AI GLOSSARY

AI Audit

An AI audit is a systematic review of an AI system to assess its quality, fairness, security, and compliance—conducted by internal teams or external auditors. Under the EU AI Act, it will become mandatory for many systems and advisable for all.

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Test Levels
Technology, Data, Processes, Compliance

4

Core Areas
Quality, Fairness, Safety, Transparency

3

Trigger
AI Act, GDPR, Customer Requirements

6

Best Practices
for Successful Audits

Why AI Audits Are Becoming Important for Businesses

AI systems are becoming business-critical—and subject to regulatory scrutiny. A professional AI audit reveals whether your AI delivers on its promises—technically, ethically, and legally—and lays the foundation for trust among customers, regulators, and your team.
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AI Act Preparation

Starting in 2026, many audits will be mandatory—practicing early on builds confidence.

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Proof of Quality

External testing confirms: Your AI delivers what it promises.

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Risk Mitigation

Vulnerabilities are detected before they cause damage in production.

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Competitive Advantage

Certificates and audit reports are valuable in sales.

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Trust Within the Team

Internal audits provide clarity on strengths and limitations.

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Governance Maturity

Every audit improves internal AI governance.

What is an AI audit?

An AI audit is the systematic examination of an AI system based on defined criteria—conducted by internal teams (first-party), independent departments (second-party), or external auditors (third-party).

Scope of an AI audit: Technical review (model quality, robustness, security), data review (quality, origin, representativeness), ethics & fairness (bias, discrimination), process review (governance, roles, documentation), compliance review (EU AI Act, GDPR, industry-specific regulations).

Triggers for AI audits: Regulatory requirements (the AI Act mandates conformity assessment for high-risk AI), customer requirements (B2B customers are increasingly requesting certificates), internal quality assurance (maturation of governance).

For small and medium-sized enterprises, building internal audit capabilities is strategically valuable—not only for compliance but also for the systematic improvement of their own AI applications.

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Audit Levels & Methods in an AI Audit

An AI audit encompasses several levels. These eight are particularly important:

Model Performance

Accuracy, precision, recall — measured by segment and over time.

Data Quality

Training, validation, and test data were checked for quality and representativeness.

Fairness Analysis

Bias was measured and documented across various groups.

Robustness & Adversarial Tests

How resilient is the model against disruptions and attacks?

Explainability

Can decisions be justified in a way that is easy to understand?

Governance Audit

Roles, Rights, Processes — Documented in a Structured Way?

Compliance Check

Have all regulatory requirements been met?

Operational Aspects

Monitoring, incident management, and change management.

Best Practices for AI Audits

These six principles help ensure successful audits:

  • Preparation is key: Complete documentation and test results speed up every audit.
  • Use a framework: ISO 42001 or the AI Act as a basis for criteria—don’t reinvent the wheel.
  • Maintaincritical distance: Internal auditors must not have developed the systems being audited themselves.
  • Systematic approach to fairness and bias: Don’t do it ad hoc—conduct dedicated tests with clear metrics.
  • Iterative approach: Small, regular audits are more effective than large annual audits.
  • Follow-up: Track up on findings—don’t just document them.
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Type 1

First-Party

Self-assessment by your own team. Continuous improvement, minimal effort.

Regular

Type 2

Second-Party

Internal independent audit. To build trust within the company and with partners.

Trust

Type 3

Third-Party

Certified external auditors. A requirement for AI Act compliance for high-risk AI.

Certified

Common Mistakes in AI Audits

We see these pitfalls time and time again:

  • Unclear Scope: What does the audit cover? Without clear boundaries, every audit becomes endless.
  • Lack of Documentation: Without technical documentation, an audit turns into detective work.
  • One-time only: A single audit isn’t enough—AI systems change, so audits must be ongoing.
  • Technical focus only: Fairness, ethics, and governance are missing—the audit is incomplete.
  • No follow-up: Findings are documented but not addressed—the audit was a waste of time.

First-Party vs. Second-Party vs. Third-Party Audits

Three types of audits with different roles:

  • First-Party: Internal self-assessment by the company’s own team. For continuous improvement.
  • Second-Party: Audit conducted by an independent internal department or a client. For building trust.
  • Third-Party: External, independent auditors. For certification and AI Act compliance.
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Frequently Asked Questions About AI Audits

AI Audit for Your AI Systems

In a free initial consultation, we’ll assess the audit needs of your AI systems and outline a suitable audit approach—including a preparation plan.

As an AI partner for small and medium-sized businesses, we guide you through AI audits from preparation to follow-up—with expert knowledge and a pragmatic approach.

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