What does HiddenLayer cover that DeepInspect does not?

HiddenLayer scans model files for tampering and supply-chain attacks, monitors model behavior for adversarial inputs, and provides MLDR for runtime drift detection. DeepInspect operates at the HTTP request layer and does not inspect model artifacts. The two cover different layers of the AI security stack and routinely run together in regulated environments.

Can HiddenLayer and DeepInspect run together?

Yes. HiddenLayer continues to monitor model integrity and runtime adversarial behavior. DeepInspect handles the HTTP request layer, identity-aware policy, and the per-decision audit record. The defense-in-depth pattern covers both the model and the request boundary. Enterprises running both end up with a coherent security posture across the layers regulators inspect.

Does HiddenLayer satisfy EU AI Act Article 12?

Article 12 requires automatic recording of AI events over the system lifetime with identification of the natural persons involved and detail sufficient to reconstruct the decision. Model-layer monitoring captures inference inputs and outputs but rarely captures the natural-person identity that initiated the request, the policy state at the moment of decision, or the data classification applied. The Article 12 record needs HTTP-layer evidence that the model layer cannot produce alone.

What about agentic AI workflows?

Agentic workflows chain multiple LLM calls per user-initiated action. The action lineage required by NIST Pillar 3 lives across the chain of HTTP calls. Model-layer monitoring sees inference events in isolation. An HTTP proxy that sees the full chain and records the originating user identity produces the connected lineage record regulator

HiddenLayer Alternatives: 2026 Buyer Evaluation

HiddenLayer concentrates on the model layer. The platform inspects model files for tampering, detects adversarial attacks against deployed models, and offers MLDR for runtime model monitoring. The procurement question shifts when the requirement set extends to HTTP enforcement on inference traffic, identity-bound audit records, or compliance posture for the EU AI Act Article 12 logging mandate. The model layer is one part of the AI security stack. The HTTP request layer is another.

I want to walk through six HiddenLayer alternatives, what each one architecturally is, and which one fits which deployment profile.

TL;DR

HiddenLayer focuses on model integrity and runtime model monitoring. Alternatives extend coverage to HTTP-layer enforcement, identity-aware policy, and per-decision audit records that satisfy compliance regimes the model layer alone does not address.

Alternative 1: DeepInspect

A stateless HTTP proxy at the AI request boundary. Reads identity headers per request, evaluates per-route and per-role policy, classifies prompt content, and writes tamper-evident per-decision audit records. Covers every LLM endpoint regardless of provider.

Best fit when the regulatory exposure includes EU AI Act Article 12, HIPAA, GDPR, or NIST AI RMF.

Alternative 2: Protect AI

The broader Protect AI platform covers ML supply chain security, model scanning, LLM Guard for prompt-level checks, and policy enforcement across the AI lifecycle.

Best fit when the team's procurement needs cover model scanning plus prompt-level scanners and the buyer wants an integrated suite.

Alternative 3: Mindgard

A UK-based AI security platform focused on adversarial testing and runtime monitoring for generative AI. Strong at red-teaming LLMs before deployment and detecting attack patterns in production.

Best fit when the dominant requirement is pre-deployment adversarial testing and runtime monitoring rather than per-request HTTP policy.

Alternative 4: Lakera Guard

Commercial offering from Lakera (Check Point). Strong adversarial dataset coverage for prompt injection. SDK or network-side options.

Best fit when prompt-injection coverage at the LLM layer is the primary procurement driver.

Alternative 5: Aporia

AI observability platform with policy enforcement layered on monitoring and drift detection. HTTP or SDK execution.

Best fit when observability and drift detection are the primary needs and the team accepts lighter identity-aware enforcement.

Alternative 6: Cisco AI Defense

Cisco's AI security platform with runtime guardrails for genAI applications. Integrated with Cisco's broader security stack.

Best fit when the team already runs Cisco security tools and wants AI-specific capability inside the same operational platform.

Feature comparison

| Property | HiddenLayer | DeepInspect | Protect AI | Mindgard | Lakera | Aporia | Cisco AI Defense | |---|---|---|---|---|---|---|---| | Layer | Model + runtime | HTTP proxy | ML lifecycle | Pre-deployment + runtime | SDK or HTTP | HTTP or SDK | HTTP proxy | | HTTP request enforcement | No | Yes | Partial (LLM Guard) | No | Yes | Partial | Yes | | Identity-aware per-request | No | Required | Partial | No | Configurable | Configurable | Partial | | Per-decision audit record | No | Yes | No | No | Partial | Partial | Partial | | EU AI Act Article 12 fit | No | Yes | Partial | No | Partial | Partial | Partial | | NIST AI RMF Pillars 1-3 | No | Yes | Partial | No | Partial | Partial | Partial | | Model integrity scanning | Yes | No | Yes | No | No | No | Partial | | Adversarial testing | Yes | No | Yes | Yes | Yes | No | Partial | | Cross-provider HTTP scope | No | Yes | Partial | No | Configurable | Configurable | Yes |

Pick DeepInspect if

The regulatory exposure requires per-decision audit records that identify the natural person behind every AI decision (EU AI Act Article 12, NIST AI RMF Pillars 1-3, HIPAA). The AI traffic spans multiple providers and the policy needs to apply uniformly at the HTTP layer.

Pick Protect AI if

The procurement covers model scanning, in-process scanners (LLM Guard), and the ML lifecycle as an integrated suite, and the in-process audit model fits the regulatory profile.

Pick Mindgard if

Pre-deployment adversarial testing and runtime drift detection are the primary drivers. The buyer treats the LLM as a model deployment problem more than an HTTP enforcement problem.

Pick Lakera, Aporia, or Cisco AI Defense if

The deployment context favors a specific vendor's adjacent product fit. Lakera for adversarial coverage. Aporia for observability. Cisco AI Defense for existing Cisco shops.

DeepInspect

HiddenLayer's model-layer focus is valuable when the threat model treats the model as the perimeter. The reality of regulated AI deployment is that the inference layer is one part of the perimeter and the HTTP request layer is the other. Regulators ask who initiated a specific request, what policy was in effect, what data classification applied, and what the decision was. Those questions cannot be answered from model-layer monitoring alone.

DeepInspect was built to answer those questions. The HTTP proxy captures identity context, applies policy uniformly across every LLM endpoint, and writes the per-decision audit record independent of the application. HiddenLayer can continue to monitor model integrity. The HTTP enforcement and the audit trail sit at the proxy layer where regulators look first.

If you are facing the August 2 EU AI Act deadline and your AI security stack is built around model-layer tooling, the HTTP enforcement and audit-record gap is what costs the deployment its compliance posture. Book a demo today.