Adding SSO and role-based access to a private LLM deployment means putting an identity provider in front of the chat interface and mapping user groups to permission levels, rather than treating the LLM as an anonymous internal tool. Most private LLM front ends, including Open WebUI, LibreChat and AnythingLLM, support OIDC or SAML, so they can connect directly to Okta, Azure Active Directory, Google Workspace or Keycloak for authentication, which removes the need for separate local accounts and passwords. Role-based access control then determines which models, data sources and features each group can reach, for example limiting a finance team's assistant to finance documents while blocking access to HR records, and restricting who can upload new documents into a shared knowledge base versus who can only query it. For regulated environments, this layer should also drive audit logging, so every query is tied to an authenticated identity rather than a shared service account, which matters for both security investigations and compliance reporting. Session timeouts, API key scoping for programmatic access, and network-level restrictions like VPN or zero-trust access add further control on top of SSO and RBAC. Nanobase AI configures these identity and access layers as a standard part of every private LLM rollout.
Sequence matters: identity before permissions
Teams sometimes try to design a detailed role and permission matrix before confirming that basic SSO login even works end to end, which leads to rework once real identity provider group data turns out to look different than assumed. The correct sequence is to get SSO login fully working first, confirm what group and attribute data the identity provider actually sends, and only then design the role-to-permission map, since the permission design depends on real data the IdP integration reveals.
Role design for a private LLM
The role list below is a starting template, not a fixed standard; it should be reshaped around the identity provider groups a company already has, not invented from scratch.
| Role | Typical permissions |
|---|---|
| Standard employee | Chat access, retrieval over company-wide public documents, no admin functions |
| Department member (e.g., Legal, Finance) | Above, plus retrieval over department-restricted document sets |
| Power user / prompt engineer | Above, plus access to model parameter tuning, saved prompt templates |
| Administrator | User management, model and connector configuration, full audit log access |
| Auditor / compliance reviewer | Read-only access to logs and usage reports, no chat or configuration access |
Implementation steps
- Register the private LLM's chat interface as an application in the identity provider (Okta, Azure AD, Google Workspace, or Keycloak) and configure OIDC or SAML.
- Confirm the exact claims and group memberships the identity provider sends on login, since role mapping depends on this data being present and correctly formatted.
- Design the role matrix based on real organizational groups rather than a generic template, adjusting the table above to match how the company actually segments access.
- Map identity provider groups to the chat interface's internal roles, testing with at least one account from each role before wider rollout.
- Disable local account creation once SSO is confirmed working, removing the fallback path that would otherwise bypass centralized identity management.
- Set up periodic access reviews, since role membership drifts over time as employees change teams, and a stale mapping is a common source of over-permissioned access.
A role matrix that is designed before the SSO integration is live tends to need revision anyway once real group data arrives, so building the matrix second rather than first avoids wasted design work.
Document-level access adds a second dimension
Role-based access controls who can use the system and what functions they can reach, but a separate, equally important control has to govern which documents a given role can retrieve through RAG. A legal team role should retrieve legal documents, not the entire company's HR files, which means the retrieval layer needs its own permission check tied to the same identity, not just the chat interface's role setting. This is a common gap: SSO and RBAC get implemented at the application layer while the retrieval layer quietly indexes and serves every document to every authenticated user regardless of role.
Frequently asked questions
Can RBAC be added after a private LLM is already live with users?
Yes, but it requires a migration step: existing local accounts need to be mapped to identity provider accounts and roles, and any document access already granted needs to be re-evaluated against the new role structure, which is more disruptive than building RBAC in from the start.
Does RBAC need to match the document access controls already in SharePoint or Google Drive exactly?
Ideally yes, since duplicating a separate permission system creates drift risk; where possible, the retrieval layer should check the source system's existing permissions at query time rather than maintaining a second, parallel access control list.
How often should role mappings be reviewed?
Quarterly is a reasonable default for most organizations, aligned with typical access-review cycles for other enterprise applications, though organizations with high employee turnover or frequent reorganizations may need a shorter cycle.
How Nanobase AI helps
Nanobase AI implements SSO and RBAC for private LLM deployments end to end, from identity provider integration through role design and document-level access controls in the retrieval layer used to connect a private LLM to company documents, so access follows the same organizational structure as every other enterprise application. The team also sets up the periodic access review process that keeps role mappings from drifting out of date.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.