EU AI Act Compliance for IT, Developers & Administrators Training Course

Foundations: EU AI Act for Government Technical Teams

• Key obligations and terminology relevant to developers and operators
• Understanding prohibited practices under Article 4 from a technical perspective
• Mapping legal requirements to engineering controls for government compliance

Secure and Compliant Development Lifecycle for Government AI Projects

• Repository structure and policy-as-code implementation for AI projects
• Code review processes and automated static checks to identify risky patterns
• Dependency and supply-chain management for model components to ensure security

CI/CD Pipeline Design for Government Compliance

• Pipeline stages: build, test, validation, package, deploy
• Integrating governance gates and automated policy checks into the pipeline
• Ensuring artifact immutability and tracking provenance for audit purposes

Model Testing, Validation, and Safety Checks for Government AI Systems

• Data validation and bias detection tests to ensure fairness and accuracy
• Performance, robustness, and adversarial resilience testing to maintain reliability
• Automated acceptance criteria and test reporting for transparency and accountability

Model Registry, Versioning, and Provenance Management for Government AI Models

• Using MLflow or equivalent tools for model lineage and metadata tracking
• Versioning models and datasets to ensure reproducibility in government applications
• Recording provenance and producing audit-ready artifacts for compliance

Runtime Controls, Monitoring, and Observability for Government AI Systems

• Instrumentation for logging inputs, outputs, and decision-making processes
• Continuous monitoring of model drift, data drift, and performance metrics
• Implementing alerting mechanisms, automated rollback procedures, and canary deployments

Security, Access Control, and Data Protection for Government AI

• Least-privilege Identity and Access Management (IAM) for model training and serving environments
• Protecting training and inference data with robust security measures both at rest and in transit
• Effective secrets management and secure configuration practices to prevent unauthorized access

Auditability and Evidence Collection for Government AI Systems

• Generating machine-readable logs and human-readable summaries for audit purposes
• Packaging evidence for conformity assessments and audits to ensure compliance
• Implementing retention policies and secure storage of compliance artifacts

Incident Response, Reporting, and Remediation for Government AI Systems

• Detecting suspected prohibited practices or safety incidents in government AI systems
• Technical steps for containment, rollback, and mitigation of identified issues
• Preparing detailed technical reports for governance and regulatory bodies

Summary and Next Steps for Government AI Projects