June 2, 2026
About LabKey
AI assistants can now connect directly to your LIMS, read your data model, and generate queries and scripts on your behalf. Getting that connection right requires asking a few specific questions before you go live.
The connection point is usually something called an MCP (Model Context Protocol) server: a standardized interface that lets AI assistants interact with your systems in a structured way. When an AI connects to your LIMS via MCP, it reads your data model, what schemas exist, what tables are in each schema, what columns those tables contain, and uses that structure to write queries and scripts. It doesn’t retrieve your sample records, assay results, or annotations directly. Your data stays in your LIMS until you run a script that retrieves it through your normal authentication path, and the AI tool just writes the script.
These are five questions designed to help you determine whether a LIMS-AI connection is set up safely.
1. What does the AI actually have access to when it connects?
Schema access means the AI learns the shape of your data: what tables exist, what columns they contain. Data access means it can read the values inside those tables. These are different, and not every integration treats them that way.
Ask your LIMS vendor specifically: when the AI connects, does it see column names and table structures, or does it see the records inside those tables? If they can’t give you a clear answer, that’s worth noting.
2. Who can enable the integration — and for whom?
This varies significantly by vendor and by how the integration is set up, so it’s worth asking explicitly rather than assuming.
Some integrations are provisioned per user; others use shared credentials that extend the same access to anyone using the same AI client. In multi-team environments, that distinction matters. A researcher in one project may not need visibility into another team’s data, and the integration should respect that boundary the same way your other access controls do.
Ask your vendor how the connection is provisioned, whether it can be scoped by user or team, and what the process is for revoking access when someone’s role changes.
3. Can the LIMS AI integration be scoped to read-only access?
Read-only access is the right default for most users. Write access should be granted deliberately, to specific people, for specific reasons.
How permissions are inherited varies by implementation. Ask your vendor whether the integration can be configured for read-only access, whether that’s the default or something that has to be explicitly set up, and what happens if the connection is established using credentials with broad system access. If write access is possible and hasn’t been explicitly scoped, ask your vendor how to lock it down.
4. Does the AI session operate within your existing LIMS access controls?
Your existing access controls should apply to the AI session the same way they apply to any other user. Ask whether the AI session inherits the permissions of the credentials used to connect, or whether it gets a separate, broader set of access by default.
If the integration can see across your entire system regardless of how your permissions are structured, push back on that, particularly if your lab requires LIMS validation.
5. Where does your data go when your LIMS talks to AI?
When you use an MCP-connected AI tool, your queries and the AI’s responses pass through the AI provider’s infrastructure: Anthropic, Google, OpenAI, depending on which client your team uses. What that means for data retention and privacy depends on the AI provider’s terms, not your LIMS vendor’s terms. Enterprise agreements with those providers typically include stronger data handling commitments than consumer tiers.
Ask about the data handling terms for the AI client your team uses, not just whether your LIMS is secure.
LIMS AI Integration FAQ
Not directly. The AI reads your data model: schema names, table names, column names and types. Your actual sample records aren't retrieved unless you run a script that explicitly pulls them, and that retrieval happens through your normal LIMS authentication using the permissions attached to your credentials.
Yes, if your LIMS supports folder-level or project-level scoping. Combining that with a read-only API key gives you a well-bounded surface area. Ask your vendor specifically how session scope is configured and whether it can be restricted per user.
The AI can suggest operations that modify or delete data, and those operations can be executed by whoever is running the session. Admin keys should be reserved for users who need write access and understand what they're authorizing. For most use cases, a read-only key is the right starting point.
The AI client processes your prompts and the context it receives through its own infrastructure. Your schema metadata passes through that infrastructure as part of normal operation. Whether raw data values pass through depends on what scripts you run and what they retrieve. Review the data handling terms for whichever AI client your team uses, particularly in a regulated environment. Enterprise agreements typically include specific commitments that consumer accounts don't.
Your LIMS software needs to support MCP or have a comparable structured integration. Not all LIMS products support it. If yours doesn't, your team is likely working around this by exporting data manually and pasting it into AI tools, which creates more data handling risk than a structured connection.
Connecting AI to your LIMS data isn't inherently a compliance problem. How you configure the connection and which AI tools your team uses determines whether it becomes one. The questions above are a reasonable starting checklist. Your compliance team should also review the data handling terms for any AI provider involved.
LabKey LIMS AI Solutions
If you’re evaluating LIMS options, LabKey LIMS is built around the kind of structured, governed data that makes AI connections useful and easier to govern responsibly. Its MCP server gives AI assistants access to your data model within boundaries your team defines, using folder-level access controls and API key permission scoping.
Tour LabKey LIMS to see it in action.