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AI records should identify who did what, with which data and model, when, and who may review it. Balance useful evidence against sensitive information in logs.

EP 10 · Updated 2026-09-14 · Published 2026-09-14 · Written by RAGBOX · Reviewer: awaiting assignment
Definition and overview

AI records should identify who did what, with which data and model, when, and who may review it. Balance useful evidence against sensitive information in logs.

Putting it into practice

Define events and correlation IDs. Check synchronized clocks, restrict log readers and set retention. Prove auditability by reconstructing a sample incident, not merely showing a log screen.

Workflow

Define pilot gates first: retrieve expected evidence, reject unauthorized requests and route consequential actions for approval. Assign system and knowledge ownership separately. Expand only after testing, retaining failures as regression cases when models or data change.

RAGBOX Private AI Control Framework

Before you begin

  1. Who and when
  2. Model and sources
  3. Log retention and access

Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.

Key takeaways

  1. Select permitted documents
  2. Assign data owners
  3. Prepare reference questions and answers
  4. Test denied access and deletion
  5. Measure quality, speed and cost before scaling

This is RAGBOX planning guidance, not an external certification standard. Each layer needs an owner, evidence and tests.

Frequently asked questions

Does data have to leave the network?

It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.

Can an AI answer be trusted immediately?

Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.

How should a project begin?

Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.

References

Public sources explain principles; they do not certify or endorse RAGBOX.

Expert review is required before operational use. This is not case-specific legal or tax advice.

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Shown capabilities are design concepts. Confirm scope and test the implementation before use. This demonstration connects to no live AI or customer data.