How to Audit AI Applications | Lessons from ISACA Scotland with a Real-World Example

Yesterday, I attended an insightful seminar by Allan Boardman and ISACA Scottish Chapter , where we explored the challenges and opportunities in auditing AI systems. The session highlighted a vital yet often-overlooked reality: while AI is transforming industries, many organisations lack robust processes to audit their AI applications.

Here’s the issue: AI systems, if left unchecked, can introduce risks such as bias, regulatory violations, or even reputational damage. Without proper auditing, you’re essentially blind to how these systems perform and impact your organisation. So, how can we ensure AI operates ethically, effectively, and within regulatory boundaries?

The Solution: A Framework for Auditing AI Applications

Auditing AI involves a structured approach to identify and mitigate risks while maximising value. Here’s a step-by-step framework, combining insights from the seminar, Microsoft’s three-question model, and ISO/IEC 42001:2023.

1. Ask the Right Questions (Microsoft’s Framework) as mentioned by Roland Verhaaf

Before deploying an AI tool, ask these three critical questions:

1. What is the intended outcome?

2. What are the acceptable consequences?

3. What are the unacceptable consequences?

This simple framework ensures clarity around what the AI is meant to achieve and flags potential risks upfront.

2. Map the AI Application

? Inputs: What data feeds into the AI, and where does it come from?

? Processes: How does the AI process and analyse this data?

? Outputs: What results are generated, and how are they used?

Mapping these elements uncovers vulnerabilities and ensures all system components are accounted for.

3. Test for Bias and Ethics

? Bias Audits: Use scenario-based testing to identify unfair or biased outcomes.

? Transparency: Can the AI’s decisions be explained to stakeholders?

? Diverse Data: Regularly update datasets to avoid perpetuating outdated biases.

4. Ensure Compliance (ISO/IEC 42001:2023)

ISO/IEC 42001:2023 provides a global standard for AI governance, covering:

? Governance: Oversight throughout the AI lifecycle.

? Risk Management: Proactively addressing unintended consequences.

? Continuous Improvement: Establishing audit cycles to ensure ongoing compliance.

5. Monitor Performance and Reliability

AI systems require continuous evaluation. Test for:

? Accuracy: Are outputs consistent and aligned with expectations?

? Scalability: Can the system handle peak loads?

? Real-World Scenarios: Do results meet user needs across diverse conditions?

Real-World Use Case: Auditing LinkedPostGPT

To illustrate this framework, let’s consider a fictional AI tool, LinkedPostGPT, designed to help businesses craft LinkedIn posts.

Audit Objectives

? Generate engaging, SEO-optimised LinkedIn posts tailored to user preferences.

? Ensure outputs are unbiased, ethical, and compliant with GDPR.

Technical Observations

? Bias Testing: Prompts revealed a 15% higher favouritism for tech keywords.

? Performance: Latency increased by 10% during high-volume usage.

? Monitoring: Drift detection showed a 4% decline in post relevance over six months.

Final Thoughts

The LinkedPostGPT audit highlights how structured evaluations can identify risks, from bias to performance inefficiencies, while guiding improvements. By applying frameworks like Microsoft’s three questions and ISO/IEC 42001:2023, organisations can confidently scale AI solutions without compromising ethics or compliance.

Are you auditing your AI systems? What steps have you taken to manage AI risks in your organisation? Let’s discuss in the comments!