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Runtime security for AI agents is decided by whether protection lives in the path or in your codebase. 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 enforces inline as a first-party layer that sits on the traffic, or whether it ships as an API your engineers have to wire into every screening point themselves. That choice separates protection you turn on from protection you have to build.
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. Lakera, now acquired by Check Point and folded into its Infinity platform as Check Point AI Guardrails, takes a different shape. Its runtime protection is a guardrail delivered through the Guard API, a detection service you call from your own application, with native platform runtime integrations still on its roadmap. 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 payload you send off to be scored.
TL;DR
- NeuralTrust enforces first-party and inline in its own data path. TrustGuard blocks in-flight before the action executes. Lakera's runtime protection is the Guard API, a detection service your engineers must call from every screening point, with native runtime integrations still on the roadmap.
- NeuralTrust ships a native AI gateway with enforcement inline. Lakera has no gateway. It is a guardrail control layer wrapped around your models.
- NeuralTrust protects every agent surface with one policy model. No API to wire into each application. Lakera's runtime coverage exists only where you have embedded the Guard API yourself.
- NeuralTrust authenticates the agent to the tool at runtime and governs every deployment from one federated control plane. Lakera screens for denied tools and off-task use through its API, but does not authenticate the agent's identity to the tool.
NeuralTrust vs Lakera: AI Runtime Security at a Glance
| Capability | NeuralTrust | Lakera |
|---|---|---|
| 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. Lakera: 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 hand you an API to wire in and leave the rest to your engineers. 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 Lakera?
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Lakera is a guardrail company from Zurich, known for Lakera Guard and the Gandalf prompt-injection game. Check Point acquired it in late 2025 and folded it into the Infinity platform as Check Point AI Guardrails, where it now serves as the foundation of Check Point's center for AI security. Its runtime work sits inside that much larger stack as one piece among many.
Lakera Guard is a runtime guardrail delivered through the Guard API. You send prompts, model outputs, and tool calls to the API, running as a hosted service or a self-hosted container, and it returns a decision your application then acts on. Its agent offering pairs posture features, agent discovery and risk assessment managed in a portal, with that same Guard API for runtime screening. By Lakera's own documentation, runtime protection integrates through the Guard API and native platform runtime integrations are still on the roadmap. In other words, the runtime layer is a detection service you embed, not an enforcement layer that sits in the path on its own.
Secures Every AI Agent: One Policy Model vs an API You Wire In Per App
Agents run behind gateways, inside SDKs, in browsers, and on agent platforms, and new ones arrive constantly. Runtime security that only reaches the places you have wired up by hand 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.
Lakera's runtime protection exists only where you have embedded the Guard API yourself. Every place you want screened is a place your engineers call the API and handle the response, and Lakera's own documentation states that native platform runtime integrations are still on the roadmap. Agent coverage on top of that comes through connecting specific platforms for discovery, one connector at a time. That is protection assembled application by application and platform by platform, where TrustGuard extends across all of them from a single model.
Native AI Gateway Integration: Enforcement Inline vs a Guardrail API
Where runtime enforcement runs decides what it can stop. On the live traffic path, it blocks an action before it executes. As an API called from application code, it protects only what that code remembers to send.
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 service the traffic has to be routed to by hand.
Lakera has no gateway. It is a guardrail control layer wrapped around your models and agents, reached through the Guard API. There is no first-party AI gateway underneath it carrying the traffic with enforcement inline on the same path, and no routing, failover, or tool governance living where the enforcement decision is made. Protection depends entirely on your application calling the API at the right moment, every time. TrustGuard removes that dependency by owning the gateway and the enforcement in one product.
First-Party Inline Enforcement: In-Path vs an API on the Roadmap
The reliability of enforcement depends on where it lives. If protection only happens when your code remembers to invoke it, every missed call is an opening.
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. It sits on the traffic by default, not on the assumption that every application will call a service before every risky action.
Lakera's enforcement is the Guard API. Screening happens when your application sends the interaction to the API and acts on the verdict, and by Lakera's own documentation the native platform runtime integrations that would put protection in the path are still on the roadmap. Until then, coverage is only as complete as your engineering team's discipline in wiring the API into every input, every output, every tool call, and every tool response, across every application. A guardrail you have to remember to call is not the same as an enforcement layer that is always there. TrustGuard is the layer that is always there.
Agent-to-Tool Authentication: Identity at Runtime vs Deny-List Policy
Least-privilege for agents starts with a question a content check cannot answer: is this really the agent it claims to be, and is it entitled to use this tool right now? Screening 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.
Lakera screens tool behavior, but it does not authenticate the agent to the tool. Its agent behavior defense flags off-task tool use and calls to denied tools through the Guard API, which is deny-list policy applied to a call, not verification of the identity behind it. Blocking a call to a tool on a deny list does not establish who the agent is when it reaches for the tools it is allowed to use. TrustGuard closes that gap by making authentication part of the runtime decision, not a policy check bolted onto a screening API.
Federated Control Plane Across Regions: One Place vs Scattered Enforcement
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 scattered across every application and install.
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.
Lakera cannot offer the same over its runtime layer. Because runtime protection lives inside each application as Guard API calls, and native platform runtime integrations are still on the roadmap, enforcement is spread across every codebase and every self-hosted container that embeds it. There is no single control plane sitting over the runtime enforcement in every environment the way TrustGuard provides. Policy is managed in a portal, but the enforcement itself is scattered across the apps that call the API. For an enterprise running agents across many environments, that is exactly the fragmentation 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 guardrail your engineers have to call from their own code.
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 one line item in a much larger stack.
Lakera is a guardrail delivered through an API, now absorbed into Check Point's Infinity platform, where agent runtime security competes for attention against a sprawling security portfolio. Its runtime protection has no gateway of its own, it lives inside your applications as Guard API calls with native runtime integrations still on the roadmap, it screens tool behavior through deny lists rather than authenticating the agent to the tool, and its enforcement is scattered across every codebase that embeds it.
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 API you have to build into every application yourself. 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. Lakera
1. What is the main difference between NeuralTrust and Lakera?
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. Lakera is a guardrail delivered through the Guard API, a detection service your engineers call from their own application code, now part of Check Point's Infinity platform. One owns the enforcement path, the other hands you an API to wire in.
2. Does Lakera have its own AI gateway?
No. Lakera is a guardrail control layer reached through the Guard API, running as a hosted service or a self-hosted container. NeuralTrust ships TrustGate, its own AI gateway, and TrustGuard enforces inline on that traffic path, so enforcement lives in the path rather than depending on your application to call an API.
3. What does first-party inline enforcement mean and why does it matter?
It means protection sits on the traffic by default rather than running only when your code invokes it. TrustGuard makes its block and transform decisions in-flight on NeuralTrust's own collectors and gateway. Lakera's runtime protection is the Guard API, and by Lakera's own documentation the native platform runtime integrations that would put it in the path are still on the roadmap, so coverage depends on your team wiring the API into every screening point.
4. Does Lakera authenticate agents to tools the way NeuralTrust does?
No. Lakera screens tool behavior, flagging off-task tool use and calls to denied tools through its API, which is deny-list policy applied to a call. NeuralTrust authenticates the agent to the tool at runtime, tying the tool call to the agent's identity and enforcing authentication and authorization together, so an agent reaches only the tools it is entitled to as the identity it actually is.
5. Can both products be deployed privately and in the cloud?
Yes. Both NeuralTrust and Lakera 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 Lakera's runtime protection lives inside your applications as Guard API calls with enforcement scattered across every codebase that embeds it.
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.
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