DeepInspect vs Giskard: Open-Source LLM Testing and Runtime Enforcement

Giskard generates the attacks for you. Point it at a model or a RAG application and it produces adversarial test cases automatically, probing for hallucinations, contradictions, prompt injection, sensitive-data disclosure, and inappropriate content. It is Apache 2.0 licensed, covers traditional ML quality alongside LLM behavior, and adds a Hub where domain experts and engineers review findings together. The design centers on one activity: finding out how a model breaks before users do.

That coverage of both LLM security testing and classical ML quality gives it reach across a data-science team's whole model portfolio, not only the language models. A finding flows into a fix pipeline, which is what makes it more than a scanner in practice.

Teams comparing Giskard to DeepInspect are usually holding a testing framework next to a production control point. These sit at opposite ends of the deployment timeline. I want to make that explicit, because it is the whole distinction.

TL;DR

Giskard is an open-source framework that auto-generates adversarial tests to surface vulnerabilities in models and RAG applications before deployment, with a Hub for collaborative review. DeepInspect sits inline in production between authenticated callers and model providers, decides whether each request proceeds based on identity and data, and commits a per-decision audit record. One finds weaknesses before you ship. The other governs traffic after you ship.

Giskard: automated vulnerability testing

Giskard's model is the generated test. Rather than asking an engineer to hand-write adversarial prompts, it scans a model and produces cases targeting known failure modes, then reports where the model gave up sensitive output, hallucinated, or followed an injected instruction. Because it also handles traditional ML testing for bias, performance, and data leakage, one framework covers a team's tabular models and its language models together.

The Hub is the collaboration half. Findings land in a shared workspace where a compliance reviewer or a subject-matter expert can annotate them, and the results feed a pipeline that turns a discovered weakness into a task or a proposed guardrail. For a team hardening a model before launch, that loop of scan, review, fix is the daily work.

Giskard's position on the timeline is fixed at pre-deployment. It evaluates a model or application in a test setting and reports what it found, with no authority over live traffic. That is correct for a testing framework and it is the boundary every scanner shares.

Finding weaknesses versus governing traffic

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint in production. Every live call traverses it, and the decision happens before the provider sees the request.

Authority and timing separate them. Giskard tells you a model is susceptible to a class of attack so you can harden it before launch. DeepInspect governs the shipped system and decides, per request, whether this identity may proceed with this data. A clean Giskard report describes how the model behaved against generated tests in staging. It has no bearing on the analyst who this afternoon routes regulated data through a live call no test anticipated. That runtime case belongs to an enforcement layer, and the case for inline enforcement is in why AI security must be inline.

Identity is the second gap. A Giskard test carries the scenario it generated. It models no concept of the production caller, their role, or their entitlements. DeepInspect treats that identity as the primary key of every decision and records it.

Feature comparison

| Capability | Giskard | DeepInspect | |---|---|---| | Open source | Yes | No | | Auto-generated adversarial tests | Yes, core | No | | LLM and classical ML testing | Yes, core | No | | Collaborative review Hub | Yes | No | | Findings-to-fix pipeline | Yes | No | | Runs in production on live traffic | No | Core | | Inline blocking of a request | No | Yes | | Identity-bound per-request policy | No | Core | | Data classification against custom classes | No | Yes | | Application-independent audit record | No | Core | | Primary buyer | Data science and ML quality | Security and compliance |

The overlap is narrow because the two live at different lifecycle stages. Giskard decides whether a model is hardened enough to ship. DeepInspect governs what the shipped system is allowed to do, and produces the evidence.

Where Giskard is the better answer

If the question is whether a model leaks sensitive data under adversarial pressure, or whether a RAG pipeline hallucinates on edge cases, Giskard answers it and DeepInspect does not. Automated vulnerability generation, ML quality testing, and a review workflow for domain experts are its territory, and a policy proxy has nothing to contribute to pre-launch hardening.

Being open source and covering both ML and LLM testing makes Giskard a strong addition to a data-science team's stack. Pre-deployment testing is a genuine requirement, and Giskard serves it well.

Where the gap opens

Two requirements move a team past what a testing framework provides.

The first is control over live traffic. Production sees callers and prompts no generated test modeled. A passing test report cannot refuse a request. An inline policy layer can, keyed to the caller's identity and the data in the prompt. The class of runtime risk a test cannot cover is the post-authentication gap.

The second is audit evidence. A test report is a pre-launch artifact. A regulator under the EU AI Act asks for a per-decision record of what was authorized in production, on storage the application cannot rewrite. The independence argument is developed in signed audit logs for AI requests.

Pick Giskard if

• Your team is hardening models and RAG applications before deployment.
• You want automated adversarial test generation rather than hand-written cases.
• You need one framework for both LLM and traditional ML testing.
• Open source and collaborative review are requirements rather than preferences.
• Nobody is asking you for per-decision production evidence.

Pick DeepInspect if

• You need live model calls refused, not vulnerabilities reported.
• An auditor or enterprise customer wants per-decision records tied to an identity.
• Policy must differ by role and be enforced on production traffic.
• You need an audit record on storage the application cannot alter.
• You operate under the EU AI Act, DORA, HIPAA, or a comparable mandate.

DeepInspect

This is exactly what DeepInspect does. DeepInspect sits inline between your users or agents and the LLM APIs they call. For every request and response, it evaluates identity, data classification, model authorization, and organizational policy, then makes a pass or block decision before the traffic reaches the model.

The record is the second half of the product. Every decision writes a structured entry containing the authenticated identity, the resolved role, the data classes found in the prompt, the policy version in force, and the outcome. That entry commits before the response returns, on storage the application cannot write to, which is what lets it serve as evidence rather than as a test result. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. A testing framework and an enforcement layer belong in the same program, at different points in the lifecycle.

If you are facing the August deadline, let's talk.