Intermediate

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

Assessment Area Key Questions Documentation Required
Purpose and Scope What decisions does the AI make? Who is affected? What are the consequences of errors? System description, use case specification
Data Quality Is training data representative? Are there known biases? How is data quality maintained? Datasheet, bias audit report
Performance What are the accuracy metrics? How does performance vary across subgroups? Model card, disaggregated evaluation
Human Oversight Can humans understand, monitor, and override AI decisions? Are there escalation procedures? Oversight procedures, interface documentation
Security Is the system protected against adversarial attacks? What are the cybersecurity measures? Security assessment, penetration test results

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

Internal Audits

Regular self-assessments by the development team or internal audit function. Should cover technical compliance, documentation completeness, and process adherence.

External Audits

Independent third-party assessments for high-risk systems. Required by some regulations and adds credibility to compliance claims.

Conformity Assessments

Formal evaluation required by the EU AI Act for high-risk systems. Can be self-assessment or third-party, depending on the risk category.

Continuous Monitoring

Ongoing automated checks on model performance, fairness metrics, and security posture. Alerts when metrics drift outside acceptable bounds.

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Start Early: Do not wait for regulations to take effect. Building compliance infrastructure now — documentation templates, risk assessment processes, monitoring dashboards — is far easier than retrofitting compliance onto existing systems under deadline pressure.