A language model can draft or recommend; a person or accountable process should decide what happens next. Software should record those as separate events: what the model contributed, what a reviewer did with it, and who or what made the final decision. That separation improves traceability, but it is an architectural recommendation—not a rule that every use of an LLM requires the same human review.
Track the content, the decision, and the oversight separately
Consider a system that drafts a response for a staff member. A useful workflow identifies the model-generated draft, lets the staff member examine and change or reject it, and records the resulting decision and its accountable owner where appropriate. A label attached to the draft answers a different question from a record of who reviewed it and what they decided.
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Content provenance
Content provenance concerns the artifact: whether it was generated or manipulated by AI, what marks or other provenance signals it carries, and whether those signals remain available when the content is shared. Visible disclosures and machine-readable marks serve different purposes; neither alone describes a later human decision.
Decision provenance
Decision provenance concerns the workflow: what AI output or recommendation was considered, who reviewed it, and whether it was accepted, changed, or rejected. A record of an output’s origin is not evidence that anyone examined its substance.
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System oversight
Oversight concerns what a person can do while the system operates: understand relevant limits, interpret its output, challenge or disregard it, and intervene when necessary. These capabilities matter especially in regulated or high-impact settings, but legal requirements depend on the system’s use and classification.
What the EU AI Act requires—and what it does not
The European Commission says the AI Act’s Article 50 transparency obligations apply from 2 August 2026. They are targeted duties, not a blanket requirement to publicly label every piece of AI-assisted writing. Providers of systems that generate synthetic audio, image, video, or text must ensure that outputs are marked in a machine-readable format and detectable as artificially generated or manipulated within Article 50’s scope. Deployer disclosure duties apply in specified situations, including deepfakes and certain AI-generated or manipulated text published to inform the public on matters of public interest. The Commission identifies an exception for such text when it has undergone human review and is subject to editorial responsibility. See the Commission’s Article 50 guidance and its Code of Practice on AI-generated content.
The Commission defines human review in this context as “the deliberate examination of the substance of the content by one or more natural persons possessing relevant knowledge and professional judgement pertaining to the subject matter under scrutiny.” That is more than a click-through approval or the presence of a reviewer’s name in a log: the person needs to engage with the substance and bring relevant judgment to it. This quotation describes human review under the Commission’s Article 50 guidance; it should not be treated as a universal legal definition for every AI workflow.
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For high-risk AI systems, Article 14 requires effective human oversight proportionate to risk, autonomy, and context. The Act says: “High-risk AI systems shall be designed and developed in such a way, including with appropriate human-machine interface tools, that they can be effectively overseen by natural persons during the period in which they are in use.” The assigned people must be able, as appropriate, to understand relevant capabilities and limitations, monitor for anomalies, interpret outputs, decide not to use or to disregard or override an output, reverse a decision, and intervene or stop operation. These provisions apply to high-risk AI systems, not automatically to every LLM application. See Regulation (EU) 2024/1689, Article 14.
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High-risk deployer duties include monitoring and logs
Deployers of high-risk systems have duties that include using the system according to its instructions, assigning competent human oversight, monitoring operation, and retaining automatically generated logs that are under their control for at least six months, unless other applicable law provides otherwise. This is a scoped obligation, not a general retention period for every AI tool or business record. The AI Act Service Desk’s Article 26 summary describes the deployer requirements.
The Commission describes high-risk obligations as applying on a staged timeline and currently gives 2 December 2027 for the high-risk systems it discusses. The relevant category and transitional provisions matter; check the Commission’s AI Act overview and the current consolidated Act before relying on a deadline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design a decision record that can answer an audit question
A practical record should make it possible to reconstruct the workflow without implying that a log itself proves a meaningful review. Depending on the task and applicable rules, capture:
- The AI system or model identifier and version, where available.
- The task or workflow context and a copy of the output, or a stable reference to it.
- Relevant provenance or disclosure signals associated with the output.
- The reviewer’s identity or role and when the review occurred.
- The action taken: accepted, edited, rejected, or escalated, plus material changes or a concise rationale where useful.
- The accountable decision-maker or process, especially where the output informs a consequential action.
This is an implementation recommendation, not a universal record schema prescribed by the cited sources. Keep only data needed for the purpose, protect sensitive information, and set retention according to applicable law and operational need. A reviewer field that can be completed without examining the substance risks creating the appearance of oversight without the capability or authority to challenge the output.
Choose technical controls for the question they answer
Provenance metadata, machine-readable marks, watermarks, detection tools, testing, and auditing can help authenticate or identify synthetic content. NIST’s 2024 overview surveys these approaches and their use in tracking provenance, labeling, detection, testing detection software, and auditing systems. They are technical approaches, not guarantees of certainty, and they do not establish that a person reviewed an output or made the final decision. See NIST’s report on technical approaches to synthetic content.
When comparing implementations, ask whether disclosure is visible, machine-readable, or both; whether records attach to the artifact or to the decision; whether provenance survives sharing; whether a reviewer can actually understand and challenge the output; what logs are retained and under which rule; and whether a person can override, reverse, or stop an automated action. The right controls vary by use, risk class, jurisdiction, and date.
Make the human role real, not ceremonial
A defensible workflow gives a reviewer enough context and authority to disagree with the model, and preserves an accountable decision trail distinct from the artifact’s AI-origin signal. Treating generation, review, and decision as separate events makes it clearer what the technology did—and what the responsible person or process actually decided.
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