What is AI Audit?

What is an AI Audit?

An AI Audit is a structured evaluation of an AI system that examines how it was built, how it operates, and whether it meets applicable standards for fairness, accuracy, transparency, compliance, and governance. Similar to financial audits or IT security audits, AI audits provide an independent assessment that helps organisations identify issues, demonstrate compliance, and build stakeholder confidence.

For business leaders, AI audits serve two critical functions: they find problems before they cause harm, and they provide evidence of responsible AI practice to regulators, customers, and partners.

Types of AI Audits

Compliance Audit

Evaluates whether an AI system meets specific regulatory requirements and legal obligations. This includes data protection compliance (PDPA requirements across ASEAN markets), sector-specific regulations (financial services, healthcare), and adherence to AI-specific guidelines.

Ethics Audit

Assesses whether an AI system aligns with ethical principles, including fairness, non-discrimination, transparency, and accountability. This goes beyond legal compliance to evaluate the system against the organisation's stated values and broader societal expectations.

Technical Audit

Examines the technical aspects of an AI system, including model architecture, training data quality, accuracy metrics, robustness testing, and security measures. This is the most technically detailed type of audit and typically requires data science expertise.

Process Audit

Reviews the organisational processes around AI development and deployment, including governance structures, approval workflows, documentation practices, monitoring procedures, and incident response capabilities. This audit type focuses on how your organisation manages AI rather than the specifics of individual models.

Impact Audit

Evaluates the actual outcomes and impacts of an AI system on individuals and communities. This includes assessing whether the system produces equitable outcomes across different demographic groups and whether it creates unintended negative consequences.

What an AI Audit Examines

A comprehensive AI audit typically covers the following areas:

Data Assessment

• Data sources: Where does the training data come from? Is it legally obtained and properly licensed?
• Data quality: Is the data accurate, complete, and representative?
• Data protection: How is personal data handled? Does it comply with applicable PDPAs?
• Data bias: Does the data contain biases that could lead to unfair outcomes?

Model Assessment

• Development process: How was the model built? What decisions were made and why?
• Performance metrics: How accurate is the model? Does accuracy vary across different groups or scenarios?
• Fairness testing: Does the model produce equitable outcomes across demographic groups?
• Explainability: Can the model's decisions be meaningfully explained?
• Robustness: How does the model perform under stress, with unusual inputs, or in edge cases?

Governance Assessment

• Accountability: Who is responsible for the AI system? Are roles and responsibilities clearly defined?
• Documentation: Is the development process, testing, and deployment adequately documented?
• Monitoring: Are there systems in place to monitor ongoing performance and detect issues?
• Incident response: What happens when something goes wrong? Are there clear escalation and remediation procedures?

Deployment Assessment

• User communication: Are users informed about AI involvement in decisions that affect them?
• Human oversight: Is there appropriate human oversight, especially for high-risk decisions?
• Feedback mechanisms: Can users report concerns or appeal AI-driven decisions?

AI Audit in Southeast Asia

AI auditing is an emerging discipline in Southeast Asia, with several important developments:

Singapore is leading the region through AI Verify, a testing framework that functions as a self-assessment audit tool. It allows organisations to test their AI systems against governance principles and generate reports that demonstrate compliance. Singapore has also developed the Companion to the Model AI Governance Framework, which provides practical audit guidance.

Thailand and Indonesia do not yet have specific AI audit requirements, but their data protection laws (PDPA in both cases) include accountability and documentation requirements that effectively mandate elements of AI auditing for systems that process personal data.

Internationally, the ISO/IEC 42001 standard for AI management systems includes audit requirements that are increasingly relevant for ASEAN businesses operating in global markets or serving multinational clients.

For businesses operating across ASEAN, conducting AI audits demonstrates governance maturity and prepares you for the more formal audit requirements that are likely to emerge as regulation evolves.

Conducting an AI Audit

Internal vs. External Audits

Internal audits are conducted by your own team and are valuable for regular assessment and continuous improvement. External audits, conducted by independent third parties, provide greater assurance to stakeholders and are typically required for regulatory compliance.

Practical Steps

1. Define scope: Determine which AI systems will be audited, what types of assessment are needed, and what standards will be applied.
2. Assemble the team: AI audits require multidisciplinary expertise, including data science, legal, ethics, and domain knowledge. Few organisations have all these skills internally.
3. Gather documentation: Collect all available documentation about the AI system, including development records, testing results, data descriptions, and monitoring reports.
4. Conduct assessment: Apply the audit methodology to evaluate the AI system against the defined criteria.
5. Report findings: Document findings, including identified issues, risk assessments, and recommendations for remediation.
6. Remediate and follow up: Address identified issues and conduct follow-up assessments to verify remediation.