Identifying Security Requirements of an AI Solution

In this course, you will walk through the services used to create an AI solution in Azure and the requirements to secure the solution, including data security and deployment security of Machine Learning models and Cognitive Services.
Course info
Level
Beginner
Updated
Dec 12, 2019
Duration
1h 28m
Table of contents
Description
Course info
Level
Beginner
Updated
Dec 12, 2019
Duration
1h 28m
Description

Learn what it takes to secure your newly developed and/or deployed AI solution in Microsoft Azure. In this course, Identifying Security Requirements of an AI solution, you will learn foundational knowledge to secure your AI Solution no matter whether you are leveraging Azure Cognitive Services or custom machine learning models. First, you will discover how to leverage Azure Cognitive Services in a secure authenticated way. Next, you will learn what security options are available for any test data that you need to train the models of both Cognitive Services and machine learning models. Finally, you will explore how to secure the deployment of an AI solution using machine learning models. When you are finished with this course, you will have the skills and knowledge of security options within Azure Services needed to secure an AI solution from development to deployment.

About the author
About the author

Brian is currently a Cloud Solution Architect with Microsoft.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Brian Harrison, and I am a cloud solution architect. Welcome to my course, Identifying Security Requirements for an AI Solution in Azure. This course will help you understand what questions you need to ask when thinking about the security controls that you need to put in place within Azure when deploying an AI solution. You will start by focusing on Azure Cognitive Services and then learning what the different authentication methods are for those services. Then we switch to the data that is leveraged by both cognitive services and machine learning to either help train the models or to even use as part of the AI solution and how can you secure that data depending upon which data services in Azure are being used. We will then finish up by talking about the AI solution deployment as a whole and the different use cases that could drive many different security requirements such as network boundaries and web application firewalls. Some of the major topics that we will cover include authentication methods for cognitive services and machine learning model deployments, different security options for data being used in combination with your models, and lastly, a holistic view of AI solution deployments and the security requirements that need to be discussed as part of that. By the end of this course, you should be able to talk through the architecture of an AI solution in Azure and answer any questions that may arise about what security controls may need to be put in place for that deployment.