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Designing an AI Audit: Objectives, Scoping, and Resources
Evaluating complex automated systems calls for a strategy that reaches well beyond a traditional information technology review. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series walks through how assessing artificial intelligence fundamentally differs from a conventional IT audit, the mindset required to design the engagement, the core objectives the audit must achieve, and how to assemble the right scope and resources for the work.
What this episode covers
- Why the scope expands for intelligent systems — questioning data reliability, decision transparency, and potential harm beyond predictable IT logic.
- The black box challenge posed by deep learning and why interpreting decisions requires blended technical and professional judgment.
- A top-down, holistic design strategy that begins with enterprise goals and prioritizes trustworthiness, ethics, privacy, and long-term impact.
- The six core objectives an AI audit must meet across alignment, project management, SDLC, compliance, and continuous communication.
- Co-sourcing with skilled internal staff and external advisors to handle unfamiliar emerging technologies.
- The six-step scoping process — identification, data, documentation, review history, regulatory mapping, and specialized documentation.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
How does auditing AI differ from a traditional IT audit?
A traditional IT audit examines predictable logic, like inspecting an elevator where pressing a button triggers a specific pre-programmed action. AI systems learn and adapt, so the scope must widen to ask whether the historical data is reliable and free from prejudice, whether decisions are transparent and explainable, and whether the system could cause harm. Deep learning often acts as an opaque black box, making the decision path hidden below the surface.
What is a top-down approach when designing an AI audit?
A top-down approach means starting by looking at the broader strategic goals of the entire enterprise before examining a single line of code. The auditor must be a holistic thinker who ensures a tactical technological deployment does not accidentally destroy the company’s future objectives, prioritizing trustworthiness, ethical behavior, privacy, governance, and long-term impacts rather than just efficiency and security.
What are the six core objectives of an AI audit?
The six objectives are to verify a consistent integrated approach aligned with enterprise goals, ensure the design and intended outcomes match company strategy, confirm standard project management and financial controls, examine the system development life cycle for transparent oversight, validate compliance with all legal and regulatory requirements, and ensure indicative operational reporting flows to the accountable owner and governance committee.
What is the six-step process for scoping an AI audit?
Step one is identification of the systems and accountable owner. Step two is deciding which datasets to access and obtaining permissions. Step three is locating documentation like architectural and testing reports. Step four is reviewing prior privacy, security, second-line, and ethical assessments. Step five is regulatory mapping of all applicable laws. Step six is acquiring specialized transparency, explainability, bias, and ethics documentation unique to intelligent systems.
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Reference: This article is based on concepts discussed in Designing an AI Audit: Objectives, Scoping & Resources.