Modular System Layers, MLOps Flow & Security/Data Governance
This document details the modular architecture, MLOps lifecycle, and security/data governance design for ShieldCraft AI.
1. Modular System Architecture & MLOps Diagram
Figure: Modular MLOps flow showing data, model, and governance layers
Benefits
2. MLOps Flow
- Automated Pipelines:
- Data ingestion, validation, and versioning
- Model training, evaluation, and registry (SageMaker/MLflow)
- Continuous integration and deployment (CI/CD) for models and code
- Canary and shadow deployments for safe model updates
- Experiment Tracking:
- All experiments logged with parameters, metrics, and artifacts
- Rollback and audit trails for all model changes
- Monitoring & Feedback:
- Automated monitoring for drift, performance, and cost
- Human-in-the-loop feedback for prompt/model improvement
3. Security & Data Governance
- Data Privacy:
- Masking, anonymization, and minimization at ingestion and processing
- Data retention and deletion policies
- Regular privacy impact assessments (see doc)
- Access Control:
- IAM roles and least-privilege access
- Centralized secrets management (see doc)
- Audit logging for all access and changes
- Compliance:
- Automated checks for GDPR, SOC2, POPIA, etc.
- Documentation and evidence collection for audits
- Regular reviews and updates (see doc)
- Security Posture:
- Threat modeling and adversarial testing (see doc)
- Automated vulnerability scanning in CI/CD
- Incident response plan and runbooks