• views: 154
• date: 5 March 2026
• posted by: 0nelove

Protecting data and AI workloads in Google Cloud requires a comprehensive security strategy. This course will teach you how to implement encryption, access controls, and protect sensitive data across GCP services.
What you'll learn
Organizations face increasing challenges in protecting sensitive data and AI workloads in the cloud, from data breaches and compliance violations to unauthorized access and inadequate encryption.
In this course, Google Cloud Certified Professional Cloud Security Engineer: Ensuring Data Protection by Pluralsight, you'll gain the ability to implement comprehensive security controls across your GCP data infrastructure.
First, you'll explore how to discover, mask, and tokenize sensitive data while configuring granular access controls for BigQuery, Cloud Storage, and Cloud SQL.
Next, you'll discover how to manage encryption at rest, in transit, and in use by implementing CMEK, Cloud EKM, and Confidential Computing across your workloads.
Finally, you'll learn how to secure Vertex AI pipelines, protect training data and model artifacts, and distinguish security requirements between IaaS and PaaS machine learning environments.
When you're finished with this course, you'll have the skills and knowledge of Google Cloud security needed to protect sensitive data and AI workloads in enterprise environments.




   
   
   

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