Forensics for Inference

When a forensic investigation of an inference request is required, you need an instantly retrievable, complete, tamper‑evident record of who called the model, what data was sent, and how the service responded. The evidence includes the exact payload, the identity of the caller, any approvals that were needed, and a replayable session that shows the request flow from start to finish. That level of visibility turns a mystery into a provable chain of events.

In many organizations, internal applications or batch jobs expose inference services directly. Engineers often embed static API keys in code, copy them into CI pipelines, or share them across teams. The service sits behind a load balancer and receives traffic without any mediation. When a breach or a data‑leak incident occurs, you only get generic web‑server entries that lack user context, payload details, or approval traces. The result is a blind spot that makes root‑cause analysis slow and uncertain.

Even when teams adopt modern identity providers and issue short‑lived tokens for each application, the request still travels straight to the inference endpoint. The token proves that the caller is allowed to connect, but it does not record what the caller did, does not mask sensitive fields in the response, and does not provide a way to pause a risky request for manual review. Those gaps leave forensic readiness incomplete.

Why forensics matters for inference

Inference workloads often handle personally identifiable information, financial figures, or proprietary model inputs. A single mis‑routed request can expose raw data, reveal model internals, or trigger downstream actions that affect customers. Forensic readiness means you capture every interaction so you can examine it later without altering the original request or response. You also mask any data that should not leave the environment before it reaches your logs.

How a Layer 7 gateway enables forensics for inference

hoop.dev places a Layer 7 gateway between the caller and the inference service. hoop.dev records each request, logs the full payload, and stores a replayable session that security analysts can inspect. hoop.dev masks sensitive fields in responses according to policy, ensuring that logs never contain raw personal data. When a request matches a high‑risk pattern, hoop.dev pauses the flow and requires a human approver before the model is invoked.

Because the gateway runs inside the customer network, the original credentials never leave the control plane. hoop.dev never exposes the service credential to the caller, and it enforces the policy at the only point where traffic can be inspected. hoop.dev captures every query, every response, and every approval decision in an immutable audit trail, giving you a single source of truth for forensic evidence.

Key forensic capabilities delivered by hoop.dev

• Session recording – a binary‑level capture that you can replay to see exactly what was sent and received.
• Payload logging – full request and response bodies are stored, with optional inline masking of fields such as credit‑card numbers or health identifiers.
• Just‑in‑time approval – risky inference calls trigger an approval workflow that must be satisfied before execution.
• Identity‑driven audit – the caller’s OIDC or SAML identity attaches to every log entry, providing clear accountability.
• Replay for incident response – analysts can replay a session in a sandbox to reproduce the exact conditions that led to an issue.

These capabilities turn a raw inference endpoint into a forensically sound service that satisfies internal investigations and external audit requirements.

Getting started

To try the solution, follow the getting started guide and configure the gateway to front your inference API. Detailed policy examples and best‑practice recommendations are available in the learn section. The open‑source repository contains all the code you need to self‑host and extend the platform.

FAQ

Can hoop.dev capture encrypted traffic?

hoop.dev terminates TLS at the gateway, inspects the plaintext payload, and then re‑encrypts traffic to the downstream inference service. This allows full visibility while preserving end‑to‑end security.

Does masking affect model accuracy?

Masking applies only to logs and audit records. The live request and response sent to the model remain unchanged, so inference accuracy is not impacted.

Is the audit trail tamper‑proof?

hoop.dev writes each session to a storage backend that is separate from the gateway process. Because the gateway is the only component that can create records, any alteration would be evident when the replay is attempted.

Explore the source code and contribute on GitHub.