Managing security and access control in LlamaIndex involves integrating with existing security systems, leveraging metadata for granular control, and ensuring data encryption. LlamaIndex doesn’t enforce security out of the box, so developers must implement these measures by customizing data ingestion, query pipelines, and storage. The framework provides flexibility to incorporate authentication, role-based access, and encryption using standard tools and libraries.
First, integrate authentication and authorization systems into your application. For example, use OAuth or API keys to verify user identity before allowing access to LlamaIndex operations. During query execution, filter results based on user roles or permissions. Suppose your application uses Firebase Authentication: extract the user’s role from a JWT token and apply it to filter documents. In LlamaIndex, you can modify queries dynamically using MetadataFilters to restrict results to documents tagged with the user’s department (e.g., department: "engineering"). This ensures users only retrieve data they’re authorized to access.
Second, enforce document-level access control using metadata. When ingesting data, tag each document or node with metadata like access_level or project_id. For instance, a healthcare app might tag patient records with role: "doctor" or role: "admin". During queries, use LlamaIndex’s query pipelines to inject these metadata filters automatically. Tools like SQLAlchemy or custom Python logic can map user roles to allowed metadata values. For example, a query for “patient history” from a nurse role might append filters=MetadataFilters(filters=[{"role": "nurse"}]), limiting results to records explicitly tagged for nurses.
Third, secure data storage and transmission. Encrypt sensitive documents before indexing them—using libraries like cryptography for AES encryption—and decrypt only when necessary for processing. If using cloud storage (e.g., AWS S3), enable server-side encryption and configure IAM policies to restrict access. For data in transit, enforce HTTPS/TLS when LlamaIndex interacts with external services like vector databases (e.g., Pinecone) or LLM APIs. Additionally, audit access logs to detect unauthorized activity. For example, log all query attempts and flag requests that try to bypass metadata filters. By combining encryption, secure storage, and auditing, you create multiple layers of protection around your indexed data.