AI Audit Checklist By: Kamran Iqbal

📘 What’s Covered

The checklist is structured around clearly defined audit domains, each containing multiple diagnostic questions with suggested review methods. It serves both as a self-assessment tool and as a baseline for professional AI audits. Core sections include:

1. AI Governance & Compliance

Covers whether an organization has a documented AI governance framework, risk management aligned with ISO 42001, NIST AI RMF, and GDPR, and oversight from an AI ethics committee. The section emphasizes integrating AI oversight with regulatory frameworks and enterprise risk management.

2. Bias, Fairness, and Transparency

The checklist goes into substantial detail on bias mitigation, including:

• Use of fairness metrics like Equalized Odds and Statistical Parity
• Application of bias auditing tools like IBM AI Fairness 360 and Fairlearn
• Requirements for human-in-the-loop oversight and user appeal mechanisms in high-risk applications such as hiring or lending

3. Security and Adversarial Risk

Audit items here address:

• Access controls (RBAC, MFA)
• Encryption (AES-256, TLS)
• Adversarial robustness and penetration testing with tools like Microsoft Counterfit
• AI-specific anomaly detection and drift monitoring tools such as Evidently AI and Azure ML Monitor

4. Explainability and Interpretability

Assesses whether explainability techniques (SHAP, LIME, Integrated Gradients) are used, and whether systems meet requirements under laws like GDPR’s “right to explanation.” It includes checks for user comprehension, auditor dashboards, and transparency in model decision pathways .

5. Post-Deployment Monitoring

Audit items include model drift detection, retraining schedules, and tools for anomaly and bias monitoring post-launch. There’s emphasis on stakeholder communication and the documentation of corrective actions after system failures or ethical concerns .

6. AI Audit Reporting

This section outlines best practices for structuring audit reports, categorizing risks, and mapping issues to standards (GDPR, ISO 42001, EU AI Act). It encourages data-driven reporting, visualization of fairness metrics, and continuous monitoring planning .

💡 Why it matters?

Most AI governance guidance today is either highly conceptual or legally oriented. This resource fills the operational gap by offering a field-ready checklist that can be immediately used by auditors, internal compliance teams, or AI governance leads. The format makes it easy to apply across various industries and system risk levels. Importantly, it aligns with internationally recognized frameworks (ISO, NIST, EU AI Act), which makes it particularly useful for organizations seeking cross-border compliance or preparing for regulatory audits.

❗ What’s Missing

• No scoring rubric: While it provides detailed “yes/no” checks, it lacks a maturity model or scoring mechanism for benchmarking.
• Limited sectoral granularity: There’s no tailoring for sensitive verticals like healthcare or finance beyond standard compliance cues.
• No integration guidance: While it names standards and tools, it doesn’t offer process diagrams or operational workflows to help integrate them into daily audits.

👍 Best For

• AI auditors and internal compliance teams
• Legal and risk teams preparing for AI Act or GDPR alignment
• Enterprises conducting internal readiness assessments
• Researchers exploring real-world AI accountability methods

📚 Source Details

Title: AI Audit Checklist

Author: Kamran Iqbal (CIA, CISA, CFE, CMA, LLB, MBA)

Publisher: Certified Trainers and Consultants (CTC Global)

Date: 2024

Format: PDF, 8 pages

Link: ctc-global.com

Key Tools Referenced: SHAP, LIME, IBM AI Fairness 360, Microsoft Counterfit, Evidently AI, AWS Model Monitor