Tenant Isolation

Enforces separation through software-defined boundaries rather than dedicated hardware. Each tenant's cached embeddings and responses are tagged with a unique tenant identifier and access-controlled at the application layer.

• Namespace isolation: Each tenant operates within a dedicated logical namespace or keyspace prefix
• Metadata tagging: Every cache entry carries an immutable tenant ID for access validation
• Query-time filtering: The cache layer automatically scopes all read/write operations to the authenticated tenant's partition
• Example: A Redis cluster using key prefixes like tenant_a:embeddings:* and tenant_b:embeddings:* to prevent cross-tenant access

Dedicates separate hardware resources—compute, memory, and storage—to each tenant, eliminating any shared attack surface. This is the gold standard for sovereign AI deployments handling classified or regulated data.

• Dedicated cache nodes: Each tenant receives exclusive access to specific cache instances or clusters
• Network segmentation: VLANs or VPCs isolate tenant traffic at the network layer
• Noisy neighbor prevention: Eliminates resource contention where one tenant's workload degrades another's performance
• Example: Deploying separate Milvus or Qdrant vector database instances per defense contractor within an air-gapped environment

Uses encryption to enforce tenant boundaries even when data shares physical storage. Each tenant's cached data is encrypted with a unique key, making it cryptographically impossible for one tenant to read another's data.

• Per-tenant encryption keys: Derived from a tenant-specific master key managed in a KMS or HSM
• Application-layer encryption: Data is encrypted before entering the cache, so even cache administrators cannot access plaintext
• Secure enclave integration: Combines with confidential computing to encrypt data in use within TEEs
• Example: Encrypting cached LLM responses with AES-256-GCM using keys unique to each organizational tenant in a sovereign cloud

Implements authentication and authorization at every cache interaction point to verify tenant identity before allowing data access. This prevents horizontal privilege escalation where one tenant accesses another's cached inference results.

• JWT-bound sessions: Cache requests carry signed tokens embedding the tenant scope
• Policy-as-code: Fine-grained access policies define which operations each tenant role can perform
• Audit logging: Every cache access is logged with tenant identity for forensic analysis
• Example: An API gateway validating OAuth2 tokens and injecting tenant context headers before requests reach the semantic cache layer

Ensures that tenant isolation respects jurisdictional boundaries by pinning each tenant's cached data to specific geographic regions. This is essential for compliance with regulations like GDPR, CCPA, and sovereign data mandates.

• Geofenced cache nodes: Cache infrastructure is deployed within tenant-specified legal jurisdictions
• Data locality guarantees: Cache replication never crosses tenant-defined geographic boundaries
• Jurisdictional metadata: Each cache entry is tagged with its permitted processing region
• Example: A European tenant's semantic cache data stored exclusively in Frankfurt-region nodes, while an Asian tenant's data remains in Singapore

Ensures that eviction policies operate strictly within a tenant's partition. When memory pressure triggers eviction, only the target tenant's entries are considered—preventing one tenant's cache thrashing from evicting another tenant's high-value cached responses.

• Per-tenant LRU queues: Each tenant maintains an independent eviction candidate list
• Resource quotas: Hard memory limits per tenant prevent any single tenant from monopolizing cache capacity
• Isolated TTL enforcement: Time-based expiration operates independently per tenant namespace
• Example: A tenant exceeding their allocated 10GB cache quota triggers eviction of their own least-recently-used entries without affecting neighboring tenants