Access Control & Authorization

Production Large Language Model (LLM) applications require robust security perimeters. Unlike traditional applications where users query static database rows, AI systems execute complex workflows (retrieval-augmented generation, tool calling, database writes) on behalf of users.

Enforcing safety boundaries requires shifting from simple front-end validation to a backend Policy-Driven Authorization model.

🔒 1. Secure AI Authorization Flow

A secure LLM application wraps the model generation loop within input policies, vector store filters, and output sanitizers.

🏗️ 2. Core Access Control Patterns

AI systems implement three key authorization patterns depending on scale and metadata complexity:

Role-Based Access Control (RBAC)

Simplest pattern. Users are assigned static roles (e.g., guest, employee, admin), which map directly to permitted actions.

• Best For: Restricting access to specific models (e.g., only pro subscribers can query GPT-4o, while free users are routed to GPT-4o-mini).

Attribute-Based Access Control (ABAC)

Evaluates dynamic attributes at runtime (e.g., client IP, country, active session time, or document confidentiality level).

• Best For: Preventing data egress during out-of-office hours or blocking cross-border data transfer compliance violations.

Relationship-Based Access Control (ReBAC)

Enforces access based on relationships between entities (e.g., “User A is a member of Team B, which owns Document C”).

• Best For: Granular knowledge base permissions where users can only query documents uploaded by their workspace team.

📂 3. Secure RAG Ingestion (Row-Level Security)

In a RAG system, developers often make the mistake of performing a vector search across the entire database, and then filtering the retrieved results post-generation. This introduces information leakage and degrades performance.

Instead, always enforce Row-Level Security (RLS) by injecting authorization filters directly into the database query segment (Pre-Query Filtering).

Secure pgvector Ingestion with Row-Level Security

The following SQL schema illustrates how to configure Row-Level Security in PostgreSQL to ensure users can only search vector embeddings they are authorized to access:

-- Create tenant organization table
CREATE TABLE organizations (
    id UUID PRIMARY KEY,
    name VARCHAR(255) NOT NULL
);
 
-- Create a table for documents with owner organization relations
CREATE TABLE document_store (
    id UUID PRIMARY KEY,
    org_id UUID REFERENCES organizations(id) NOT NULL,
    content TEXT NOT NULL,
    embedding vector(1536) NOT NULL
);
 
-- Enable Row-Level Security on the document store
ALTER TABLE document_store ENABLE ROW LEVEL SECURITY;
 
-- Create an RLS policy matching tenant org_id to the session user context config
CREATE POLICY tenant_document_access_policy ON document_store
    FOR SELECT
    USING (org_id = NULLIF(current_setting('app.current_org_id', true), '')::uuid);

Implementing Secure Query Execution (Python)

When querying the database, the backend application must set the active session context inside a transaction block before invoking the vector search:

import psycopg2
from typing import List, Dict
 
def secure_vector_search(conn, org_id: str, query_embedding: List[float], limit: int = 5) -> List[Dict]:
    with conn.cursor() as cur:
        # 1. Start transaction block
        cur.execute("BEGIN;")
        
        # 2. Inject session-level tenant identifier context
        cur.execute("SET LOCAL app.current_org_id = %s;", (org_id,))
        
        # 3. Execute vector cosine similarity search
        # RLS automatically restricts traversal to rows matching the org_id
        cur.execute("""
            SELECT id, content, (embedding <=> %s::vector) as distance 
            FROM document_store 
            ORDER BY embedding <=> %s::vector 
            LIMIT %s;
        """, (query_embedding, query_embedding, limit))
        
        results = [{"id": row[0], "content": row[1], "distance": row[2]} for row in cur.fetchall()]
        
        # 4. Commit transaction, clearing local session context
        cur.execute("COMMIT;")
        return results

🔌 4. Fine-Grained Authorization with Policy Engines

For complex microservices, decouple authorization logic from application code by utilizing centralized Policy-as-Code engines (such as Open Policy Agent or Permify).

Below is an authorization schema configured for a multi-tenant corporate handbook knowledge base:

// Policy definition representing access constraints
entity user {}
 
entity workspace {
    relation owner @user
    relation employee @user
}
 
entity knowledge_document {
    relation parent_workspace @workspace
    attribute classification_clearance integer
    
    // Admins / Workspace owners can read any clearance level
    permission admin_view = parent_workspace.owner
    
    // Employees can view documents if their clearance matches the document's classification
    permission employee_view = parent_workspace.employee and check_clearance(classification_clearance)
    
    action view = admin_view or employee_view
}
 
rule check_clearance(classification_clearance integer) {
    // Only return documents with classification level <= 2 (Public/Internal) for employees
    classification_clearance <= 2
}

💼 5. Best Practices

1. Least-Privilege API Keys: When configuring external tools, never use admin database keys or wildcard service accounts. Use scoped, read-only API keys for vector search.
2. Enforce Output Redaction: Implement a post-generation regex/PII classifier loop to block LLM responses containing credit cards, passwords, or emails, preventing accidental data exposure.
3. Auditable Telemetry: Log all authorization decisions (ALLOW or DENY) and tie them to tracing IDs (trace_id) in your observability stack to detect adversarial scanning attempts.

🔗 Related Sections

• Observability & Tracing — Logging security alerts, token consumption anomalies, and tracing injection attempts.
• Agent Security & Guardrails — Scoped tool roles, network isolation, and sandboxing code execution runtimes.
• Anatomy of RAG Systems — Multi-stage retrieval architectures and vector index configurations.