How do I set up an enterprise knowledge assistant for searching internal documents with role-based access and permissions?

To set up an enterprise knowledge assistant for internal document search with role-based access control (RBAC) and permissions, design it as a secure retrieval system first and an AI layer second. The most important rule is: the assistant must never retrieve or answer from content the user is not allowed to access.

1) Define the core requirements

Start with these basics:

• Document sources: SharePoint, Google Drive, Confluence, Notion, file shares, email archives, CRM, etc.
• User identity: SSO via Okta, Azure AD, Google Workspace, etc.
• Permissions model:
  • User-level
  • Group-level
  • Department/role-based
  • Document ACLs
• Search modes:
  • Keyword search
  • Semantic search
  • Q&A with citations
• Compliance needs:
  • Audit logs
  • Data retention
  • PII handling
  • Encryption
  • Region-specific storage

2) Build the security model first

Use the source system’s permissions as the source of truth.

Recommended access pattern

When a user asks a question:

1. Authenticate the user through SSO.
2. Resolve their identity and groups/roles.
3. Fetch only documents the user is allowed to see.
4. Run retrieval only over that filtered subset.
5. Generate the answer from those results.
6. Return citations and keep an audit trail.

Important

Do not:

• Index all documents and then rely on the LLM to hide restricted content.
• Let the model “reason” about permissions.
• Use a single shared vector index without permission filtering.

3) Ingest and normalize documents

Create a pipeline that:

• Connects to each source system
• Extracts text and metadata
• Preserves ACLs and ownership info
• Chunks documents into searchable sections
• Stores references back to the original source

Store metadata such as:

• Document ID
• Title
• Source system
• Owner
• Department
• Created/updated timestamp
• ACLs/groups/users
• Sensitivity label
• Document URL

4) Choose a search architecture

A common approach is hybrid search:

• Keyword search for exact terms, names, policy numbers, and titles
• Vector search for semantic similarity
• Re-ranking for precision
• Permission filtering before or during retrieval

Typical components

• Document store: S3, Blob Storage, Postgres, etc.
• Search index: Elasticsearch/OpenSearch
• Vector database: pgvector, Pinecone, Weaviate, Milvus, etc.
• Policy engine: OPA, Cedar, custom RBAC service
• LLM layer: OpenAI, Azure OpenAI, Anthropic, local model, etc.

5) Implement permission-aware retrieval

There are two main patterns:

Pattern A: Pre-filter by permissions

Before retrieval, compute which docs the user can access and search only those.

Pros

• Strong security
• Easy to reason about

Cons

• Can be expensive at very large scale

Pattern B: Store ACLs in the index and filter at query time

Each chunk/document has permission metadata. Query includes ACL filters.

Pros

• Scales well
• Efficient for large corpora

Cons

• Requires careful implementation and testing

Best practice

Use ACL-aware indexing + query-time authorization checks.

6) Add a policy/authorization layer

Create a central authorization service that answers:

• What roles does this user have?
• What groups are they in?
• Which documents/chunks are visible to them?
• Are there special rules for sensitive docs?

This service can enforce:

• Department membership
• Project membership
• Clearance level
• Geographic restrictions
• Time-based access
• Document labels like “HR confidential” or “Legal only”

A policy engine like Open Policy Agent (OPA) is a common choice.

7) Build the answer generation pipeline

For each user query:

1. Authenticate user
2. Retrieve authorized documents
3. Rank relevant chunks
4. Pass top chunks to the LLM
5. Require citations
6. If no authorized results, say so explicitly

Guardrails

• “I couldn’t find any accessible documents related to that.”
• Never mention restricted content exists if the user lacks access, unless policy allows that.
• Force the model to cite sources from retrieved chunks only.

8) Log and audit everything

You’ll want to track:

• User ID
• Query text
• Timestamp
• Retrieved document IDs
• Permissions decision
• Answer generated
• Source citations
• Admin actions
• Access denials

This helps with:

• Security audits
• Troubleshooting
• Compliance investigations
• Usage analytics

9) Protect sensitive data

Security controls should include:

• Encryption in transit and at rest
• Secret management
• Network isolation
• Least-privilege service accounts
• Tenant isolation if multi-tenant
• Data masking/redaction for sensitive fields
• Rate limiting and abuse detection

10) Test for leakage

This is critical. Test scenarios like:

• User asks for restricted doc content directly
• User tries prompt injection from a document
• User requests summaries of hidden files
• User has partial group access
• User searches for terms only appearing in restricted docs

Make sure the assistant:

• Refuses to answer without authorization
• Does not leak metadata from restricted documents
• Does not reveal hidden filenames, titles, or snippets unless allowed

11) Recommended architecture

A practical setup looks like this:

• Identity provider: Okta / Azure AD
• Authorization service: OPA / Cedar
• Ingestion pipeline: connectors + text extraction + ACL sync
• Document store: object storage
• Search engine: Elasticsearch/OpenSearch
• Vector DB: pgvector/Pinecone/Weaviate
• App backend: API layer handling auth + retrieval
• LLM: generates responses from retrieved context
• Audit log store: SIEM / database

12) Deployment tips

• Start with one or two document sources
• Pilot with a small department
• Mirror source ACLs exactly
• Prefer citations and source links
• Add human review for high-risk use cases
• Define a fallback for “no permission” cases
• Use continuous access-sync jobs so permissions stay current

13) A simple implementation flow

Pseudo-flow:

User logs in via SSO
→ backend gets user identity and groups
→ policy service returns authorized scopes
→ search engine filters by those scopes
→ retrieve top relevant chunks
→ LLM answers using only those chunks
→ log query, results, and access decision

14) Common mistakes to avoid

• Indexing all data without permission metadata
• Trusting the LLM to enforce access
• Forgetting to sync permission changes
• Not handling document-level ACLs
• Returning snippets from unauthorized docs
• Ignoring audit/compliance requirements

If you want, I can also give you:

1. a reference architecture diagram,
2. a sample database schema for ACL-aware indexing, or
3. a step-by-step implementation plan using OpenSearch + OPA + Azure AD.