Resource / Safe AI
Safe AI in a Norwegian business is not only a model choice. It is about which data is used, who gets access, what is logged, which vendors process information, and where humans must review suggestions before they become actions.
Last updated: May 25, 2026
Before an AI solution is built, the business should know which data is public, internal, confidential, personal data, or especially sensitive. The data type decides where the solution can run, which vendors can be used, and whether data may be sent to an external model.
AI should not give users more insight than they already have. RAG, agents, and automation must filter by role, department, customer, project, or document type. This matters when AI retrieves from shared folders, CRM, case systems, or internal knowledge bases.
Production-near AI should log what the user asked, which tools or sources were used, and which actions were suggested or performed. Logging must be balanced against privacy, but without traceability it is hard to improve quality and handle incidents.
Not every AI suggestion needs the same level of review. Drafts, summaries, and search can often be lower risk, while decisions that affect customers, finance, HSE, rights, or operations should have approval, limited tools, and clear stop rules.
A safe AI solution should describe which vendors are used, where data is processed, whether data is stored, how access is handled, and which agreements apply. This should be understandable for leadership, IT, and employees using the system.
Use these points before AI is connected to internal documents, customers, operational systems, or automated actions.
List data sources, data types, owners, access levels, and whether the data includes personal data.
Define who can see what, and how AI should enforce the same boundaries as the source systems.
Clarify model, hosting, data processing, storage, logging, and agreement basis before production data is used.
When AI answers from documents, the answer should show which sources or systems influenced the conclusion.
Decide what AI can do directly, what is only a suggestion, and what always requires human approval.
Test with real questions, expected answers, failure scenarios, and clear go/no-go criteria.
Yes, but only when data sources, access, vendors, logging, and privacy are clarified. Internal documents should not be connected to AI without a clear access model.
That AI gets access to too broad data sources and answers without respecting the user's role or document access.
No. Review level should follow risk. Low-risk drafts can be simpler, while actions that affect customers, finance, HSE, or operations should require approval.
Choose one workflow, one user group, concrete data sources, clear stop rules, and measurable quality criteria.
Aprex builds AI solutions with clear boundaries for data, access, and actions. The goal is for AI to support employees without creating hidden data leaks, unclear ownership, or automated decisions nobody can audit.
Send the workflow you are considering, which data is involved, who should use the solution, and what AI may be allowed to do.
Contact Aprex about safe AI