AI Compliance

Practical guidance for achieving and maintaining compliance with AI regulations. Learn how to conduct risk assessments, build documentation, perform audits, and prepare for conformity assessments.

Compliance Framework

1. Step 1: AI Inventory

   Catalog all AI systems in your organization. Document what each system does, what data it uses, who it affects, and what decisions it influences.

2. Step 2: Risk Classification

   Classify each AI system according to applicable regulatory frameworks. Determine if it falls under prohibited, high-risk, limited-risk, or minimal-risk categories.

3. Step 3: Gap Analysis

   Compare current practices against regulatory requirements. Identify gaps in documentation, testing, monitoring, and governance.

4. Step 4: Remediation

   Develop and execute a remediation plan to close identified gaps. Prioritize based on risk level and regulatory deadlines.

5. Step 5: Ongoing Compliance

   Establish continuous monitoring, regular audits, and processes for managing changes to AI systems and regulatory requirements.

Risk Assessment

Documentation Requirements

Comprehensive documentation is the backbone of AI compliance. Key documents include:

• Technical documentation: System architecture, algorithms, training methods, data processing, and performance benchmarks
• Risk management plan: Identified risks, mitigation strategies, residual risks, and risk monitoring procedures
• Data governance records: Data sources, collection methods, processing steps, bias assessments, and data quality metrics
• Testing and validation reports: Test methodologies, results, including fairness and robustness testing
• Instructions for use: Clear guidance for deployers on intended use, limitations, and human oversight requirements
• Monitoring plan: Metrics to track, alerting thresholds, review frequency, and incident response procedures

Auditing AI Systems