⚡ Quick Summary
This document is a highly structured, practitioner-oriented checklist for auditing AI systems across their full lifecycle. Developed by Dr. Nath Alagbe, it translates AI governance, security, and compliance expectations into concrete audit questions, validation methods, and required evidence. The strength of the resource lies in its operational clarity—each control is tied to recognized frameworks such as ISO/IEC 42001, NIST AI RMF, ISO 27001/27002, and OWASP Top 10 for LLMs. It is less a conceptual guide and more a ready-to-use audit instrument. For organizations struggling to move from “AI principles” to verifiable controls, this checklist provides a direct bridge into implementation and assurance.
🧩 What’s Covered
The checklist is organized into ten domains that collectively map the full AI system lifecycle—from governance to infrastructure to post-deployment monitoring. Each domain follows a consistent structure: audit area, audit question, validation method, required evidence, and references to standards.
It begins with AI Governance and Accountability, focusing on policies, roles, and ethical principles. This includes verification of governance structures, RACI matrices, and integration of ethics into system design. The second domain, AI Risk Management and Oversight, introduces formal risk assessments, treatment plans, and AI-specific impact assessments aligned with GDPR and ISO standards.
The Model Lifecycle Security domain operationalizes secure development practices, including threat modeling, versioning, and change management. This is complemented by a deep focus on data security (Domain 4), covering provenance, privacy-enhancing techniques, and protections against prompt injection and data leakage.
The checklist then moves into model-centric risks: robustness, adversarial testing, explainability, and fairness (Domain 5). These are not treated abstractly—each requires evidence such as adversarial test reports or fairness metrics documentation.
Domains 6 and 7 address access control and infrastructure security, including RBAC, secrets management, MFA, network segmentation, and CI/CD pipeline integrity. Domain 8 introduces continuous monitoring, logging, and incident response tailored to AI-specific threats like model drift or poisoning.
Finally, Domains 9 and 10 extend governance outward—to third-party models, open-source dependencies, and regulatory compliance. This includes supply chain due diligence, license scanning, regulatory mapping (e.g., EU AI Act), and maintaining full audit trails of model decisions and development lineage.
💡 Why it matters?
Most AI governance frameworks remain high-level and difficult to operationalize. This checklist solves that problem by translating abstract requirements into auditable controls. It enables organizations to demonstrate compliance, not just declare it.
It is particularly valuable in the context of the EU AI Act and ISO/IEC 42001, where organizations must prove traceability, risk management, and oversight. The explicit mapping to standards reduces ambiguity and accelerates audit readiness.
From a governance perspective, it also forces alignment between legal, technical, and security teams. Each control requires evidence—policies, logs, reports—making it a practical tool for internal audits, external assurance, and even vendor assessments.
❓ What’s Missing
The checklist is intentionally rigid, which is both its strength and limitation. It lacks prioritization—there is no guidance on which controls matter most for different risk levels or AI use cases. Everything is treated as equally relevant.
It also does not address organizational maturity. Smaller teams or early-stage AI adopters may find the checklist overwhelming without a phased implementation approach.
Another gap is the absence of concrete examples or templates (e.g., sample AI risk assessments, incident playbooks). While it defines “what good looks like,” it does not help build it.
Finally, there is limited treatment of emerging GenAI-specific risks beyond OWASP references—topics like hallucination governance, human-in-the-loop design, or model alignment are not deeply explored.
👥 Best For
AI auditors and internal audit teams
CISOs and security leaders responsible for AI systems
Organizations preparing for ISO/IEC 42001 or EU AI Act compliance
MLOps and engineering teams building audit-ready AI pipelines
Consultants designing AI governance and assurance frameworks
📄 Source Details
AI Security Audit Checklist
Author: Dr. Nath Alagbe (CISA, CISSP, CISM, CRISC, and others)
Format: Structured audit checklist (10 domains, control-based)
Length: 7 pages
📝 Thanks to
Dr. Nath Alagbe for translating AI security and governance into a truly operational audit tool that bridges frameworks and real-world implementation.
