https://store-images.s-microsoft.com/image/apps.45581.81bc53b4-b500-4e11-bb56-133bd4d89cbc.c7a2bc4b-5ac7-451e-bd05-318937588c90.17ab875b-7993-4f46-9061-5341e84b64aa

Security Assessment for Copilot Readiness: 3 Day Workshop

Netwoven

Netwoven's security assessment for Copilot workshop will train you to better discover, secure, and govern your corporate data for usage with Copilot for Microsoft 365

In today's data-driven world, organizations are increasingly turning to generative AI to unlock new insights and drive innovation. However, the potential benefits of generative AI come with inherent risks, as these models can be vulnerable to misuse and data breaches.

In this essential workshop, you'll learn how to harness the power of Copilot for Microsoft 365 while safeguarding your sensitive corporate data. Our experienced instructors will guide you through the process of discovering, securing, and governing your data, ensuring that it remains protected and compliant with your organization's policies. You'll gain valuable insights into data discovery techniques, data security best practices, and data governance strategies, empowering you to confidently leverage Copilot for Microsoft 365 for enhanced productivity without compromising data integrity.

Whether you're a data security professional, IT administrator, or business user, this workshop will equip you with the knowledge and skills to effectively manage corporate data in the era of intelligent automation

Workshop Agenda

Day 1

Introduction to Generative AI and Copilot for Microsoft 365

Master data discovery techniques to identify and locate relevant data sources for Copilot for Microsoft 365

Day 2

Implement robust data security measures to protect sensitive information from unauthorized access and misuse

Day 3

Establish comprehensive data governance policies to ensure ethical and compliant use of data for Copilot readiness

概览

https://store-images.s-microsoft.com/image/apps.45002.81bc53b4-b500-4e11-bb56-133bd4d89cbc.c7a2bc4b-5ac7-451e-bd05-318937588c90.67b22572-263c-4b74-9cb4-35bc1ff80ce1