ChatGPT for Business: Data Risks and On-Premise Alternatives
Staff are already using public AI, with or without permission. It is better for a company to set clear rules and options than to pretend it is not happening.
The right question is not "may we use ChatGPT or not" but "which data may go into which service". Public AI services are useful for general tasks. Trouble starts when internal documents, customer data or contracts go in without rules.
Which data risks should you understand?
- Data leaves the company. Typed text is processed on the provider's servers. How data is treated (retention, use for model training) differs between plans and providers and can change. Read the terms of the plan actually in use, do not rely on memory.
- Personal accounts. Staff using free personal accounts leave the company with no control or audit trail.
- Personal data. Entering customer or employee data may conflict with personal data protection duties.
- Confidentiality. Leaked tender documents, prices or strategy are hard to recall.
Simple rules you can start this week
- Set three data classes: allowed, allowed with anonymisation, and forbidden (contracts, customer data, credentials, secret source code).
- Provide company accounts with privacy settings that have been checked, not personal ones.
- Train staff with real examples of what is and is not allowed.
- Record who uses what for audit purposes.
When does on-premise or a private LLM make more sense?
- The documents you want AI to help with are confidential or regulated.
- Your agency or industry requires data to stay in your own environment.
- You need answers from internal documents (SOPs, contracts, reports) with source references.
- Usage is large enough that per-use cost of public services becomes a factor.
What must you accept with on-premise?
Open models you run yourself are generally smaller than the largest public models, so quality on hard tasks can differ. You also carry the hardware (GPU, RAM, storage), updates and security. So start with one clear use case, such as SOP question answering, and measure results before expanding.
How to set up a sensible pilot
Pick one tidy set of documents, define 30 to 50 test questions with known answers, run it with a few users for several weeks, then assess answer accuracy and user trust. Decide on hardware after the pilot, not before.
Sentra builds AI assistants inside your company environment. See the Private LLM service, SentrAI and corporate solutions.
Frequently asked questions
Is ChatGPT safe for company data?
It depends on the plan and settings. Business plans usually have different data policies from free accounts. Read the provider's current terms and define which data classes may be entered.
Is a private LLM always better?
No. It is better for data control and internal-document references, but quality on hard general tasks can be lower than the largest public models.
How much hardware is needed?
It depends on model size and number of users. Decide after a pilot, not before.
Should staff be banned from public AI?
Total bans are often ignored. Data-class rules and an official alternative work better.
Tell us about the process. We'll help map out the most proportionate approach.
