Content Domain 3: AI Safety, Security, and Governance
Task 3.1: Implement input and output safety controls.
Skill 3.1.1: Develop comprehensive content safety systems to protect against harmful user inputs to FMs (for example, by using Amazon Bedrock guardrails to filter content, Step Functions and Lambda functions to implement custom moderation workflows, real-time validation mechanisms).
Skill 3.1.2: Create content safety frameworks to prevent harmful outputs (for example, by using Amazon Bedrock guardrails to filter responses, specialized FM evaluations for content moderation and toxicity detection, text-to-SQL transformations to ensure deterministic results).
Skill 3.1.3: Develop accuracy verification systems to reduce hallucinations in FM responses (for example, by using Amazon Bedrock Knowledge Base to ground responses and perform fact-checking, confidence scoring and semantic similarity search for verification, JSON Schema to enforce structured outputs).
Skill 3.1.4: Create defense-in-depth safety systems to provide comprehensive protection against FM misuse (for example, by using Amazon Comprehend to develop pre-processing filters, Amazon Bedrock to implement model-based guardrails, Lambda functions to perform post-processing validation, API Gateway to implement API response filtering).
Skill 3.1.5: Implement advanced threat detection to protect against adversarial inputs and security vulnerabilities (for example, by using prompt injection and jailbreak detection mechanisms, input sanitization and content filters, safety classifiers, automated adversarial testing workflows).
Task 3.2: Implement data security and privacy controls.
Skill 3.2.1: Develop protected AI environments to ensure comprehensive security for FM deployments (for example, by using VPC endpoints to isolate networks, IAM policies to enforce secure data access patterns, AWS Lake Formation to provide granular data access, CloudWatch to monitor data access).
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, Amazon S3 Lifecycle configurations to implement data retention policies).
Skill 3.2.3: Create privacy-focused AI systems to protect user privacy while maintaining FM utility and effectiveness (for example, by using data masking techniques, Amazon Comprehend PII detection, anonymization strategies for sensitive information, Amazon Bedrock guardrails).
Task 3.3: Implement AI governance and compliance mechanisms.
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 3.3.3: Create organizational governance systems to ensure consistent oversight of FM implementations (for example, by using comprehensive frameworks that align with organizational policies, regulatory requirements, and responsible AI principles).
Skill 3.3.4: Implement continuous monitoring and advanced governance controls to support safety audits and regulatory readiness (for example, by using automated detection for misuse, drift, and policy violations, bias drift monitoring, automated alerting and remediation workflows, token-level redaction, response logging, AI output policy filters).
Task 3.4: Implement responsible AI principles.
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, Amazon Bedrock agent tracing to provide reasoning traces).
Skill 3.4.2: Apply fairness evaluations to ensure unbiased FM outputs (for example, by using pre-defined fairness metrics in CloudWatch, Amazon Bedrock Prompt Management and Amazon Bedrock Prompt Flows to perform systematic A/B testing, Amazon Bedrock with LLM-as-a-judge solutions to perform automated model evaluations).
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).
