AI World 1 · GenAI Foundations · Stage 6

Govern It

Roles not keys, logs the auditors can read, a Model Card, a Clarify report, a human in the loop, and Macie on the bucket.

IAM roles vs API keys
Model Cards
SageMaker Clarify
Augmented AI
Macie

Domain: AI and machine learning
World: AI World 1: GenAI Foundations (Associate)
Game type: Evidence desk: Each ticket is the reporter's words: find its cause in the evidence.
Time: About 30 minutes on a first attempt (estimated)
Exam guides: AIF-C01Task 4.1: Explain the development of AI systems that are responsible; Task 4.2: Recognize the importance of transparent and explainable models; Task 5.1: Explain methods to secure AI systems; Task 5.2: Recognize governance and compliance regulations for AI systems
MLA-C02Skill 1.3.3: Identify and mitigate sources of bias in data by using AWS tools and techniques (for example, dataset splitting, shuffling, augmentation); Skill 2.3.4: Explain model outputs; Skill 4.3.2: Configure least privilege access to ML and AI artifacts; Skill 4.3.3: Configure IAM policies and roles for users and applications in ML and AI systems; Skill 4.3.4: Configure comprehensive monitoring, auditing, compliance, and logging for ML and AI systems (for example, AWS CloudTrail, AWS Config); Skill 4.3.8: Select the appropriate credential type to access FMs (for example, Amazon Bedrock API keys, IAM credentials)
AIP-C01Skill 2.3.3: Create secure access frameworks to ensure appropriate security controls (for example, by using identity federation between FM services and enterprise systems, role-based access control for model and data access, least privilege API access to FMs); Skill 3.2.2: Develop privacy-preserving systems to protect sensitive information during FM interactions (for example, by using Amazon Comprehend and Amazon Macie to detect personally identifiable information [PII], Amazon Bedrock native data privacy features, Amazon Bedrock guardrails to filter outputs); Skill 3.3.1: Develop compliance frameworks to ensure regulatory compliance for FM deployments (for example, by using SageMaker AI to develop programmatic model cards, AWS Glue to automatically track data lineage, metadata tagging for systematic data source attribution, CloudWatch Logs to collect comprehensive decision logs); Skill 3.3.2: Implement data source tracking to maintain traceability in GenAI applications (for example, by using AWS Glue Data Catalog to register data sources, metadata tagging for source attribution in FM-generated content, CloudTrail for audit logging); Skill 3.4.1: Develop transparent AI systems in FM outputs (for example, by using reasoning displays to provide user-facing explanations, CloudWatch to collect confidence metrics and quantify uncertainty, evidence presentation for source attribution); Skill 3.4.3: Develop policy-compliant AI systems to ensure adherence to responsible AI practices (for example, by using Amazon Bedrock guardrails based on policy requirements, model cards to document FM limitations, Lambda functions to perform automated compliance checks)