Auditability asks whether the model produces outputs that can be logged, traced, and attributed in accordance with Article 12. Can individual decisions be reconstructed from the logs?

Models that require only the input and the model version for output reconstruction are strongly auditable: the audit trail is compact, and any decision can be verified by replaying the input through the documented model version. Models where the output depends on runtime conditions (session state, conversation history, retrieval-augmented generation context) require more sophisticated logging. The assessment specifies what must be logged for the candidate architecture to achieve auditability.

For RAG-based systems, auditability requires logging not only the input and output, but also the retrieved documents, their relevance scores, and the prompt assembled from the retrieved context. For agentic systems, the entire chain of reasoning and action must be captured. The logging payload size and storage requirements should be estimated as part of the assessment.

The auditability score reflects the ease with which individual decisions can be reconstructed and the completeness of the audit trail that the architecture naturally supports.

Key outputs

• Auditability score per candidate model
• Logging requirements specification