KVKK

When payroll, customer, candidate, or contract files containing personal data are connected to an AI assistant, a processing activity may be introduced. The question is not only what the model answers; collection, storage, access, and transfers to external services matter together.

Before launching an assistant, inventory which files contain personal data and which users need to see them. Keep a company-wide knowledge base separate from a restricted legal or HR assistant.

In KobiGPT, company and department scope helps keep personal-data documents out of every user’s retrieval space. The role model defines super_admin, admin, manager, and viewer levels; those roles do not replace a data-protection policy.

Citations also support governance. Seeing which document passage supports an answer makes an incorrect scope or stale file easier to detect; it does not automate a legal-compliance decision.

Data minimisation, retention, access rights, and supplier agreements should be settled with the legal team. Supplying an assistant only with the folders it actually needs reduces technical and organisational risk at the same time.

KVKK is not a product badge; the assessment depends on the use case. KobiGPT’s citations and scope controls provide technical support, while notices, legal basis, and retention decisions require qualified advice.

A useful way to evaluate kvkk is to follow one real question from the source document to the final answer. Record which file was selected, what context reached the model, and what a reviewer would need to verify. This turns a definition into an operational check and makes the result comparable across teams.

The same check should include ownership and change management. Decide who updates the relevant documents, how an outdated result is reported, and which access boundary applies. KobiGPT can provide the assistant and the cited document context, but the organisation still owns the source material, permissions, and the decision made from the answer.