Domain 5: Security, Compliance, and Governance for AI Solutions
Securing AI systems, data governance, and regulatory compliance on AWS.
This domain covers 14% of the exam. It focuses on AWS services and features to secure AI systems (IAM, encryption, Amazon Macie, AWS PrivateLink, shared responsibility model), source citation and data lineage, secure data engineering best practices, security considerations for AI systems (prompt injection, encryption at rest and in transit, threat detection), regulatory compliance standards (ISO, SOC, algorithm accountability laws), AWS compliance services (AWS Config, Inspector, Audit Manager, Artifact, CloudTrail, Trusted Advisor), data governance strategies (lifecycles, logging, residency, monitoring, retention), and governance frameworks such as the Generative AI Security Scoping Matrix.
5 minutes
5 Questions
Domain 5: Security, Compliance, and Governance for AI Solutions is a critical component of the AWS Certified AI Practitioner (AIF-C01) exam, focusing on how organizations can responsibly and securely deploy AI/ML workloads on AWS.
**Security** encompasses protecting AI systems, data, and models from unauthorized access and threats. This includes understanding AWS shared responsibility model as applied to AI services, implementing encryption for data at rest and in transit, securing training data and model artifacts using services like AWS KMS, IAM policies, and VPC configurations. Candidates must know how to prevent data leakage, model theft, and adversarial attacks on AI systems.
**Compliance** addresses regulatory and legal requirements surrounding AI implementations. This includes understanding frameworks like GDPR, HIPAA, and industry-specific regulations that impact AI data handling. AWS services such as AWS Artifact, AWS Audit Manager, and AWS Config help organizations demonstrate compliance. Candidates should understand data residency requirements, data lineage tracking, and how to maintain audit trails for AI/ML pipelines.
**Governance** focuses on establishing policies, processes, and oversight mechanisms for AI solutions. This includes responsible AI practices such as fairness, transparency, explainability, and bias detection. AWS provides tools like Amazon SageMaker Clarify for bias detection and model explainability, and Amazon SageMaker Model Monitor for tracking model drift. Governance also covers model versioning, approval workflows, and lifecycle management.
Key topics include:
- **Data privacy**: Anonymization, pseudonymization, and PII handling
- **Access control**: Least privilege principles for AI resources
- **Monitoring and logging**: CloudTrail, CloudWatch for AI workloads
- **Responsible AI**: Addressing bias, toxicity, and hallucinations in generative AI
- **Guardrails**: Using Amazon Bedrock Guardrails to filter harmful content
This domain emphasizes that AI solutions must be built with security-first principles while maintaining ethical standards and meeting organizational governance requirements throughout the entire AI lifecycle.Domain 5: Security, Compliance, and Governance for AI Solutions is a critical component of the AWS Certified AI Practitioner (AIF-C01) exam, focusing on how organizations can responsibly and securely deploy AI/ML workloads on AWS.
**Security** encompasses protecting AI systems, data, and models fr…
