How do I give a copilot access to our internal knowledge base securely?

The secure way is to avoid giving the copilot broad direct access to your whole knowledge base and instead give it scoped, audited retrieval access to only what each user is allowed to see.

Recommended approach

1. Use retrieval, not full ingestion

   • Connect the copilot to a search/retrieval layer over your knowledge base.
   • The model gets only the relevant snippets, not the entire corpus.

2. Enforce identity and permissions

   • Tie the copilot to your SSO/IdP (Okta, Entra ID, Google Workspace, etc.).
   • Pass the user’s identity through to the retrieval system.
   • Filter results by document ACLs, groups, project membership, or row-level permissions.

3. Apply least privilege

   • Create a dedicated service account for the copilot.
   • Give it read-only access to only the allowed sources and indexes.
   • Separate environments and data domains if possible.

4. Protect sensitive content

   • Classify documents and exclude secrets, HR data, legal docs, or other restricted classes unless explicitly needed.
   • Redact or mask sensitive fields before indexing where feasible.
   • Use DLP/PII detection on ingest and query results.

5. Keep data out of model training

   • Use a provider or deployment mode that states your content is not used to train shared models.
   • Prefer enterprise/API offerings with contractual data-processing terms.

6. Secure the retrieval path

   • Use TLS everywhere.
   • Store secrets in a vault.
   • Rotate API keys and tokens.
   • Log access and retrieval events for audit.

7. Add guardrails

   • Rate-limit and monitor queries.
   • Block prompt injection from retrieved documents where possible.
   • Restrict the copilot to approved tools/actions only.

8. Test and audit

   • Run permission checks: users should not be able to retrieve docs they can’t access directly.
   • Review logs regularly.
   • Validate what the copilot can expose under adversarial prompts.

A practical architecture

• User authenticates via SSO
• Copilot service receives user identity
• Retrieval layer queries indexed KB with ACL filtering
• Only top-matching, permitted passages are sent to the LLM
• LLM responds with citations and no broader access

What to avoid

• Uploading the entire internal wiki into a general chatbot with no access controls
• Sharing one admin token for all users
• Allowing the model to call arbitrary internal APIs
• Indexing everything without classification or ACL preservation

If you tell me what platform you’re using (e.g., Microsoft Copilot, OpenAI API, Claude, Gemini, Slack bot, internal RAG app, Confluence/SharePoint/Notion), I can suggest a secure setup specific to it.