Intermediate

AI Audit Framework

A structured audit framework ensures consistent, comprehensive, and repeatable AI assessments. This lesson covers the end-to-end audit process from planning through follow-up.

Audit Lifecycle

  1. Planning and Scoping

    Define the audit objectives, scope, criteria, and timeline. Identify the AI system to be audited, the specific dimensions to evaluate (performance, fairness, security, compliance), and the stakeholders who will receive the findings.

  2. Evidence Collection

    Gather documentation, source code, training data metadata, model artifacts, deployment configurations, and operational logs. Conduct interviews with developers, operators, and users. Request access to testing environments.

  3. Analysis and Testing

    Execute audit tests including performance benchmarks, fairness metric calculations, security assessments, and compliance checks. Compare findings against audit criteria and industry benchmarks.

  4. Findings and Recommendations

    Document findings with severity classifications. Provide actionable recommendations for each finding. Distinguish between critical issues requiring immediate action and improvement opportunities.

  5. Reporting

    Produce a comprehensive audit report with executive summary, detailed findings, evidence references, and prioritized recommendations. Tailor report sections for different audiences (executive, technical, compliance).

  6. Follow-Up

    Track remediation of findings. Conduct follow-up assessments to verify that recommendations have been implemented. Update the audit plan based on findings and emerging risks.

Audit Scoping Dimensions

Dimension Scope Questions Evidence Required
Data What data was used? How was it collected, labeled, and validated? Data documentation, provenance records, quality reports
Model What architecture? How was it trained, validated, and selected? Model cards, training logs, validation results
Deployment How is it deployed? What monitoring is in place? Infrastructure configs, monitoring dashboards, incident logs
Governance Who approved? What reviews were conducted? Approval records, review minutes, risk assessments
Impact Who is affected? What decisions does it influence? Impact assessments, user research, complaint records

Audit Criteria and Standards

Regulatory Requirements

Map specific regulatory obligations (EU AI Act, NYC LL144, SR 11-7) to audit criteria. Verify that the AI system meets all applicable legal requirements for its risk classification and deployment context.

Industry Standards

Benchmark against ISO/IEC 42001, NIST AI RMF, IEEE standards, and sector-specific guidelines. These provide recognized frameworks for evaluating AI system quality and governance.

Internal Policies

Verify compliance with the organization's own AI policies, ethical principles, and governance requirements. Internal standards often exceed regulatory minimums.

Technical Benchmarks

Evaluate against performance benchmarks, fairness thresholds, and security baselines appropriate for the AI system's domain and risk level.

💡
Next Up: In the next lesson, we dive into technical auditing — evaluating model performance, data quality, security controls, and deployment infrastructure.