Artificial Intelligence Auditing Framework

Summary

The Institute of Internal Auditors' AI Auditing Framework fills a critical gap in the market—giving internal audit teams the specific tools and methodologies they need to evaluate AI systems. Unlike generic risk management frameworks, this resource provides audit-specific guidance for assessing everything from data quality and model governance to algorithmic bias and AI ethics programs. The 2024 update reflects the rapidly evolving AI landscape, incorporating lessons learned from early AI audit implementations and addressing emerging risks like generative AI deployment.

The Three Pillars of AI Auditing

The framework is built around three core audit domains that internal auditors must master:

AI Governance and Strategy Auditing focuses on evaluating whether organizations have proper oversight structures, clear AI policies, and alignment between AI initiatives and business objectives. This includes auditing AI steering committees, risk appetite statements, and vendor management for AI services.

Technical AI Controls Assessment provides methodologies for auditing the technical aspects of AI systems—model development processes, data pipeline controls, testing procedures, and deployment safeguards. This pillar helps auditors who may not have deep technical backgrounds assess complex AI implementations.

AI Risk and Impact Evaluation guides auditors through assessing operational, regulatory, and reputational risks specific to AI, including bias testing, explainability requirements, and compliance with emerging AI regulations across different jurisdictions.

What Makes This Framework Different

Most AI governance resources are written for data scientists or compliance teams. This framework specifically addresses the unique position of internal auditors—professionals who need to provide independent assurance on AI systems without necessarily being AI experts themselves. It includes practical tools like audit program templates, control testing procedures, and risk rating methodologies that can be immediately implemented.

The framework also recognizes that AI auditing isn't just about technology—it encompasses organizational culture, third-party relationships, and business process integration. This holistic approach sets it apart from purely technical AI assessment tools.

Who This Resource Is For

Internal audit departments at organizations implementing or planning to implement AI systems, regardless of industry or size. The framework is particularly valuable for:

• Chief Audit Executives developing AI audit capabilities and determining resource allocation
• Senior auditors leading AI-focused audit engagements who need structured methodologies
• IT auditors expanding their skillset to cover AI-specific risks and controls
• Risk management professionals working closely with internal audit on AI governance
• Audit committee members seeking to understand what questions to ask about AI audit coverage

The content assumes basic internal audit knowledge but doesn't require deep AI technical expertise—it's designed to bridge that knowledge gap.

Getting Your Audit Program Started

Begin with the framework's AI inventory and risk assessment templates to map your organization's AI landscape. Many organizations don't have comprehensive visibility into their AI usage, so this discovery phase often reveals shadow AI implementations and unmanaged risks.

Next, use the control frameworks to evaluate existing AI governance structures before diving into technical auditing. The framework recommends starting with higher-risk AI applications rather than trying to audit everything at once.

The resource includes sample audit programs for common AI use cases like fraud detection, customer service chatbots, and predictive analytics—these can be customized based on your organization's specific implementations.

Watch Out For

The framework acknowledges several common pitfalls in AI auditing. Many audit teams underestimate the time required for AI system evaluation—these audits typically take longer than traditional IT audits due to the complexity of testing algorithmic outputs and data quality.

Don't expect to audit AI systems using traditional sampling methods. The framework provides guidance on alternative testing approaches, but auditors need to adjust expectations about sample sizes and testing procedures.

The resource also warns against over-relying on vendor attestations for AI services. Third-party AI auditing requires specialized procedures that differ significantly from traditional vendor management auditing.