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Enterprise LLM integration means connecting a Large Language Model (LLM), such as OpenAI’s models, to your own software through an application programming interface, or API. Many SaaS teams want to add AI features this way. The first question from business leaders is usually about data: if customer information goes to an outside AI provider, where does it go, and who can see it? This post covers the default rules, what zero data retention means, and how to design the rest of the system.
What the default API rules say
Consumer chat tools and business APIs follow different rules. Free consumer tools may use your conversations to improve their models, depending on the settings. For its API, OpenAI states that data sent through the API is not used to train or improve its models unless you choose to opt in.
There is a second rule to know about. OpenAI’s documentation says abuse monitoring logs are kept for up to 30 days by default. That means prompts and responses can be stored for a period, even though they are not used for training. For teams handling highly sensitive data, 30 days of storage may not meet internal policy. Check the current OpenAI data controls page, because these terms can change.
What zero data retention means
Zero data retention (ZDR) is an option for eligible customers. With it, your content can be excluded from those abuse monitoring logs. It needs prior approval from OpenAI and acceptance of additional requirements, so it is not a setting you switch on yourself.
There are limits. Some endpoints keep application state until you delete it, and ZDR does not cover them. OpenAI’s documentation lists examples such as the Assistants API, threads and file storage. Before planning your LLM integration around ZDR, check which endpoints your feature needs.
An example: HR performance summaries
Consider a SaaS platform for human resources teams. The developers want a feature that reads a set of employee performance reviews and writes a short summary for managers. The reviews can contain salary details, health information and complaints.
With default settings, that text could sit in provider logs for up to 30 days. If the company is approved for ZDR and chooses its endpoints carefully, the platform can send the text, receive the summary and avoid that storage. The platform still needs its own records, access controls and human review of the output, because the summaries may influence decisions about people.
Five steps for safer LLM integration
Securing the API is only one part of LLM integration. The whole data path matters, from the user interface to the database and then to the API. These steps help:
- Read the provider’s current retention terms and compare them with your customer contracts.
- Ask about zero data retention if 30 days of storage is not acceptable, and confirm which endpoints it covers.
- Send only the text the feature needs, not whole records.
- Remove names, contact details and other identifiers before data leaves your servers.
- Encrypt data in transit, limit who can trigger AI features, and log access in your own system.
If your users are in Singapore, also review your duties under the Personal Data Protection Act. Our post on healthtech software and PDPA shows how privacy rules can shape a system design.
Building with Zimozi
Zimozi builds custom web and mobile applications, SaaS products, and AI agents and workflow automation. We also connect new tools to existing systems through system integrations. When we work on LLM integration for a product, we plan the full data path first: the interface, the database, the access rules and the API call. In regulated work such as fintech, that also means thinking about sector rules, as in our fintech architecture guide. Our post on agentic AI for Singapore SMEs covers a related use case.
We suggest starting your LLM integration with one clearly defined feature, such as the summary example above, and testing it with a small group before widening it.
If you are considering a similar application, Zimozi can help define a small first version and assess the technical requirements. Would you like to discuss the idea?
