Lilly Tech Systems
Cloud AI Security
Secure your AI workloads across AWS, GCP, and Azure. Master IAM policies for ML services, VPC endpoint configuration, data encryption at rest and in transit, audit trails, and cloud-specific compliance controls for AI deployments.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Cloud AI security fundamentals, shared responsibility model for ML, and the unique risks of cloud-hosted AI services.
2. AWS AI Security
SageMaker security, IAM for Bedrock, VPC endpoints, KMS encryption, CloudTrail for ML, and AWS AI compliance controls.
3. GCP AI Security
Vertex AI security, IAM for AI Platform, VPC Service Controls, CMEK encryption, and Cloud Audit Logs for ML.
4. Azure AI Security
Azure ML security, Managed Identity, Private Endpoints, Key Vault, Azure Monitor for ML, and compliance certifications.
5. Multi-Cloud AI Security
Cross-cloud identity federation, unified policy management, data residency, and multi-cloud AI governance strategies.
6. Best Practices
Cloud AI security checklist, zero trust for ML, cost-security tradeoffs, and production hardening patterns.
What You'll Learn
By the end of this course, you'll be able to:
Configure IAM for ML
Design least-privilege IAM policies for ML services across AWS, GCP, and Azure with proper role separation.
Encrypt AI Data
Implement encryption at rest and in transit for training data, model artifacts, and inference requests on every major cloud.
Audit AI Operations
Configure comprehensive audit trails for all ML operations using CloudTrail, Cloud Audit Logs, and Azure Monitor.
Achieve Compliance
Map cloud AI security controls to regulatory requirements including GDPR, HIPAA, SOC 2, and industry-specific standards.