APLOMB · Reading guide

AI governance: models, rules and responsibilities

AI governance here means the rules, responsibilities and checks that frame how AI is used. Choosing a capable model is one decision. Deciding who may use it, with which data and for which operations is another.

Editorial update : · APLOMB

Start with the work

We suggest starting with a specific task: drafting a note, analyzing a case or preparing a table. Identify the user, required documents, recipient and potential consequences of error. This helps select the right controls instead of applying the same process to a brochure and a document requiring approval.

Separate four decisions

Permission to create content, evaluation of its claims, approval to release it and signing the file answer different questions. A draft can be useful before every reference is checked. It must remain labeled accordingly. A technical signature does not resolve a missing approval.

Define the data boundary

The proposed framework should name approved sources and all services actually used: main model, optional second reviewer, extraction, search and rendering. In its French guidance, CNIL recommends analyzing risks and framing generative AI use. That guidance is not a certification of APLOMB. [1]

Retain the ability to change

Our goal is to preserve useful policies and documents when an organization changes models. This requires testing the capabilities actually connected: tools, files, parameters, errors and output delivery. Compatibility with one route does not establish compatibility with an entire application. Missing capabilities must be disclosed, not silently removed.

What APLOMB is developing

APLOMB is building independent orchestration, governance and evidence infrastructure. The Workspace is one interface to that foundation. APLOMB LAB explores improvements through prototyping and evaluation. Operational scope must be confirmed for the relevant version and environment; these principles do not describe a universally available, industrialized service.

The question before a pilot

For a representative case, can you explain what was produced, which checks ran, which operations were permitted and what remains uncertain? Then request a repeat with another role or document version. A useful demonstration makes its conditions understandable; a striking result alone is not enough.

External references

  1. CNIL · Questions-réponses sur les systèmes d’IA générative ↗

Translations describe the same scope; they do not extend legal or technical coverage.