Evidence and review guide
How do you make AI document review auditable?
An AI document review becomes auditable when each released claim can be traced to a source, the system records the model and tool actions that produced the proposal, uncertainty triggers a defined exception path, and a named policy or human reviewer authorizes release. A chat transcript alone is not an adequate evidence model.
In practice: Traceability makes review and investigation possible; output quality still depends on source quality, validation, and the release process.
01
Separate facts, proposals, and decisions
The system should store source facts separately from model-generated proposals and reviewer decisions. A value extracted from page 12 is not the same thing as an inferred conclusion, and neither becomes an approved output until the release rule is satisfied. This separation reduces silent overwrites and makes corrections attributable.
| Record type | Example | Required provenance |
|---|---|---|
| Source fact | Company number shown on page 12 | File hash, page or region, extracted text, extraction method |
| Model proposal | Entity A appears to control Entity B | Input references, model route, configuration, output, confidence or rationale |
| Validation result | Ownership percentages do not total 100% | Rule version, values checked, pass/fail, timestamp |
| Reviewer decision | Approve after replacing one unsupported value | Reviewer identity, edits, reason, approval scope, time |
| Released artifact | Final filing package | Artifact hash, destination, release authority, linked evidence package |
02
Treat uncertainty as workflow state
A model score should not be the only gate. Escalation can be triggered by missing documents, contradictory sources, unsupported values, deterministic rule failures, out-of-distribution layouts, or a change in a high-impact field. Each trigger needs an owner and allowed resolution.
- Request an additional source
- Route the item to a specialist reviewer
- Block export until a deterministic check passes
- Mark a field as unknown rather than inventing a value
- Retain both the original proposal and the reviewer correction
03
Worked example: controlled disclosure review
A case folder arrives with identity documents, statements, and an ownership chart. Intake preserves the originals and records file hashes. Extraction proposes normalized parties, assets, and relationships with page citations. Rules flag a missing account owner and a percentage mismatch. The workflow blocks export and opens two review items. A reviewer supplies the missing source, corrects the relationship, and records the reason. Only then does the system generate a filing package and a portable evidence bundle linked to the released artifact.
04
Minimum evidence package
The evidence should remain useful even if the application UI changes. A portable package can contain a manifest, referenced file hashes, action events, configuration fingerprints, validation results, reviewer decisions, released artifact hashes, and timestamp evidence. Sensitive payloads can be minimized or referenced by hash when policy does not allow duplication.
- Stable identifiers and manifest
- Hash-linked events in execution order
- Source and output file hashes
- Model, tool, and policy configuration fingerprints
- Validation and exception records
- Reviewer and release decisions
- Independent verification result
05
Sources
Primary references and technical material used in this guide.
- [1] Cynsta AAP specification ↗
Open specification and verifier for portable AI action evidence.
- [2] NIST AI Risk Management Framework ↗
Govern, map, measure, and manage functions for AI risk.
- [3] EU AI Act, Regulation (EU) 2024/1689 ↗
Official regulation text, including record-keeping requirements for covered systems.
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