AI Security and Governance on AWS

Learn to design and implement comprehensive AI security architectures on AWS using Bedrock guardrails, CloudTrail auditing, and responsible AI practices. You will explore defense-in-depth security architecture across five scopes from consumer apps to self-trained models, following frameworks developed by AWS Security Specialists. The course covers IAM-based authentication patterns for AI service access, role-based authorization for Bedrock endpoints, and complete security architecture integrating identity, network, and application controls. You will implement continuous monitoring and logging for AI workloads using CloudTrail to create audit trails for every Bedrock API invocation, and build CloudTrail visualizations that reveal usage patterns and anomalies. The Bedrock guardrails module covers configurable safety controls including content filters, PII detection, and topic controls with real-time content classification at multiple severity levels. You will configure both input validation and output safety controls, define security boundaries, and test guardrails against adversarial edge cases. The course also covers Amazon Q security with authentication, data protection, and compliance monitoring, and SageMaker Clarify for bias detection, model explainability, and responsible AI governance. By completing this course, you will be able to design secure AI architectures, implement Bedrock guardrails for content safety, and apply responsible AI practices using SageMaker Clarify.