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AI security posture management is decided by your vantage point: are you on the call the agent makes, or beside the agent watching for signals of it? An agent's real risk is the call itself, the tool it invoked, the input it sent, the data that came back. A platform that sits on that traffic captures the call firsthand. A platform that sits on the endpoint reconstructs the call from the signals around it, and only for the agents its endpoint coverage reaches. Both can be called observability, but one is the interaction and the other is an account of it assembled from the edges.
TrustLens is NeuralTrust's posture management and observability layer. It identifies every agent in the enterprise and tracks how each one behaves, because it is integrated directly in the interaction path, where every LLM, MCP, and tool call flows through NeuralTrust's own gateway and into TrustLens. Geordie AI takes the opposite architectural stance. It is an endpoint-based platform that positions itself explicitly against the gateway model, correlating signals from code, endpoints, and APIs to observe agents from where they run. Both observe behavior and both are enterprise-ready, so this comparison focuses on the choice that actually separates them for AI-SPM: whether posture is built on the call itself, from a gateway in the path, or on signals correlated around the agent, from the endpoint.
TL;DR
- NeuralTrust is on the call. TrustLens sits in the interaction path on NeuralTrust's own gateway, so it captures the actual LLM, MCP, and tool call firsthand. Geordie is endpoint-based and reconstructs behavior from code, endpoint, and API signals around the agent.
- NeuralTrust is built on a native AI gateway. Geordie has none, by design, and positions itself against the gateway model.
- NeuralTrust's coverage is complete through the gateway. Every call that flows through it is captured. Geordie's coverage is bounded by where its endpoint instrumentation reaches.
- NeuralTrust runs in your private environment or the cloud, and both platforms are enterprise-ready, so that is parity.
NeuralTrust vs Geordie AI: AI-SPM at a Glance
| Capability | NeuralTrust | Geordie AI |
|---|---|---|
| Enterprise readiness | ✅ | ✅ |
| Native AI gateway integration | ✅ | ❌ |
| Flexible deployment (private, cloud) | ✅ | ❌ |
| Directly integrated in the interaction path | ✅ | ❌ |
| Observation from the actual call, not endpoint or API correlation | ✅ | ❌ |
| Coverage of every agent's real tool-call execution | ✅ | ❌ |
NeuralTrust vs. Geordie AI: Platform Overview
What is NeuralTrust TrustLens?
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TrustLens is NeuralTrust's AI posture management and observability layer. It identifies every agent across the enterprise and tracks how each one behaves, building posture from the calls agents actually make.
TrustLens has that vantage point because it is integrated directly in the interaction path. Agent traffic flows through TrustGate, NeuralTrust's own AI gateway and the single point every LLM, MCP, and tool call passes through, and that call feeds TrustLens with trace-level detail on every execution. TrustLens records the actual inputs, outputs, and system calls, maps them to frameworks like OWASP, MITRE, and ISO, and makes the whole history searchable. The observation is the call itself, captured firsthand, for every agent whose traffic flows through the gateway, no matter where that agent happens to run.
And because TrustLens sits on the same platform as NeuralTrust's runtime security enforcement, what it observes can be acted on in the same path, so posture leads to prevention rather than to a report. It runs in your private environment or in the cloud, and it carries the enterprise readiness production security demands.
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What is Geordie AI?
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Geordie AI is an endpoint-based AI agent security and governance platform. It discovers agents by correlating signals from code, endpoints, and APIs, builds an inventory with configuration understanding and behavioral observability, scores risk against frameworks, and offers a remediation engine that intervenes at the agent level. It is vendor-agnostic and aims to cover agents wherever they run.
Its defining architectural choice is to avoid the gateway. Geordie positions its endpoint-based model as simpler than a gateway approach, watching agents from where they run rather than sitting on the traffic they generate. That gives it a way to observe activity close to the endpoint across many agent types, but it also sets the terms of what it can see: its picture of an agent is assembled from the code, endpoint, and API signals it can correlate, and its coverage extends to the agents its endpoint instrumentation reaches. It is a young, well-funded company with the enterprise controls to meet the bar. But by choosing the endpoint over the gateway, it chose to observe the agent from around the call rather than on it.
Native AI Gateway Integration: On the Call vs an Endpoint Model
Geordie makes a deliberate architectural argument: that a gateway is more complex than an endpoint model, so it avoids one. The question for AI-SPM is what that choice costs in what you can see. The gateway is not complexity for its own sake. It is the one place that sits on the actual call.
TrustLens is built on TrustGate, NeuralTrust's own AI gateway, which brokers every LLM, MCP, and tool call. Because the gateway carries the traffic, the call it brokers is the call TrustLens sees, complete and structured, and posture is built from that. The gateway is not an extra layer to observe around, it is the vantage point that makes the observation firsthand.
Geordie has no gateway. Its endpoint-based model observes agents from where they run and correlates signals from code, endpoints, and APIs. Avoiding the gateway does simplify the footprint, but it also means never sitting on the call itself, only on the signals that surround it. An endpoint model watches the agent. A gateway watches the interaction. For posture that has to reflect what an agent actually did, watching the interaction is the point, and that is what a gateway gives you.
Flexible Deployment: Private and Cloud vs Cloud Service
Where a posture platform runs decides which environments it can cover and where your agent data goes. Some organizations need posture and observability running inside their own environment, not only as a service that reaches in.
TrustLens runs in your private environment or in the cloud, so posture and behavior tracking can live inside your perimeter, with agent traffic and its telemetry staying where you need them.
Geordie is delivered as a cloud service backed by its endpoint-based instrumentation. That is quick to stand up, but it operates as an outside service collecting from your endpoints rather than posture deployed and running inside your own environment. For organizations that want their agent observability and posture running privately, in their own infrastructure, an endpoint-fed cloud service is not the same thing. TrustLens gives you the private option.
Directly Integrated in the Interaction Path: On the Traffic vs Beside the Agent
The difference between sitting on the traffic and sitting beside the agent is the difference between the interaction and a reconstruction of it. Posture on the traffic does not have to infer what happened from signals around the event.
TrustLens is integrated directly in the interaction path. Traffic runs through NeuralTrust's gateway and into TrustLens, so the observation is firsthand: the actual call, the actual payload, the actual response, captured as it happens on the path the agent's traffic travels.
Geordie sits beside the agent, at the endpoint, correlating code, endpoint, and API signals. That is close to where activity happens, but it is not on the call, so its view of an interaction is assembled from what those signals expose rather than captured from the traffic itself. Being near the agent is not the same as being on its calls. TrustLens is on the calls.
Observation from the Actual Call, Not Endpoint or API Correlation: The Call Itself vs Signals Around It
Both platforms observe behavior, so the question is what the observation is made of. A record built from the call itself is exact. A record built by correlating signals from the endpoint and APIs is an inference, as complete and as precise as the signals allow.
TrustLens observes the actual call. Because it sits on the traffic, it captures exactly which tool an agent invoked, with what inputs, returning what outputs, as a first-class record it can search, classify, and map to frameworks. There is no reconstruction step, because the call is the source.
Geordie observes by correlating code, endpoint, and API signals around the agent. That produces behavioral observability, but it is an account assembled from the edges rather than the call captured directly, so its fidelity depends on which signals it can gather and how cleanly they correlate. Correlated signals can tell you an agent probably did something. The call tells you exactly what it did. TrustLens has the call.
Coverage of Every Agent's Real Tool-Call Execution: Complete Through the Gateway vs Bounded by Endpoint Coverage
An agent's risk shows up in its tool-call execution. Posture that captures that execution for every agent sees the whole surface. Posture that depends on endpoint reach sees the agents it happens to cover.
TrustLens covers every agent's real tool-call execution because every LLM, MCP, and tool call flows through the gateway it observes. Coverage is a property of the path, not of how thoroughly endpoints were instrumented, so whether an agent runs on a laptop, a server, or a managed platform, its calls pass through the same gateway and are captured the same way.
Geordie's coverage is a property of its endpoint model. It reaches the agents its code, endpoint, and API instrumentation can see, which is broad by design but still bounded by where that instrumentation is present and what each source exposes. An agent running somewhere the endpoint model does not reach, or a call that leaves no correlated signal, sits outside the picture. Coverage that depends on instrumentation reach has edges. Coverage through the gateway does not, because the call has to pass through it. TrustLens captures the call, every time.
Final Verdict
AI-SPM comes down to your vantage point, and NeuralTrust and Geordie chose opposite ones on purpose. NeuralTrust chose the gateway, on the call. Geordie chose the endpoint, beside the agent, and argues that avoiding a gateway is simpler. Simpler it may be, but it decides what you can see: the call itself, or the signals around it.
TrustLens is integrated directly in the path on NeuralTrust's own gateway. It builds posture from the actual call, captures every agent's real tool-call execution completely because the traffic flows through the gateway, runs in your private environment or the cloud, and because it shares a platform with NeuralTrust's runtime security enforcement, what it observes can be acted on in the same path. Coverage is a property of the path, not of endpoint reach, and the observation is the interaction, not a reconstruction of it.
Geordie AI is an endpoint-based platform that observes agents by correlating code, endpoint, and API signals, and it deliberately avoids the gateway. That gives it a light footprint and broad reach across agent types, but it never sits on the call, its posture is assembled from signals around the agent rather than the interaction itself, and its coverage is bounded by where its instrumentation reaches. It observes the agent. It does not observe the call.
Both observe behavior and both clear the enterprise bar, so neither is the deciding factor. The decision is whether you want posture built on the actual call, from a gateway in the path, running in your own environment, with coverage that follows the traffic, or posture built from correlated signals, from the endpoint, bounded by instrumentation reach. If you want to know exactly what an agent did because you were on the call, NeuralTrust is built for exactly that.
Frequently Asked Questions about NeuralTrust vs. Geordie AI
1. What is the main difference between NeuralTrust and Geordie AI for AI-SPM?
NeuralTrust TrustLens is gateway-based and integrated directly in the interaction path, so it builds posture from the actual LLM, MCP, and tool calls agents make. Geordie AI is endpoint-based and deliberately avoids the gateway, observing agents by correlating signals from code, endpoints, and APIs. One is on the call, the other is beside the agent.
2. Does Geordie AI observe agent behavior?
Yes, Geordie provides behavioral observability, but it does so by correlating code, endpoint, and API signals around the agent rather than by capturing the call itself. NeuralTrust observes the actual call on the traffic through its gateway, so its record is the interaction rather than a reconstruction assembled from surrounding signals.
3. Geordie says avoiding a gateway is simpler. Why does NeuralTrust use one?
Because the gateway is the one place that sits on the actual call. Avoiding it does reduce the footprint, but it also means never observing the interaction directly and never having coverage that follows the traffic. NeuralTrust uses a native gateway so posture reflects the real call and coverage is complete for every agent whose traffic passes through it, regardless of where that agent runs.
4. Does endpoint coverage matter for completeness?
Yes. Geordie's coverage is bounded by where its code, endpoint, and API instrumentation reaches, so an agent running outside that reach, or a call that leaves no correlated signal, can fall outside the picture. NeuralTrust's coverage is a property of the path, since every call flows through the gateway, so completeness does not depend on how thoroughly endpoints were instrumented.
5. Are both platforms enterprise-ready and can both run privately?
Both are enterprise-ready, so that is a point of parity. On deployment, NeuralTrust runs in your private environment or the cloud, keeping agent traffic and telemetry in your perimeter, while Geordie is delivered as a cloud service fed by endpoint instrumentation. The meaningful differences are a native gateway on the call versus an endpoint model beside the agent, observation of the actual call versus correlated signals, and coverage that follows the traffic versus coverage bounded by instrumentation reach.
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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