Which company data are suitable for a private AI assistant?

Start with the use case

A private company does not automatically make data suitable. First, it is determined what task the assistant should fulfill: for example, finding released work instructions, answering product questions, or summarizing service tickets. Afterwards, a data catalog is created with source, owner, protection class, update rhythm, and authorized user group. NIST recommends documenting data sets and their origin throughout the AI lifecycle and making risks measurable.

Four checks before release

1. Purpose and Legal Basis: For personal data, purpose and legal basis must be clarified. According to Article 5 GDPR, data minimization and purpose limitation must be observed. Just because a person is allowed to read a file in the DMS does not automatically mean that its content may be processed or logged for every AI query.
2. Quality and Currency: Released guidelines, handbooks, and maintained knowledge articles usually suit better than old email mailboxes or uncleaned network drives. Duplicates, conflicting versions, and expired documents are marked or excluded before indexing.
3. Protection Requirements: Trade secrets, health data, personnel files, access data, and particularly protected client data require their own risk assessment. Secrets and keys should fundamentally be in a secret management system and not in training or RAG data.
4. Authorization: The search must apply the same or stricter rights as the source system. Permissions are checked server-side; a note in the prompt is not access control.

A reliable starting set is small, professionally bounded, and measurable. After a sample with real questions, it is checked whether answers refer to current, released sources. Only then are further data classes added. This keeps traceable which set provides which benefit and where a risk arises.