What an AI Assurance Report Should Contain

1. A clear scope

Which systems were tested, which versions, what they are used for and what was out of scope. Without this, nobody can tell what the results actually cover.

2. Results by risk area

Separate results for security, reliability and fairness, with the method used for each. A single overall score hides where the real problems are.

3. Findings ranked by severity

Every finding described in plain language, with an example, a severity rating and a recommendation someone can act on. Findings should be reproducible, so your team can see the failure for themselves.

4. Governance evidence

• An inventory of AI in use, including tools staff use without approval.
• Named owners for each system.
• Whether human oversight is real or a rubber stamp.
• Audit trails, incident response and supplier controls.

5. Mapping to frameworks

Where relevant, show how the findings relate to the EU AI Act, ISO/IEC 42001 or sector rules. That saves your compliance team from translating the report themselves.

6. The tests themselves

The strongest evidence is the ability to re-run the tests. If you keep the test harness, you can prove fixes worked and re-check the system after every change, without depending on the assessor.

Warning signs

• The assessor also built the system, or is selling the fixes.
• Results are a score with no examples.
• Testing used a demo version rather than the live system.
• There is no way to repeat the tests.