Authorization

The foundational security principle that every user, service account, or application should have the minimum necessary permissions to perform its function. In vector databases, this means:

• A search microservice may only have read permissions on specific collections.
• An embedding ingestion pipeline may only have write permissions, but not delete.
• An admin console user might have full CRUD (Create, Read, Update, Delete) on indexes but no access to raw system logs. This minimizes the attack surface and limits potential damage from compromised credentials.

An authorization model where permissions are assigned to roles, and users are assigned to these roles. This simplifies management in enterprise vector database deployments.

Common RBAC Roles:

• VectorDB_Viewer: Can execute similarity searches (SELECT/QUERY) on assigned collections.
• VectorDB_Ingester: Can insert and update vectors and metadata (INSERT, UPDATE).
• VectorDB_Developer: Can create and delete collections and indexes (CREATE, DROP).
• VectorDB_Admin: Full system control, including user and role management. Roles are mapped to specific API endpoints and query types, providing a scalable way to manage teams.

Authorization policies that extend beyond the collection level to govern access to individual vectors, metadata fields, or query results. This is critical for multi-tenant and highly secure environments.

Implementation Mechanisms:

• Attribute-Based Access Control (ABAC): Decisions based on user attributes (department, clearance), resource attributes (vector tags, project ID), and environmental context (time of day).
• Row-Level Security (RLS): Policies applied at query runtime to filter which vectors are returned based on the user's identity. For example, a query for "customer feedback" only returns vectors where metadata['tenant_id'] = current_user.tenant_id. This ensures users only access the specific data slices they are authorized to see.

The portable, verifiable unit that conveys a user's or service's permissions after authentication. The most common standard is the JSON Web Token (JWT).

Token Contents:

• Sub (Subject): The unique user/service identifier.
• Aud (Audience): The intended recipient (e.g., vector-db-api).
• Exp (Expiration): Critical for limiting token lifetime.
• Custom Claims: Role names, project IDs, or permission scopes like "scope": "vectors:read collection:prod_embeddings". The vector database's API gateway or internal authorization engine validates the token's signature and extracts this context to enforce access rules on every request.

The core runtime component inside the vector database that evaluates an incoming request against a set of authorization policies to make an allow/deny decision.

Evaluation Process:

1. Request Parsing: Extracts the principal (who), action (query, insert), resource (collection:docs), and context from the API call and token.
2. Policy Retrieval: Fetches all relevant policies for the principal and resource.
3. Decision Logic: Uses a logic model (often Policy-Based Access Control (PBAC) or ReBAC) to determine if the request is permitted.
4. Enforcement: Allows the query to proceed or returns a 403 Forbidden error. High-performance engines are essential to avoid adding latency to vector search operations.

Authorization decisions must be logged immutably to support security audits, compliance (SOC 2, ISO 27001, HIPAA), and forensic investigations.

Key Logged Attributes:

• Timestamp and unique request ID.
• Principal Identifier (user ID, service account).
• Authorization Decision (ALLOW or DENY).
• Requested Resource and action.
• Applied Policy that led to the decision.
• Query Metadata (e.g., index searched, filter parameters). These logs feed into Security Information and Event Management (SIEM) systems to detect anomalous access patterns, such as a user suddenly querying millions of vectors outside their normal scope.

Fine-Grained Access Control extends beyond collection-level permissions to govern access at the level of individual vectors or metadata fields. This is critical for multi-tenant applications and scenarios with highly sensitive data. Mechanisms include:

• Attribute-Based Access Control (ABAC): Grants access based on user attributes (e.g., department='R&D'), resource attributes (e.g., project='Alpha'), and environmental conditions.
• Row-Level Security (RLS): Applies dynamic filters to queries based on the user's identity, ensuring they can only retrieve vectors tagged with metadata they are authorized to see (e.g., WHERE tenant_id = current_user_tenant()).

API Keys are not just for authentication; they are a primary vehicle for authorization. Each key is provisioned with specific scopes or permissions that define its allowable actions. A key used by a backend ingestion service might have vectors:write and collections:create scopes, while a key for a public-facing search API would be restricted to vectors:read. This model enables secure machine-to-machine (M2M) communication and allows for key rotation per service or function, limiting blast radius if a key is compromised.

The core of any authorization system is its policy evaluation engine. When a request arrives (e.g., QUERY collection_A), the engine:

1. Identifies the principal (user/service) and their attributes.
2. Retrieves relevant policies (RBAC roles, ABAC rules, RLS filters).
3. Evaluates the request against these policies in a consistent, centralized manner. This engine ensures deterministic access decisions are made for every single operation, providing a security audit trail. Decisions are often cached for performance but re-evaluated upon policy changes.

Authorization is incomplete without auditability. Every access decision—granted or denied—should be logged to an immutable audit log. Each log entry typically includes:

• Timestamp and unique request ID.
• Principal identity and source IP.
• Action attempted (e.g., DeleteIndex).
• Target resource (e.g., collection/project_docs).
• Authorization decision and the policy/rules that led to it. These logs are essential for security forensics, demonstrating compliance with regulations (like SOC 2, HIPAA, GDPR), and detecting anomalous access patterns that may indicate credential theft or insider threats.

A security model where access permissions to vector database resources are assigned to roles, and users are granted access by being assigned to one or more of these roles. This simplifies management by decoupling permissions from individual users.

• Example Roles: read-only-user, data-ingester, collection-admin, system-owner.
• Administrative Benefit: Permissions are managed at the role level; adding a user to a role automatically grants all associated permissions.

A security model that allows administrators to define precise permissions at the level of individual database objects, such as specific collections, indexes, or metadata fields. It extends beyond coarse, collection-level access.

• Granularity: Can permit a user to query Collection A but only write to Collection B.
• Use Case: Essential for multi-tenant SaaS platforms where a single database instance serves many customers, requiring strict data isolation.

A core security principle mandating that users, accounts, and processes should have only the minimum levels of access—or permissions—necessary to perform their legitimate functions within a vector database.

• Implementation: Starts with zero permissions and adds only what is explicitly required for a task.
• Security Impact: Dramatically reduces the attack surface and limits the potential damage from compromised credentials or insider threats.

A security mechanism that specifies which users or system processes are granted access to specific objects (e.g., a vector collection), and what operations (read, write, delete) are allowed on those objects. It is a direct implementation of permissions.

• Structure: Often a list of entries tying a principal (user/role) to a set of permissions on an object.
• Contrast with RBAC: ACLs are often object-centric (who can access this thing?), while RBAC is user/role-centric (what can this role do?).