Obsidian Security Alternatives for AI Traffic Controls
Obsidian Security teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
TL;DR
- Obsidian Security is a fit for a buyer whose existing security stack already centers on its documented administration and policy controls.
- A regulated AI workflow also needs an independent record for each HTTP request, including the acting identity and the policy decision.
- Compare the request boundary, evidence write path, model coverage, and failure behavior before selecting an alternative.
The buying decision
Obsidian Security is part of a specific security architecture, and a serious evaluation starts with its documented controls instead of a feature checklist. The provider documentation describes its own administration surface and deployment model. That is useful evidence. A buyer still has to trace the actual HTTP request: an authenticated employee or agent sends prompt content to an LLM endpoint, a policy is evaluated, and the response returns to the calling application.
I would reject any evaluation that ends with a green box beside the word "logging." A security review packet often contains a 15-inch laptop screenshot, a role list, and a retention setting, yet none of those prove which policy allowed a particular prompt on August 31, 2026. The NIST AI RMF puts governance into operational practice, where evidence has to survive a later review.
The practical test starts with a trace, not a dashboard tour. Take one request from the calling application, follow it through the configured control point, and identify the record produced at the decision moment. The reviewer should be able to connect the authenticated actor to the request content and then connect that request to the response returned by the model. A provider administration event can support the trace, but it cannot stand in for the request record when the two systems capture different fields.
This test also exposes an ownership question. If the application creates the only audit entry, the application team controls the evidence path. A security team may still trust that system, but the review should state who can change its logging configuration and how an auditor receives an unaltered record. A separate enforcement layer gives the reviewer another place to inspect the decision and the policy state.
Evidence at the request boundary
Obsidian Security's published documentation should anchor the vendor-specific part of the review. Ask the vendor to show the record created for one prompt, one response, and one denied request. Then inspect the identity field, the policy version, the timestamp, and the system that owns the write path. Administrative events and platform telemetry answer part of that question. Per-decision evidence answers the audit question.
The record should also make the request path legible. Start with the caller supplied by the application, the model route, and the policy state used at evaluation. Add the decision outcome and the time at which the control took effect. A reviewer can then sample a permitted request and a denied request without reconstructing the event from several administration screens. The policy decision point concept is useful here because it names the place where authorization logic produces an enforceable result.
The write path deserves its own question in the review. Who receives the record when a request is denied? Does the record exist before the calling application receives an error? Can the security team retrieve the policy version that produced the outcome on August 31, 2026? These questions distinguish an event about system administration from evidence about a specific decision. They also show where retention and access controls need to be tested.
The control point matters because the prompt travels in HTTP traffic between the application and the model. An application log can describe its own state, while an independent proxy can evaluate the request before it reaches the LLM and write the decision record before the response returns. Those are distinct mechanisms with different review outcomes.
That distinction matters when a workload calls several model providers. The application may route prompts according to its own configuration, while a request-layer control sees the HTTP call that actually leaves the service. A useful review follows a call to the documented provider endpoint and checks whether the same identity and policy context remain attached after routing. The AI gateway architecture pattern gives the reviewer a concrete request path to examine.
Alternatives by architectural fit
Choose Obsidian Security when its documented scope matches the controls already deployed around the workload. Choose a request-layer enforcement product when the requirement is identity-bound policy for every routed model call, coupled with evidence outside the application write path. Keep model-provider settings in place as defense in depth; they address provider-specific configuration and safety behavior.
The fit question should follow the traffic. An administration-centered product can serve a team that needs visibility into its provider configuration and policy records. A request-layer product serves a team that must decide whether a live call reaches the model and retain proof of that decision. The two controls can sit in the same review packet when each one has a defined responsibility.
The useful comparison is narrow: it asks which product sees the prompt, which identity it receives, which policy it evaluates, and which record an auditor can retrieve. That keeps the decision grounded in architecture instead of vendor theater.
For the August 31, 2026 review, write the answers beside the request trace. Note the application route, the identity context, the policy result, and the location of the retained record. If a product cannot show the path with a real sample, mark that part of the evaluation as unresolved. A polished feature page cannot settle a question that depends on where the request is intercepted and who owns the evidence.
DeepInspect
DeepInspect is a stateless proxy between authenticated users or agents and HTTP LLM endpoints. It evaluates the identity context supplied by the application, prompt-level classification, route, and policy before the request reaches the model. Each decision produces an independent audit record with the relevant identity, policy, outcome, and timestamp.
That architecture gives the review a defined control point for the live call. The application supplies the identity context, the proxy evaluates the request, and the resulting record preserves the policy outcome for later inspection. The scope stays specific: HTTP AI traffic between an authenticated caller and an LLM endpoint. Provider administration records still have a role, but they answer a different question about the provider's own control surface.
That model complements platform controls while supplying the request-level evidence a regulated deployment needs. Book a technical deep dive at deepinspect.ai.