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Generative AI Risk Questionnaire

Evaluate the security, ethics, and compliance of third-party AI implementations.

Start questionnaire

Start questionnaire with the vendor profile, then move through each section at your own pace.

AI governance

The assessment covers how AI systems are built, trained, deployed, monitored, and explained, including accountability frameworks, bias testing, human review, ethical AI standards, and vendor ownership of model risk.

Security scope

Security teams can use the questions to document development environments, network segmentation, privileged access, incident response, penetration testing, restoration testing, third-party AI dependencies, monitoring responsibilities, and escalation paths for model-related incidents.

Privacy lifecycle

The privacy sections focus on retention, legal basis, data location, re-identification risk, DPIA or PIA evidence, privacy breach response, consent notices, high-risk processing, cross-border transfer concerns, accountability, documented ownership, and defensible review notes.

Currently at: AI Ethics: Data & Decisions
Rationale
Understanding the data collection methodology is essential to assess the diversity and representativeness of the dataset.
Rationale
Identifying potential biases from the outset helps mitigate risks related to model performance across different groups.
Rationale
Evaluating personal data against immutable characteristics ensures identification and eradication of bias is formalized.
Rationale
Identifies ADM systems that significantly affect individuals, requiring additional regulatory compliance.

Data remains local to your session.

AI review guidance

Use the questionnaire with a clear AI vendor risk decision.

The exported answers are most useful when they connect to an approval decision, evidence request, or remediation plan. Use the supporting GreenHat guides to decide which AI security gaps matter before an AI tool becomes part of a sensitive workflow.