Auditing AI: ISO 42001 as the New Global Governance Standard

Artificial intelligence systems are no longer niche tools. They drive customer approvals, pricing models, risk-scoring, operational logistics, and even autonomous decision-making. But with that power comes new governance, compliance, and audit risks. 

Traditional audit approaches, controls around documentation, transaction sampling, and financial reconciliations, fall short when algorithmic decisions are opaque, evolving, and embedded in real-time business flows.

Consider this: when an AI model denies a customer loan, or autonomously adjusts inventory levels, which audit tool or checklist checks the underlying model logic, bias, training data lineage, and drift? 

What assurance exists around who approved the model, how it’s monitored, or whether a human oversight loop is in place? 

The answer: very few organizations have done this well.

Hence, the concept of artificial intelligence auditing frameworks and AI audit tools is rising rapidly. Risk-management teams and internal auditors must ask: “What is auditing of AI? How do we set up audit programs that encompass AI lifecycle, data quality, model bias, transparency, and explainability?”

ISO 42001 enters this space purpose-built for exactly those questions. It frames what artificial intelligence in auditing is into actionable controls and a full-blown management system approach.

Introducing ISO 42001: AIMS for AI

ISO/IEC 42001:2023 is the first international standard aimed at establishing an Artificial Intelligence Management System (AIMS) for organizations that develop, deploy, or operate AI systems.

Key pieces of the standard

• It defines requirements for establishing, implementing, maintaining, and continually improving an AIMS, essentially a governance-and-audit infrastructure for AI.
• It spans policy, leadership commitment, planning, AI-risk assessment, operational controls, performance review, internal audit, and continual improvement.
   
• The published document comprises 38 distinct controls organized under nine objective areas (risk assessment, lifecycle management, data quality, human oversight, user communication, documentation, etc.).

In short, ISO 42001 gives organizations a governance and audit framework for AI that matches the rigor traditionally applied to information security or financial systems. 

It signals how to audit AI is now a structured, system-wide discipline, not merely a technical review of one algorithm.

For internal audit, compliance, or risk teams, the question becomes: how do you translate auditing artificial intelligence into audit plans, controls, metrics, and tools? Here are practical entry points.

1. Map the AI Inventory & Ecosystem

Begin by cataloging all AI systems (models, automation workflows, decision engines) across the organization. Identify: who owns them, what data they use, what business outcomes they support. This exercise clarifies which AI systems are subject to audit, and which may fall outside.

2. Leverage an AI audit checklist

An effective audit of AI should cover:

• Model purpose and approved use case
   
• Training-data lineage, bias assessment, and datasets used
   
• Performance monitoring and drift detection
   
• Human oversight, escalation, and override mechanisms
   
• Transparency, explainability, and documentation of decisions
   
• Data-quality controls, security, and change-management logs
   

This aligns with the controls defined in ISO 42001’s lifecycle and oversight requirements.

3. Use AI audit tools (and human insight)

While tooling exists (model-monitoring platforms, bias-detection systems, logging-and-trace platforms), nothing replaces auditor judgment. 

Combine tool outputs with governance, stakeholder interviews, audit trails, and risk analysis. 

This blend addresses questions like “which AI is the best” from a governance perspective, not merely technical performance, but also auditability, traceability, and compliance.

4. Embed Artificial Intelligence Auditing Frameworks

Auditors should adopt frameworks mapped to ISO 42001 (or national equivalents) that define roles like AI Risk Manager, Internal Auditor for AI, Chief AI Officer, and AI Ethics Committee. 

Organizations implementing ISO 42001 typically formalize these roles.

5. Continuous Auditing, Not Point-in-Time

Given AI models evolve (re-training, new data, real-time decisioning), audit must be a continuous cycle: monitoring, review, improvement. This mirrors the “check‐act” phases of the PDCA (Plan-Do-Check-Act) approach embedded in ISO 42001.

Audit Planning with ISO 42001 as Your Backbone

Implementing artificial intelligence auditing without a governing standard is akin to auditing without an auditable standard, ad hoc and inconsistent. ISO 42001 provides the backbone:

• Plan: Define the scope of your AI management system; identify all AI systems, their risks, and controls.
   
• Do: Implement governance structures, policies, AI-risk management, operational oversight, and human-in-loop controls.
   
• Check: Monitor performance, metrics, audit findings, and non-conformities.
   
• Act: Improve controls, update governance, rectify issues.

This aligns with the basic definition of “what is auditing,” an independent and systematic process of obtaining evidence and evaluating it against established criteria. In this case, the criteria = ISO 42001 controls and the organization’s own AI governance objectives.

The phrase artificial intelligence in auditing raises new obstacles:

• Opaque decisions: AI may produce decisions that humans can’t easily interpret or explain; auditors must probe model logic, not just input/output.
   
• Continual change: Models adapt, drift, retrain; an audit snapshot may be outdated within weeks.
   
• Data and bias risks: Training datasets may embed bias or quality issues; auditors must inspect dataset provenance and monitoring.
   
• Lifecycle complexity: The journey from design → validation → deployment → monitoring spans multiple functions (data science, governance, operations)   auditors must span these divisions.
   
• Tooling & standards gap: Unlike financial audit, AI audit is newer; while ISO 42001 gives structure, many organizations lack mature AI-audit tools or internal expertise.

Thus, what is artificial intelligence auditing goes beyond checking controls; it requires a mindset shift for audit teams.

Case Example: Using ISO 42001 to Audit AI at Scale

Imagine a global SaaS provider launching a large-language-model-based customer-care chatbot. As part of its audit program, the internal audit department uses ISO 42001 to:

• Define the scope: the chatbot, underlying models, and user data pipelines.
   
• Use an AI audit checklist derived from ISO 42001’s 38 controls.
   
• Engage AI Risk Manager and AI Ethics Committee.
   
• Use AI audit tools to monitor response bias, drift, and incorrect escalations.
   
• Conduct documentation review: model approvals, training-data logs, versioning.
   
• Schedule continuous audits and reporting to the board: metrics of model performance, bias incidents, and override events.

As a result, the company not only satisfied its internal compliance function but demonstrated to key customers and regulators that a recognized standard governed its AI system.

The Governance & Assurance Advantage

Adopting an artificial intelligence auditing framework based on ISO 42001 delivers distinct advantages:

• Stakeholder trust: Certification or alignment with ISO 42001 signals you are managing AI responsibly.
   
• Risk management: The standard’s lifecycle and control structure embeds continuous risk assessment, human oversight, and documentation.
   
• Regulatory readiness: As jurisdictions adopt AI-specific regulation (e.g., EU AI Act), organizations that already audit AI and deploy management systems aligned with ISO 42001 will be ahead.

Practical Steps – Building Your AI Audit Program

Here's a roadmap for initiating how to audit AI within your organization:

1. Executive sponsorship & governance: Define roles (AI Risk Manager, AI Audit Lead), secure board commitment.
    
2. Inventory AI systems & assess risk: Catalog where algorithms live, identify risks across data, model, decision, governance.
    
3. Define audit criteria and checklist: Based on ISO 42001 controls, build an AI audit checklist referencing data quality, lifecycle management, transparency, and ethical oversight.
    
4. Select appropriate tools: Use AI audit tools to monitor model drift, bias, document logs, and user interactions.
    
5. Conduct pilot audits: Start with key AI systems (high-risk, customer-facing) and test your auditing framework.
    
6. Report findings and drive improvement: Provide audit findings to the board/management, and integrate audit outcomes with AI governance processes.
    
7. Continuous improvement: Embed audit findings into model governance, training, and system updates. This aligns with ISO 42001’s continuous improvement requirement.

• Treating AI models like software components: Models evolve, data drifts, and assumptions change, and auditing must adapt.
   
• Auditing only once: A one-time audit doesn’t catch post-deployment drift or misuse continuous audit is needed.
   
• Focusing only on accuracy: Model accuracy is one metric; fairness, transparency, explainability, and documentation matter equally.
   
• Not defining scope clearly: Without knowing which AI is in scope, the audit may miss key systems or use cases.
   
• Lack of tool support and human skills: Audit teams may lack AI-specific expertise; partnering with data science and governance is essential.

The Future of AI Audit: Standardization, Automation, and Assurance

With ISO 42001 establishing a global benchmark, the next wave of auditing artificial intelligence will see:

• Auditable AI: machine-readable audit logs, model version tracking, and real-time governance dashboards.
   
• Automated audit tooling: AI audit tools that monitor model behavior continuously and flag control deviations.
   
• Integration with other standards: ISO 42001 will increasingly align with ISMS (ISO 27001), RMS (ISO 31000), and AI-specific frameworks like NIST AI RMF.
   
• Vendor assurance and supply-chain audit: As third-party AI services proliferate, organizations will need to audit not just their own models but vendor models and data pipelines.
   
• Mandatory assurance regimes: Some jurisdictions may move from voluntary to mandatory audits of high-risk AI systems; organizations already embedded in ISO 42001 will be ahead of the curve.

To gain the skills to lead these governance transformations, professionals can enroll in the GSDC ISO 42001:2023 Lead Implementer Certification, a globally recognized program designed to equip practitioners with the knowledge to build, implement, and manage AI management systems aligned with ISO 42001 requirements.

The auditing has always been subject to assurance of checking whether systems meet requirements, whether there are controls, and whether decisions are supported by evidence. 

However, by 2025 and beyond, such systems are more and more combined with algorithms, models, streams of data, and self-directed decision engines. It would translate to the question of what auditing is being (AI).

The ISO/IEC 42001:2023 is an auditable framework of guidelines that organizations can utilize to govern, control, and monitor AI throughout its lifecycle. It restructures the process of audit AI as the ad-hoc inspection of a single model into a comprehensive management-system practice: policy, control, monitoring, and continuous improvement.

To internal audit, risk, or governance professionals who pose the question as to which AI is the best, the response changes: to the extent that the most performing algorithm is chosen, it is only one portion of the garment. 

Best practices now expose practices that are best to be auditable, traceable, governed, transparent, and supervised because these are now of critical importance.

In brief, artificial intelligence auditing represents a requirement to upgrade the framework, new tools, new skills, and a strategic commitment. ISO 42001 gives that roadmap. 

The organizations that go first will not simply cope with AI; they will audit it, control it, and extract assurance out of it.

In an autonomous systems world, the link that is forgotten is code in its governance. The way audit teams work will have to change in the future since AI is not going to wait.

1. What is auditing artificial intelligence?

Auditing artificial intelligence means evaluating AI systems to ensure they adhere to policy, governance, risk, and ethical standards, checking data, models, decisions, and oversight.

2. How do you audit AI effectively?

To audit AI, start by defining the scope, documenting the model life-cycle, assessing data quality, governance controls, and drift monitoring, then using an AI audit checklist and AI audit tools for continuous monitoring.

3. What is an artificial intelligence auditing framework?

An artificial intelligence auditing framework provides structured guidance and controls governance, management, and audit functions to ensure AI systems are transparent, fair, safe, and reliable.

4. What does ISO 42001 add to auditing artificial intelligence?

ISO 42001:2023 introduces a full AI management system (AIMS) incorporating internal audits, continuous improvement, and lifecycle controls, giving auditors a recognized framework for auditing artificial intelligence. 

5. Which audit roles are key when auditing AI?

Key roles in auditing artificial intelligence include AI Risk Manager, Internal Auditor for AI, Chief AI Officer, and Ethics Committee, all supported by the artificial intelligence auditing framework.

6. What tools should be used when auditing AI?

When auditing AI, auditors should use AI audit tools for model-monitoring, bias detection, data-lineage tracking, plus an AI audit checklist aligned with ISO 42001 and other frameworks.