AWS wellarchitected documentation change
Summary
Updated section title by removing 'and resolution'. Extended BP01 explanation with data/control plane failure analysis. Added two new best practices: BP02 for AI model versioning/rollback strategies and BP03 for specialized AI testing including adversarial testing and security controls.
Security assessment
The change adds explicit security documentation in BP03 through adversarial testing, access controls, and compliance validation requirements. However, there's no evidence of addressing a specific existing vulnerability. BP03's inclusion of security practices constitutes new security documentation.
Diff
diff --git a/wellarchitected/latest/financial-services-industry-lens/fsirel01.md b/wellarchitected/latest/financial-services-industry-lens/fsirel01.md index 34fd7e593..1cf3edadd 100644 --- a//wellarchitected/latest/financial-services-industry-lens/fsirel01.md +++ b//wellarchitected/latest/financial-services-industry-lens/fsirel01.md @@ -5 +5 @@ -FSIREL01-BP01 Treat your CI/CD tools as critical workload components for recovery +FSIREL01-BP01 Treat your CI/CD tools as critical workload components for recoveryFSIREL01-BP02 Implement AI model versioning and rollback strategiesFSIREL01-BP03 Add specialized AI system testing and validation to software testing methodology @@ -7 +7 @@ FSIREL01-BP01 Treat your CI/CD tools as critical workload components for recover -# FSIREL01: Have you planned for events that impact your software development infrastructure and challenge your recovery and resolution plans? +# FSIREL01: Have you planned for events that impact your software development infrastructure and challenge your recovery plans? @@ -13 +13,9 @@ Financial services institutions are increasingly relying on continuous integrati -If key elements of an SDLC environment, such as the CI/CD pipeline, are impacted, you might not be able to commit new code, change configurations, pull containers, or upload application artifacts, which can result in an outage of your workload. Understand the entire dependencies of your SDLC and plan for disruption of the critical components that the SDLC relies on. Consider replicating your SDLC environment and supporting services in another Region, which allows you to continually replicate source code, application, and container repositories. +If key elements of an SDLC environment, such as the CI/CD pipeline, are impacted, you might not be able to commit new code, change configurations, pull containers, or upload application artifacts, which can result in an outage of your workload. Understand the entire dependencies of your SDLC and plan for disruption of the critical components that the SDLC relies on. Consider replicating your SDLC environment and supporting services in another Region, which allows you to continually replicate source code, application, and container repositories. Based on the criticality of your workload, you should understand how your components interact with both the data plan and the control plan to understand what failures would cause service disruptions to your workload. + +## FSIREL01-BP02 Implement AI model versioning and rollback strategies + +Financial services institutions must establish formal AI model versioning and rollback capabilities to maintain operational resilience. Implement immutable model registries that preserve all model artifacts, training data characteristics, hyperparameters, and performance metrics for each version. Develop clear versioning conventions that include major and minor designations based on the significance of model changes. Establish automated deployment pipelines with built-in validation gates and rollback triggers based on predefined performance thresholds. Create comprehensive rollback procedures that include not just technical reversion steps but also business impact assessments, customer communication templates, and regulatory notification processes where required. Test rollback capabilities regularly as part of disaster recovery exercises. + +## FSIREL01-BP03 Add specialized AI system testing and validation to software testing methodology + +Effective AI system testing and validation requires a multi-layered approach beyond traditional software testing methodologies. Establish separate development, testing, and production environments with appropriate data separation and access controls. Implement comprehensive testing regimes including unit tests for individual components, integration tests for system interactions, and holistic validation with representative data, prompt and response testing, and human-in-the-loop evaluations that provide qualitative checks for grounding, tone, and policy compliance. For critical financial applications, conduct adversarial testing to identify potential vulnerabilities and edge cases. Validation should include fairness and bias assessments, particularly for consumer-facing applications where regulatory adherence is essential. Document all testing procedures, results, and remediation actions to support audit requirements and regulatory examinations.