AI audit: definition, process and value
Definition
An AI audit is a structured review of whether a company's AI systems meet legal requirements, internal rules and quality criteria. It captures the systems in use, frames their risks and compares the current state against the requirements. The result is a documented overview with gaps, recommendations and a basis for evidence.
Why the Term Matters
Without an inventory, hardly any company knows exactly which AI runs where on which data. That backfires when oversight, a customer or an incident demands evidence. As a neutral advisor we review independently, with no product of our own in the background. An AI audit turns vague assumptions into a solid list showing where everything fits and where to fix things before the next step.
When It Is Relevant
- The overview of AI systems in use and their risks is missing.
- The state should be reviewed independently before a larger rollout.
- An authority, certification or customer requires evidence.
- After an incident, it should be clarified what went wrong where.
When Not
When only a single, clearly documented system runs, a targeted review suffices instead of a comprehensive audit. And when AI is still pure trial in-house, a simple overview of the experiments is more sensible than a formal audit that has no stable object yet.
Industries That Use It
- Financial services: regular reviews are routine and include AI-supported processes.
- Automotive suppliers: customers require evidence of standards along supply chains, including for AI.
- Health insurers: AI in case handling touches sensitive data and needs regular control.
- Energy utilities: critical applications are checked for reliability and rule compliance.
Typical Use Cases
- An inventory of all AI systems in use with purpose, data and owners.
- A comparison of the current state against duties from the EU AI Act, GDPR and internal rules.
- A gap list with prioritised recommendations.
- Bundling evidence in preparation for an external review.
- A follow-up on whether identified gaps were closed.
Decision Guide
| Signal | AI audit fits | Alternative |
|---|---|---|
| No overview of AI | start an inventory | staying in the dark is risky |
| Evidence is required | audit with documentation | promises without proof do not hold |
| Single clear system | a targeted review suffices | a full audit would be overbuilt |
| Pure trial | a simple overview is enough | a formal audit comes too early |
Example
A financial services firm wants to know where it stands before a supervisory review. We capture the AI systems in an inventory, compare them against the applicable duties and create a prioritised gap list with recommendations. The evidence is bundled review-ready. This describes the method, not an asserted client metric.
Service Connection
The entry is often EU AI Act consulting and AI consulting. As a structured starting point, the AI potential analysis serves well.
Sources
This framing describes common practice for AI audits. Statements on duties from the EU AI Act and GDPR are general orientation and not legal advice; they must be checked case by case against the currently applicable legal texts. An AI audit in the sense described here is a structured review, not an accredited certification.