Service Mesh

Automatically upgrades all service-to-service communication to mutually authenticated TLS. This ensures that both the client and server verify each other's identity using X.509 certificates, preventing unauthorized interception or impersonation within the cluster. Unlike manual TLS configuration, the mesh handles certificate rotation and revocation transparently, establishing a zero-trust network where every connection is encrypted and authenticated by default.

Enables fine-grained control over request forwarding without changing application code. Operators can implement canary deployments by routing 5% of traffic to a new model inference service, or perform A/B testing on different retrieval-augmented generation (RAG) pipelines. Rules are based on HTTP headers, paths, or weighted percentages, allowing for blue-green deployments and seamless rollbacks of AI microservices.

Generates uniform metrics, distributed traces, and access logs for every hop in a request lifecycle. Sidecar proxies capture latency, error rates, and throughput without requiring developers to instrument code. This provides a global view of request flows through complex AI pipelines—from the API gateway to the vector database and the large language model inference engine—enabling rapid root-cause analysis of latency spikes.

Prevents cascading failures by monitoring service health and ejecting unhealthy endpoints from the load balancing pool. If a vector database becomes overloaded, the circuit breaker trips and redirects traffic or fails fast, preserving the stability of the broader AI pipeline. Configurable parameters include consecutive error thresholds, ejection durations, and outlier detection, enforcing resilience patterns at the infrastructure layer.

Replaces traditional centralized load balancers with a decentralized, client-side model. The sidecar proxy maintains a dynamic registry of healthy service instances and applies sophisticated algorithms like least request or ring hash load balancing. This ensures that requests to stateless AI inference services are distributed evenly, maximizing throughput and minimizing tail latency across the cluster.

Allows operators to deliberately introduce latency or HTTP errors into specific routes to validate the resilience of AI microservices. By injecting a 2-second delay into calls to an embedding service, teams can verify that upstream RAG pipelines handle timeouts gracefully. This practice, rooted in chaos engineering, hardens production systems against real-world network degradation.

A lightweight network proxy deployed alongside each microservice instance to abstract network logic from application code. In a service mesh, the sidecar intercepts all inbound and outbound traffic, enforcing mTLS, circuit breaking, and telemetry collection.

• Typically implemented via Envoy or Linkerd-proxy
• Enables zero-trust networking without modifying application logic
• Handles service discovery and load balancing transparently

The architectural component within a service mesh that intercepts access requests and enforces authorization decisions. In AI data pipelines, the PEP acts as the gatekeeper, validating every request against Attribute-Based Access Control (ABAC) policies before allowing retrieval from vector databases.

• Decouples policy enforcement from application logic
• Integrates with external Policy Decision Points (PDPs) via check API calls
• Provides real-time enforcement of session-bound tokens

A standard enabling real-time session revocation within the service mesh. CAEP allows the control plane to signal sidecar proxies to terminate access immediately when critical user or device state changes occur, such as a compromised credential or anomalous behavior detected by UEBA.

• Replaces static token expiration with event-driven revocation
• Prevents replay attacks against APIs exposing proprietary content
• Integrates with Shared Signals Framework (SSF) for standardized event delivery

The service mesh control plane aggregates metrics, distributed traces, and access logs from every sidecar proxy. This provides a unified view of east-west traffic between microservices composing AI data pipelines, enabling latency analysis and anomaly detection.

• Prometheus scrapes metrics from Envoy endpoints for latency histograms
• Jaeger or Zipkin consume traces for request path visualization
• Enables SLO monitoring for retrieval-augmented generation services

A network security technique that isolates workloads into granular zones with distinct security policies. Within a service mesh, micro-segmentation prevents lateral movement by compromised AI crawlers, restricting east-west traffic to explicitly authorized service-to-service paths.

• Enforces least privilege at the network layer
• Maps to Kubernetes NetworkPolicy or mesh-specific authorization policies
• Contains breach blast radius within a single service identity