Huaris AI

7 Questions to Ask Before Sending Company Data to AI

Before using AI tools, settling which data goes where, who has access and under what conditions it is stored reduces risk from the start. The seven questions below are a practical checklist to begin that conversation.

Published: 29 September 2026 · Author: Huaris AI

Why it matters

In organisations, AI often does not arrive as a tool but as a browser tab an employee opens. That makes responsibility for data security unclear. The questions are as organisational as they are technical; the answers need IT, legal and business teams together.

Seven questions

  1. Which data will go to the model?

    First classify your data: personal data, confidential information, trade secrets and public information should not follow the same rule. Put in writing which class may be used with which tools.

  2. Does the provider use this data for model training?

    It depends on the provider and the plan you use. For enterprise and API plans, providers generally state that data is not used for training by default; still, verify the current terms at the contract stage.

  3. Where is the data processed and stored?

    The country of processing and the retention period matter for cross-border transfer (KVKK Art. 9). Find out the provider's data location options and sub-processors.

  4. Who can access it and what is recorded?

    Without user-level access control and usage records (an audit trail), you cannot tell what went where when an incident occurs.

  5. Can we work without sending personal data?

    For many tasks, personal data can be masked or removed before sending. If that is not possible, a model running inside your organisation (On-Premise LLM) can be considered.

  6. How will we check the accuracy of answers?

    Models can produce wrong information fluently (Hallucination). Plan for source citation, an evaluation set and human approval for critical outputs.

  7. Do we have an acceptable use rule for employees?

    Which tools are approved, which data must not be shared and who to ask in doubtful cases? The rule should be written and supported by short training.

Conclusion

If you cannot answer some of these clearly, you can start with a data flow and deployment model exercise through our Product Development, Security and Compliance service. See the Security and Compliance page for our approach. This article is a general framework and does not replace legal advice.

Related terms: RAG (Retrieval-Augmented Generation), Hallucination, On-Premise LLM.

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