D-SR-B-1: Are systematic verification procedures regularly conducted to ensure requirements are met?
Basic Data Provenance: Document initial sources of training data and maintain basic data lineage records.
Manual Tracking: Data provenance records are manually created and updated, with limited standardization or automation.
Limited Visibility: Partial visibility into third-party data and model components.
Description
Basic Data Provenance: Document initial sources of training data and maintain basic data lineage records.
Manual Tracking: Data provenance records are manually created and updated, with limited standardization or automation.
Limited Visibility: Partial visibility into third-party data and model components.
D-SR-B-2
D-SR-B-2: Are security requirements continuously improved and fully integrated across AI projects?
Automated Quality Checks: Automate validation processes for third-party datasets and AI models, including quality assurance and security assessments.
Enhanced Provenance Records: Automated maintenance of detailed data lineage and provenance documentation, ensuring traceability and accountability.
Structured Validation: Standardized criteria established for acceptance of third-party components.
Description
Automated Quality Checks: Automate validation processes for third-party datasets and AI models, including quality assurance and security assessments.
Enhanced Provenance Records: Automated maintenance of detailed data lineage and provenance documentation, ensuring traceability and accountability.
Structured Validation: Standardized criteria established for acceptance of third-party components.
D-SR-B-3
D-SR-B-3: Are comprehensive and proactive verification mechanisms consistently enforced and audited?
Real-Time Provenance Tracking: Real-time capture and automated management of comprehensive data and model provenance across all lifecycle stages, from initial sourcing through deployment.
Advanced Provenance Analytics: Integrate analytics to proactively detect anomalies, unauthorized changes, or potential security risks within data and model workflows.
Continuous Provenance Auditing: Automatically generate detailed audit trails, enabling immediate and transparent reporting for governance, compliance, and incident response.
Description
Real-Time Provenance Tracking: Real-time capture and automated management of comprehensive data and model provenance across all lifecycle stages, from initial sourcing through deployment.
Advanced Provenance Analytics: Integrate analytics to proactively detect anomalies, unauthorized changes, or potential security risks within data and model workflows.
Continuous Provenance Auditing: Automatically generate detailed audit trails, enabling immediate and transparent reporting for governance, compliance, and incident response.