Responsible AI on AWS: Bedrock Guardrails, Amazon Q Security, and SageMaker Clarify
1h 1mIntermediate2025-03-12
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO

Pragmatic AI Labs
Course details
Explore the cutting-edge security features of Amazon's AI services, including Bedrock, Amazon Q, and SageMaker Clarify. MLOps expert Noah Gift shows you how to implement a comprehensive security architecture that integrates multiple layers of protection. Discover methods to enforce the principle of least privilege through IAM roles and resource policies, while also using CloudTrail and CloudWatch for real-time monitoring and detailed auditing. Gain insights into advanced bias detection and model explainability with SageMaker Clarify. Learn how to configure Bedrock’s guardrails for robust content filtering and validation to prevent inappropriate or harmful outputs. Enhance your understanding of security boundaries, anomaly detection, and automated security responses to maintain the integrity and confidentiality of your AI applications. By the end of this course, you will secure AI workflows, enhance performance monitoring, and ensure compliance with industry standards.
Skills covered
Amazon BedrockAmazon SageMakerResponsible AICloud DevelopmentAmazon Web Services (AWS)AmazonGenerative AICloud ServicesArtificial Intelligence (AI)Cloud ComputingOne-Off
Concepts
Introduction to AI Security
- 01 - Course introduction
- 02 - AI security architecture
- 03 - AI auth patterns
- 04 - Complete AI security
- 05 - AI monitoring and logging
- 06 - AWS Rust compilation
- 07 - Monitoring Bedrock calls
- 08 - Visualizing Bedrock API calls
1. Guardrail Fundamentals
- 09 - Amazon Bedrock Guardrails overview
- 10 - Amazon Bedrock input validation and tagging
- 11 - Bedrock output safety - Controls and methods
- 12 - Amazon Bedrock security - Guardrails deep dive
- 13 - Amazon Bedrock Guardrails - Handling edge cases
2. Enterprise Security
- 14 - Amazon Q security
3. Responsible AI Monitoring
- 15 - Responsible AI with SageMaker Clarify
Conclusion
- 16 - Course summary