Traceability means being able to connect a material output to purpose, input version, model, prompt, sources, checks, changes and approvals. Logging all raw data is neither always necessary nor always permissible.
What the concept actually means
Logging depth depends on impact, dispute potential and repeatability. Metadata and hashes may suffice, while sensitive content is protected separately or not stored.
Why it matters in the enterprise
Evidence must be usable: consistent time basis, correlatable identifiers, immutable approvals, defined retention and role-based access.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: A document workflow stores case ID, document version, prompt version, model, sources, review result and approval. Original data remains in the system of record rather than the AI log.
Quality and test criteria
The following criteria make quality observable for this use case:
- A case is correlatable end to end.
- Model, prompt and knowledge versions are visible.
- Logs avoid unnecessary confidential content.
- Retention, access and deletion are tested.
Risks and common misconceptions
Too little logging prevents root-cause analysis; too much creates privacy, security and cost risk. Mutable tables are not dependable approval evidence.
Example transfer artefact
Transfer artefact: an evidence design defining events, fields, protection class, retention, access and audit questions.