Tenant-Aware Indexing

Assigns dedicated hardware resources—separate disks, memory, and compute—to each tenant's vector index. This provides the strongest isolation guarantee by eliminating shared memory buses and cache lines.

• Zero noisy neighbor risk: One tenant's heavy query load cannot degrade another's latency
• Independent encryption: Each partition uses unique keys, enabling crypto-shredding on offboarding
• Compliance ready: Satisfies strict regulatory requirements for data residency and physical separation

Creates isolated collections within a shared database instance using namespace identifiers. Each tenant's embeddings exist in a logically distinct space, with queries scoped to a single namespace.

• Resource efficiency: Shares underlying infrastructure while maintaining isolation
• Dynamic provisioning: New tenants are added instantly without hardware allocation
• Query scoping: namespace:tenant_123 is prepended to every vector search, preventing cross-tenant retrieval

Assigns distinct embedding models or fine-tuned variants to each tenant, ensuring that semantically identical concepts produce mathematically incompatible vectors across tenants.

• Cryptographic separation: Even if a vector leaks, it is meaningless outside its tenant's embedding space
• Domain optimization: Each tenant's model can be fine-tuned on their specific vocabulary
• Model isolation: Prevents transfer attacks where an adversary uses one tenant's embeddings to probe another's index

Partitions the vector index using tenant_id as a mandatory metadata filter on every document chunk. The query layer enforces this filter before any similarity computation occurs.

• Pre-filtering: Unauthorized vectors are excluded before distance calculations, not after
• Immutable tags: Tenant metadata is cryptographically signed to prevent tampering
• Hybrid enforcement: Combines with role-based access controls for defense in depth

Intercepts every incoming vector search at the API gateway and transparently injects the authenticated tenant's scope. The application developer never handles tenant logic directly.

• Middleware enforcement: Tenant context is extracted from JWT claims or API keys
• Developer simplicity: Application code writes generic queries; the gateway handles isolation
• Audit consistency: Every query is logged with its resolved tenant context for compliance

Implements isolated deletion and compaction processes per tenant, ensuring that removing one organization's data does not fragment or corrupt another's index structure.

• Independent lifecycle: Each tenant's index can be rebuilt, compacted, or vacuumed on its own schedule
• Secure deletion: Vectors are overwritten with cryptographic zeros, not just marked as deleted
• Zero cross-contamination: Compaction merges never span tenant boundaries

A data isolation strategy that applies distinct encryption keys and access policies to individual physical or logical shards of a vector index. Each tenant's partition is a cryptographically isolated unit.

• Physical Isolation: Dedicated hardware resources per tenant
• Key Management: Unique master keys derived per partition
• Compliance: Meets strict regulatory requirements for data residency

A pre- or post-query access control technique that restricts vector search results by applying Boolean constraints on associated document tags. This is the primary enforcement mechanism for tenant-aware indexing.

• Pre-filtering: Narrows the search scope before similarity computation
• Post-filtering: Prunes results after the ANN search completes
• Example: tenant_id == 'org_123' AND department == 'legal'

The application of role-based access controls to entire vector collections, defining which user groups can read, write, or manage specific sets of embeddings. This maps directly to tenant boundaries.

• Roles: Reader, Writer, Admin scoped to a single collection
• Integration: Ties into existing enterprise SSO and LDAP systems
• Auditability: Every role assignment is logged for compliance

The immutable recording of all access, query, and modification events within a vector database. Essential for proving that tenant data isolation has not been breached.

• Events Tracked: Query text, returned IDs, user context, timestamp
• Tamper-Proof: Append-only logs stored in separate security domains
• SIEM Integration: Feeds into Splunk or Datadog for real-time alerting

A throttling mechanism that restricts the number of vector queries a user can make based on the conceptual topic of the query. Prevents a malicious tenant from probing neighboring tenant spaces.

• Topic Detection: Classifies query intent in real-time
• Thresholds: '100 queries/min on financial data'
• Response: Returns a 429 status and triggers an alert on violation