to apply classifications

About the Microsoft Security TechTalks:

These workshops are FREE to attend and are technical in nature. They are semi-structured webinars (including some slides), with a principle objective of learning and sharing our knowledge and experience.

All Microsoft Security TechTalks are delivered by qualified Microsoft Solutions Architects with over 10 years’ experience in the design and delivery of Microsoft infrastructure and security.

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About the presenter

Matt Smith, Microsoft Solutions Architect, Softwerx

Matt offers a very rare blend of deep-technical Microsoft insight combined with a natural and personable ability to communicate clearly at all levels. He has over 10 years’ experience in managed service delivery and project design of Microsoft infrastructure and security. Matt oversees the roadmap and feature definition for our services by employing a wealth of product knowledge.

This places him in a unique position to understand customer requirements and match these to product capabilities

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