DeepInspect vs Protect AI: Comparing Runtime LLM Enforcement and Model-Supply-Chain Scanning

DeepInspect and Protect AI show up together on shortlists when an enterprise is building a full AI security program. The two products solve adjacent problems with different surface coverage. This piece is a direct comparison on the axes I see buyers care about, with notes on the surfaces where the products overlap and the surfaces where each one is the natural answer.

TL;DR

DeepInspect is an identity-aware HTTP-proxy that enforces policy on runtime LLM traffic and produces a per-decision audit record series. Protect AI ships Guardian (model-supply-chain scanning) and Layer (runtime LLM monitoring) across a broader MLOps surface. Pick DeepInspect if the program centers on runtime enforcement, identity-bound records, and multi-provider LLM coverage. Pick Protect AI if the program needs model-supply-chain scanning as a primary control. Many programs run both.

Where DeepInspect sits

DeepInspect sits inline on the HTTP path between authenticated users or agents and any LLM. The proxy terminates TLS at the inspection layer, authenticates the caller against the corporate IdP, runs deterministic classification against the prompt content, evaluates policy against identity and classification, and commits a per-decision audit record before the model receives the request. The records carry timestamp, identity, classification, policy version, decision, and an integrity signature on a tamper-evident series.

The placement gives DeepInspect identity binding at the request boundary, multi-model coverage by default, and a canonical record series the auditor samples against. The product does not scan model artifacts before they enter production. That surface is upstream of the HTTP proxy.

Protect AI: where it sits

Protect AI ships two named products. Guardian inspects model artifacts in the MLOps pipeline, catching Pickle deserialization payloads, malicious LoRA adapters, license violations, and vulnerable dependencies in the model's runtime stack. Layer monitors runtime LLM traffic with content classification and policy enforcement, available as an SDK or as an HTTP proxy.

The two surfaces address different lifecycle stages of AI risk. Guardian catches what would have happened if the artifact ran. Layer catches what is happening as the production traffic runs. The platform's broader MLOps surface includes model registry integrations, runtime telemetry, and policy management across both surfaces.

Feature comparison on the runtime surface

| Axis | DeepInspect | Protect AI Layer | |---|---|---| | Primary placement | HTTP proxy at the request boundary | SDK in application or HTTP proxy | | IdP integration | Built in at the proxy | Application or proxy-side integration | | Identity binding on every record | Yes | When the integration carries it through | | Classification | Deterministic categories (PII, PHI, source code, customer, custom) | Content classification plus adversarial-pattern coverage | | Multi-provider coverage | Yes (OpenAI, Anthropic, Google, Bedrock, Azure, self-hosted) | Yes (model-agnostic) | | Tamper-evident record series | Yes (signed) | Available as platform feature | | Article 19 natural-person field | Yes by default | Yes when wired through | | Latency overhead | Under 50 ms in internal testing | Comparable on proxy path | | Model-supply-chain coverage | No | Yes via Guardian | | MLOps integrations | Identity-first surface | Broader MLOps platform surface |

Surface comparison on the artifact side

Protect AI Guardian covers the artifact side. DeepInspect does not. The alternatives to Guardian on the artifact surface include HiddenLayer's Model Scanner and open-source projects like ModelScan and Picklescan. Programs running self-hosted open-weight models on Hugging Face artifacts typically pick an artifact scanner regardless of which runtime enforcement product they pick on the HTTP side. Programs running entirely against managed LLM APIs (OpenAI, Anthropic, Bedrock) inherit the artifact-side control from the provider and may de-prioritize Guardian.

Pick Protect AI if

• The program needs model-supply-chain scanning as a primary control, especially for self-hosted open-weight models.
• The MLOps platform surface (model registry, lineage, runtime telemetry) is consolidating on a single vendor and Protect AI's coverage matches.
• The runtime LLM control is part of a broader MLOps program rather than a standalone AI security boundary.

Pick DeepInspect if

• The program centers on identity-bound per-request records under EU AI Act Article 12 or HIPAA Security Rule audit obligations.
• The deployment spans multiple LLM providers on the same policy surface.
• The corporate IdP integration belongs at the inspection boundary rather than inside each application team's code.
• The risk model centers on prompt content classification and policy enforcement against natural-person identity.

Run both when

• The program runs self-hosted open-weight models AND has Article 12 / HIPAA audit obligations on production traffic.
• The MLOps team owns the artifact pipeline and the security team owns the runtime boundary, and the two surfaces report through different functions.
• The audit program needs both the upstream supply-chain control and the runtime enforcement record on every decision.

In a combined deployment, Protect AI Guardian inspects artifacts at the registry boundary and DeepInspect enforces policy at the runtime HTTP boundary. The two records flow into the same audit program through different evidence pipelines.

Regulatory framing under EU AI Act and HIPAA

Article 12 requires automatic recording of events sufficient to ensure traceability. Article 19 specifies records identify natural persons involved. Article 99 sets penalties at €15 million or 3% of global annual turnover for high-risk non-compliance. The August 2, 2026 deadline applies to high-risk AI systems including credit scoring, employment screening, education access, and biometric identification.

HIPAA Security Rule 45 CFR 164.312(b) expects audit controls on systems that process PHI. The runtime record series is the evidence both regulations sample against. The model-supply-chain record series supplements that evidence on the artifact side but does not replace it on the runtime side.

Pricing approach

Both vendors quote against the specific deployment after scoping. DeepInspect prices per protected endpoint and request volume tier. Protect AI prices across Guardian and Layer modules with platform tiers. Public price lists are not available for either product, which is standard at this stage of the market.

DeepInspect

DeepInspect is the runtime HTTP-proxy enforcement gateway shape of this comparison. The proxy authenticates the caller against the corporate IdP, classifies the prompt content, evaluates policy against identity and classification, and commits a per-decision audit record before the response returns to the application. The records carry the fields EU AI Act Article 12 and Article 19 expect on the series HIPAA Security Rule references.

For programs choosing between DeepInspect and Protect AI, the question reduces to which surface needs the primary investment. If the program needs identity-bound runtime records across multiple LLM providers, DeepInspect is the natural runtime answer and Protect AI Guardian sits upstream on the artifact side. If the program needs the broader MLOps platform and the runtime control is one piece of that, Protect AI's Layer covers the runtime surface.

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