EU AI Act
Implement EU AI Act compliance using ValidMind.
Follow this detailed, step-by-step guide which will:
Walk you through the practical steps required to implement end-to-end compliance using ValidMind.
Link to supporting documentation and training for specific product features.
Focus on actionable implementation rather than just explaining requirements.
Overview
The EU AI Act1 establishes requirements across six key areas for high-risk AI systems. This guide is organized around these requirements to help you set up ValidMind for compliance.
1 EUR-Lex:
EU AI Act (Regulation (EU) 2024/1689)
Quick reference to risk classifications
The EU AI Act categorizes AI systems by risk level:
Prohibited — AI systems that pose unacceptable risks, such as social scoring or real-time biometric identification in public spaces.
High-risk — AI systems in critical areas requiring strict compliance, such as employment, credit scoring, or law enforcement.
Limited risk — AI systems with transparency obligations, such as chatbots or emotion recognition.
Minimal risk — AI systems with no specific requirements.
Harmonization with other standards
The EU AI Act requirements overlap with existing model risk management frameworks. Organizations already following SR 11-7, SS1/23, or E-23 can leverage existing documentation and controls but must also comply with the EU AI Act requirements.
1. Risk management system implementation (Article 9)
Purpose
Set up a risk management system in the ValidMind Platform to identify, evaluate, and mitigate risks at all stages of the AI system’s lifecycle.
Steps
Complete the risk classification questionnaire:
- Configure custom inventory fields2 to capture EU AI Act risk classification.
- Set up fields for prohibited, high-risk, limited-risk, and minimal-risk categories.
Map fields to EU AI Act requirements:
- Create custom fields aligned to Article 9 requirements.
- Document risk assessment criteria and thresholds.
Set up custom workflows3 for different risk levels:
- Configure approval workflows based on risk classification.
- High-risk systems require additional review gates.
2. Data governance setup (Article 10)
Purpose
Implement data quality controls and bias detection to meet Article 10 requirements for training, validation, and testing data.
Steps
Initialize the ValidMind Library:4
- Set up the developer environment for data quality testing.
- Connect to your data sources.
Implement out-of-the-box data quality tests:5
- Run automated tests for data completeness, accuracy, and relevance.
- Document data quality metrics.
Create custom tests for your specific data requirements:
- Extend the test framework for domain-specific quality checks.
- Implement bias detection tests.
Centralize governance for oversight:
- Configure dashboards to monitor data quality across systems.
- Set up alerts for data quality issues.
3. Technical documentation management (Article 11)
Purpose
Generate and customize required model documentation to meet Article 11 requirements for technical documentation.
Steps
Work with documentation templates:6
- Select or create templates aligned to EU AI Act requirements.
- Configure sections for system description, design, and compliance.
Discover and use tests:7
- Map available tests to Article 11 documentation requirements.
- Run tests to generate evidence
Customize sections for specific use cases:
- Add custom sections for AI system-specific requirements.
- Document intended purpose, design choices, and limitations.
Leverage AI-assisted documentation features:
- Use document generation to accelerate documentation.
- Review and refine AI-generated content.
Add content blocks:8
- Insert additional evidence and documentation, as needed.
- Link artifacts to documentation sections.
4. Accuracy and robustness validation (Article 15)
Purpose
Validate model accuracy and demonstrate robustness to meet Article 15 requirements.
Steps
Prepare validation reports:9
- Set up validation report templates
- Configure validation workflows
Link evidence to compliance requirements:
- Map test results to Article 15 requirements
- Document accuracy metrics and thresholds
Create and document challenger models:
- Implement challenger model workflows
- Compare performance across models
Record findings and remediations:10
- Track validation findings
- Document remediation actions
Assess compliance:
- Generate compliance summaries
- Review against Article 15 criteria
5. Transparency implementation (Article 13)
Purpose
Set up transparent reporting and decision explanations to meet Article 13 requirements.
Steps
Work with analytics11 for non-technical stakeholders:
- Configure dashboards for business users
- Create accessible summaries of AI system behavior
Set up custom dashboards:12
- Build dashboards showing key transparency metrics
- Include interpretability information
Set up decision explanation frameworks:
- Document how AI systems make decisions
- Implement explainability features
6. Human oversight configuration (Article 14)
Purpose
Implement human oversight capabilities to meet Article 14 requirements.
Steps
Configure ongoing monitoring:13
- Set up monitoring workflows
- Define monitoring metrics and thresholds
Set up alerts and thresholds:
- Configure alerts for performance degradation
- Establish escalation procedures
Create human review workflows:
- Implement review gates for high-risk decisions
- Document human-in-the-loop processes
Document intervention processes:
- Define procedures for human override
- Track intervention decisions
7. Compliance workflow implementation
Purpose
Integrate all components into a complete compliance workflow addressing Articles 43, 60, 61, and 62.
Steps
Set up the conformity assessment process (Article 43):
Register high-risk systems (Article 60):16
- Maintain EU database registration information
- Track registration status
Implement post-market monitoring (Article 61):17
- Set up ongoing monitoring for deployed systems
- Configure incident detection
Establish incident reporting procedures (Article 62):
- Define incident reporting workflows
- Document escalation procedures