Generative AI governance in the enterprise: roles, rules, logs and costs
The six workstreams of a generative AI governance that holds up in operation: roles, usage policy, model catalogue, access rights, traceability and cost control.
Read the guideEvery action is recorded in a SHA-256 hash-chained audit trail, verifiable end to end. Sovereign, traceable, deployed on your premises. Touch one line and the chain knows.
Change a single line: the whole chain breaks, and the log proves it.
Security is not a toggle: it is wired into the product.
A glimpse of the foundation, not an exhaustive list.
An honest posture, pitched at the level a first CISO read needs. The control-by-control detail lives in the compliance pack, on request.
Logging, transparency, human oversight (Art. 12/13/14). Art. 50 in progress.
Minimisation, right to erasure, encryption, pseudonymisation. Vendor = processor.
Govern, Identify, Protect, Detect. Maturity framework, not certifiable.
Technically aligned; certification in preparation (ISMS, SoA, COFRAC audit).
Model lifecycle and human oversight in place. Certification planned.
Privacy extension of the ISMS, aligned. Prerequisite: 27001 certification.
Deployable on qualified infrastructure; certification on the host side.
SmartAGT is not yet third-party certified: technical alignment is in place, certification is on the roadmap.
SmartAGT runs inside your infrastructure. You can use a local model, with no external transfer, or a SaaS model. In SaaS mode, the content needed for the request is sent to the chosen provider; direct identifiers can be pseudonymised before sending.
In 100% local mode, nothing ever leaves your infrastructure: the model runs on your side. The request content is sent to the chosen provider. Direct identifiers can be pseudonymised before sending.
email.send to 312 recipients. Side-effect action.Tool email.send “WRITE level” 312 recipients. Nothing is sent without your approval.
By default, every side-effect action is paused before execution: the nature of the action decides, not the model. The operator approves or refuses, and the decision is recorded.
Write actions stop dead. The action's level triggers the pause, not the model's judgement.
The operator sees exactly what is about to happen before confirming.
Approval or rejection lands in the audit chain, with the human actor.
The six workstreams of a generative AI governance that holds up in operation: roles, usage policy, model catalogue, access rights, traceability and cost control.
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Read the guide