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Runtime security for AI agents is decided by where enforcement lives. Agents do not just answer, they act. They call tools, trigger workflows, and take real actions, and the attacks that matter arrive inside the action itself. The question is whether your security sits inline in the execution path and enforces as a first-party layer, or whether it watches from the outside and responds after the signal reaches it. That single architectural choice separates prevention from cleanup.
TrustGuard is NeuralTrust's AI runtime security, and it is the first runtime security built to extend across every AI agent, without a custom integration rebuilt for each tool or framework. HiddenLayer comes from a different place. It grew out of model scanning into a non-invasive AI detection and response tool, and its agent protection rides on components it does not own: a third-party proxy, SDK instrumentation, or a gateway you provide. This comparison covers both on the terms that decide AI runtime security: whose data path enforces, how far protection reaches, and whether the agent is treated as an identity and an action rather than a signal to observe.
TL;DR
- NeuralTrust enforces first-party and inline in its own data path. TrustGuard blocks in-flight before the action executes. HiddenLayer is a non-invasive detection and response tool whose enforcement rides on a third-party proxy, SDK hooks, or a gateway you already run.
- NeuralTrust ships a native AI gateway with enforcement inline. HiddenLayer has no gateway of its own and inspects one you provide.
- NeuralTrust protects every agent surface with one policy model. No custom integration per framework. HiddenLayer instruments per framework through LiteLLM proxy interception and SDK hooks.
- NeuralTrust authenticates the agent to the tool at runtime and governs every deployment from one federated control plane. HiddenLayer inspects tool use and reconstructs sessions for investigation, but does not authenticate the agent's identity to the tool.
NeuralTrust vs HiddenLayer: AI Runtime Security at a Glance
| Capability | NeuralTrust | HiddenLayer |
|---|---|---|
| Flexible deployment (private, cloud) | ✅ | ✅ |
| Secures every AI agent | ✅ | ❌ |
| Native AI gateway integrated | ✅ | ❌ |
| First-party inline enforcement | ✅ | ❌ |
| Agent-to-tool authentication | ✅ | ❌ |
| Federated control plane across regions | ✅ | ❌ |
NeuralTrust vs. HiddenLayer: Platform Overview
What is NeuralTrust TrustGuard?
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TrustGuard is NeuralTrust's AI runtime security. It inspects every interaction and stops attacks at the moment of execution, on every surface where your agents run: gateways, SDKs, browsers, and platforms. It was built for that job, and it enforces the way agents actually behave.
The design rests on one policy model that covers every threat class at once: injections, sensitive data, unsafe actions, and behavioral attacks. Collectors pull agent traffic from gateways, SDKs, browsers, sidecars, and log streams, protocol-typed policies run detection on every request and response, and the decision is enforced in-flight. Allow, block, or transform, before the action reaches your system. TrustGuard reads the session, the identity, and the protocol together, so it catches the indirect prompt injection hidden in a poisoned tool result, the agent reaching for a tool it should never touch, the automated loop burning through tool calls, and the exfiltration buried in a tool input, and it acts before any of it executes.
Because enforcement is first-party and inline, TrustGuard does not depend on someone else's proxy to sit in the path. It integrates natively with TrustGate, NeuralTrust's own AI gateway, and it extends across SDKs, browsers, and platforms with the same policy model. That is what makes it the first runtime security built to reach every AI agent without a new integration for each one, and it is why one control plane can govern every deployment across every region.
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What is HiddenLayer?
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HiddenLayer is an AI security platform that grew out of model scanning. Its platform is organized around discovery, supply chain scanning of model artifacts, attack simulation, and a runtime module marketed as non-invasive AI detection and response. The center of gravity is detection, investigation, and response for the SOC, not inline enforcement in production traffic.
Its agent protection reflects that origin. HiddenLayer does not run its own AI gateway. It gains runtime visibility through LiteLLM proxy interception, SDK instrumentation, and inspection of a gateway you already operate. It detects prompt injection, inspects tool use, watches memory and context, and reconstructs agent activity across sessions so analysts can investigate after the fact. It is an observation and response layer bolted onto whatever infrastructure you bring, positioned as non-invasive precisely because it sits outside the action rather than owning the path the action travels on.
Secures Every AI Agent: One Policy Model vs Per-Framework Instrumentation
Agents run behind gateways, inside SDKs, in browsers, and on agent platforms, and new ones arrive constantly. Runtime security that only reaches the surfaces you have wired up leaves everything else uncovered.
TrustGuard reaches every surface with a single policy model. The same rules on injections, sensitive data, unsafe actions, and behavioral attacks apply whether the agent calls a tool through a gateway, an SDK, or a browser. This is the core of NeuralTrust's position. It is the first runtime security built to extend across every AI agent, without a custom integration rebuilt for each tool. You write policy once and it protects every agent, including the ones your teams have not deployed yet.
HiddenLayer works surface by surface. Its agent coverage is delivered through LiteLLM proxy interception, SDK instrumentation, and per-framework support, so protection exists only where you have stood up one of those integrations. Add a new framework or a new agent platform and you are back to instrumenting it before HiddenLayer can see it, let alone act on it. That is coverage assembled one integration at a time, where TrustGuard extends across all of them from a single model.
Native AI Gateway Integration: Enforcement Inline vs No Gateway of Its Own
Where runtime enforcement runs decides what it can stop. On the live traffic path, it blocks an action before it executes. Beside the path, it is always reacting to something that already happened.
TrustGuard integrates natively with TrustGate, NeuralTrust's own AI gateway, so runtime enforcement runs inline on the actual path that LLM and MCP traffic already travels. The decision to block, transform, or allow a tool call is made in-flight, on infrastructure NeuralTrust controls end to end. Enforcement is part of the road, not a camera pointed at it.
HiddenLayer has no gateway of its own. By its own description, its runtime protection depends on inspecting a gateway you provide, intercepting through the LiteLLM proxy, or instrumenting your SDK. There is no first-party AI gateway underneath it carrying the traffic with enforcement inline on the same path. That leaves its runtime protection tied to whatever routing layer you already run and to a third-party proxy it does not maintain. TrustGuard removes that dependency by owning the gateway and the enforcement in one product.
First-Party Inline Enforcement: Your Own Data Path vs Riding on Third-Party Interception
The strength of an enforcement decision depends on who owns the point where it is made. If that point belongs to a component you do not control, your protection inherits its limits.
TrustGuard enforces on its own first-party data path. The collectors, the policy engine, and the enforcement point are all NeuralTrust's, so a block or a transform happens in-flight inside infrastructure built for exactly that. There is no external proxy to route through and no third-party hook to depend on for the decision to land.
HiddenLayer's runtime enforcement rides on interception points it does not own. Its agent protection is delivered through the LiteLLM proxy, SDK instrumentation, and inspection of your gateway, and the product is marketed as non-invasive by design, sitting outside the model and the data rather than owning the path. That makes its enforcement only as capable and as reliable as the third-party proxy and the instrumentation it borrows. When the enforcement point belongs to someone else, so do its blind spots. TrustGuard keeps the enforcement point first-party so the decision is made where NeuralTrust can guarantee it.
Agent-to-Tool Authentication: Identity at Runtime vs Tool-Use Inspection
Least-privilege for agents starts with a question that inspection alone cannot answer: is this really the agent it claims to be, and is it entitled to use this tool right now? Watching a tool call is not the same as verifying who is making it.
TrustGuard controls how an agent authenticates to MCP tools at runtime. It ties the tool call to the agent's identity and enforces authentication and authorization together at the moment of execution, so an agent reaches only the tools it is entitled to, as the identity it actually is. Identity and permission are verified inline, in the same step as the action.
HiddenLayer inspects tool use, but it does not authenticate the agent to the tool. It monitors agent actions across APIs, MCP tools, and other operations and applies runtime policy, and it reconstructs those actions across sessions for investigation. That is observation and policy over the tool call, not verification of the identity behind it. Inspection can flag a suspicious action, but it does not establish who the agent is when it reaches for a tool. TrustGuard closes that gap by making authentication part of the runtime decision, not a report generated after the call.
Federated Control Plane Across Regions: One Place vs Isolated Instances
Enterprises run agents across many environments at once. Different regions, different clouds, on-premises, and air-gapped networks. The operational question is whether all of that runtime security is governed from one place or managed instance by instance.
TrustGuard is governed by a single federated control plane. One control plane reaches every TrustGuard deployment across regions and environments, so policy, visibility, and enforcement stay consistent everywhere your agents run. You set a rule once and it holds in every region, and you see what happened across all of them without stitching consoles together. That is how you run agent runtime security at enterprise scale instead of maintaining a fleet of separate installs.
HiddenLayer cannot offer the same across its most sensitive footprint. Its deployments span SaaS, on-premises, and air-gapped environments, and an air-gapped instance runs disconnected by definition, isolated from anything outside the customer boundary. Those isolated installs are not governed by a single shared control plane spanning regions. Each one is configured and watched on its own. For an enterprise running agents across many environments, that is a per-deployment burden TrustGuard was built to remove.
Final Verdict
AI runtime security is decided inside the action, and inside the identity behind it. Security that owns the path and enforces first-party behaves differently from a tool that observes from the outside and responds after the fact.
NeuralTrust built TrustGuard as runtime security from the start. It is the first runtime security built to extend across every AI agent, without a custom integration for each one. It enforces first-party and inline in its own data path, it ships a native AI gateway with enforcement on the live traffic path, it authenticates the agent to the tool at runtime, and it governs every deployment from a single federated control plane across every region. NeuralTrust is an independent company focused on AI security, and TrustGuard is the product, not a module added on the way from somewhere else.
HiddenLayer approaches the problem from model scanning and non-invasive detection and response. Its agent protection has no gateway of its own and rides on a third-party proxy, SDK instrumentation, and inspection of infrastructure you provide. It watches tool use and reconstructs sessions for investigation, but it does not authenticate the agent to the tool, its coverage is assembled one framework integration at a time, and its most sensitive instances run isolated with no shared control plane.
Both can run in private and cloud environments, so deployment flexibility is not the deciding factor. The decision is whether you want runtime security that owns the path and enforces inside the action, or a detection and response layer that watches it from outside. If you are building on agents and you want protection that spans every one of them, enforces first-party and inline, and verifies the identity behind every tool call, NeuralTrust is built for exactly that.
Frequently Asked Questions about NeuralTrust vs. HiddenLayer
1. What is the main difference between NeuralTrust and HiddenLayer?
NeuralTrust TrustGuard is first-party AI runtime security that enforces inline in its own data path and extends across every agent surface with one policy model. HiddenLayer is a non-invasive detection and response tool that grew out of model scanning, with no gateway of its own, whose agent protection rides on a third-party proxy, SDK instrumentation, and inspection of infrastructure you provide. One owns the enforcement path, the other observes it from outside.
2. Does HiddenLayer have its own AI gateway?
No. HiddenLayer gains runtime visibility through LiteLLM proxy interception, SDK instrumentation, and inspection of a gateway you already run. NeuralTrust ships TrustGate, its own AI gateway, and TrustGuard enforces inline on that traffic path, so enforcement lives on infrastructure NeuralTrust controls rather than on a third-party proxy.
3. What does first-party inline enforcement mean and why does it matter?
It means the enforcement point belongs to the vendor, not to a borrowed component. TrustGuard makes its block and transform decisions in-flight on NeuralTrust's own collectors and policy engine. HiddenLayer's enforcement rides on the LiteLLM proxy and SDK hooks it does not own, so its protection inherits the limits of those third-party pieces. When the enforcement point belongs to someone else, so do its blind spots.
4. What is agent-to-tool authentication and does HiddenLayer provide it?
Agent-to-tool authentication verifies the identity of the agent when it reaches for a tool and enforces authentication and authorization together at runtime. NeuralTrust does this inline. HiddenLayer inspects tool use and applies runtime policy, and it reconstructs sessions for investigation, but it does not authenticate the agent's identity to the tool, which leaves the question of who is behind the call unanswered.
5. Can both products be deployed privately and in the cloud?
Yes. Both NeuralTrust and HiddenLayer support private and cloud deployment, so deployment flexibility is not the deciding factor. The meaningful differences are architectural: NeuralTrust owns the enforcement path with a native AI gateway, extends across every agent with one policy model, authenticates agents to tools at runtime, and governs every deployment from one federated control plane, while HiddenLayer observes from outside through third-party interception and manages its isolated instances on their own.
About the Author
Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety, contributing to the development of secure and trustworthy AI.
NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for AI Gateways and Guardian Agents, and the KuppingerCole 2025 Leadership Compass for Generative AI Defense. Headquartered in Barcelona with ISO 27001 certification.