Zero Trust Architecture

Trust is never granted implicitly or permanently. Every access request to a model inference endpoint is authenticated, authorized, and encrypted in real-time, regardless of its origin. This means a service account inside a Kubernetes cluster is treated with the same suspicion as a request from the public internet. Verification relies on dynamic signals—user identity, device health, service identity via SPIFFE, and behavioral analytics—not static IP addresses. The system continuously re-evaluates trust throughout the session, revoking access the moment context changes.

Every identity—human or machine—is granted only the absolute minimum permissions required to perform its specific task, and only for the duration needed. For model serving, this means a data scientist querying a model for a report should not have the same permissions as a pipeline that retrains the model. Just-In-Time (JIT) Access protocols eliminate standing privileges, granting temporary elevation for specific administrative tasks. This drastically limits the blast radius of a compromised JSON Web Token (JWT) or stolen API key.

The network is divided into isolated segments down to the level of individual workloads or containers. A model serving container cannot communicate with a logging container or a database unless an explicit, identity-aware policy allows it. This is enforced by a Policy Enforcement Point (PEP) , typically a sidecar proxy, that authorizes every east-west traffic flow. Even if an attacker compromises the inference API, they cannot pivot laterally to access the training data store or the Immutable Audit Trail.

Access decisions are based on a rich set of attributes, not just network location. A robust policy engine, such as Open Policy Agent (OPA) , evaluates every request against a Policy as Code framework. For a model serving request, this includes:

• Subject: The SPIFFE identity of the calling service.
• Action: inference, fine-tune, or admin.
• Resource: The specific model version and endpoint path.
• Context: Device posture, time of day, and data sensitivity level. This granularity enables dynamic, fine-grained Role-Based Access Control (RBAC) and attribute-based access control.

The architecture is designed with the fundamental assumption that an attacker is already present within the environment. This drives a defense-in-depth strategy where every layer provides independent security controls. For a model serving platform, this means:

• Payload Encryption is applied at the application layer, in addition to Mutual TLS (mTLS) at the transport layer.
• Runtime Application Self-Protection (RASP) is embedded in the serving runtime to detect and block attacks like prompt injection from within the application.
• All queries are analyzed by User and Entity Behavior Analytics (UEBA) to detect anomalous patterns indicative of Model Stealing Detection.

Every access request, authorization decision, and model query is logged to an Immutable Audit Trail. This is not just for compliance; the telemetry feeds the dynamic risk engine that powers continuous verification. Logs capture the full context: the HMAC Signature of the request, the Token Introspection result, the specific data fields accessed, and the model's response. This data is used to build behavioral baselines, detect drift, and provide non-repudiation for every interaction with the model serving system.