)
AI security posture management is decided by one question: does your posture reflect what agents actually did, or what their code and configuration say they could do? Static analysis of repositories, models, and settings maps the attack surface an agent might have. Observing the live interaction shows the tool it actually called, the data it actually moved, the action it actually took. Both are useful, but only one is the record. When an agent reaches into a system and pulls data, you want to know because you saw the call, not because a scan of its code suggested the path was possible.
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 gateway and into TrustLens. Pillar Security approaches AI from the build side. It is an agentless, full-lifecycle platform that discovers AI assets by scanning source code repositories, data platforms, and endpoints, maps the attack surface, red-teams before deployment, and adds runtime guardrails on top. Both are enterprise-ready, so this comparison focuses on what actually separates them for AI-SPM: whether posture is observed or static, whether the platform is integrated in the path or agentless, and where it runs.
TL;DR
- NeuralTrust posture reflects observed behavior. TrustLens builds posture from the live interaction because it sits in the path. Pillar's discovery and AI-SPM are static, built from agentless scans of repositories, platforms, and endpoints.
- NeuralTrust is integrated directly in the path, not agentless. Every LLM, MCP, and tool call flows through its gateway and into TrustLens. Pillar connects agentlessly to your code and platforms.
- NeuralTrust is built on a native AI gateway. Pillar has no gateway of its own and instead secures the ones you run.
- NeuralTrust runs in your private environment or the cloud, and both platforms are enterprise-ready, so that is parity.
NeuralTrust vs Pillar Security: AI-SPM at a Glance
| Capability | NeuralTrust | Pillar Security |
|---|---|---|
| Enterprise readiness | ✅ | ✅ |
| Native AI gateway integration | ✅ | ❌ |
| Flexible deployment (private, cloud) | ✅ | ❌ |
| Directly integrated in the interaction path | ✅ | ❌ |
| Observed behavior, not static posture | ✅ | ❌ |
| Coverage of every agent's real tool-call execution | ✅ | ❌ |
NeuralTrust vs. Pillar Security: Platform Overview
What is NeuralTrust TrustLens?
)
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 what agents actually do rather than from what their code declares.
TrustLens has that vantage point because it is integrated directly in the interaction path. Agent traffic flows through TrustGate, NeuralTrust's 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. So when an agent invokes a tool, reads data, or reaches an external service, TrustLens records the actual inputs, outputs, and system calls, maps them to frameworks like OWASP, MITRE, and ISO, and makes the whole history searchable. Posture here is the live record of behavior, not a projection from static analysis.
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 finding. It runs in your private environment or in the cloud, and it carries the enterprise readiness that production security demands.
)
What is Pillar Security?
)
Pillar Security is an agentless, full-lifecycle AI security platform. It covers AI discovery, AI security posture management, red teaming, and adaptive runtime guardrails, and it connects to your environment agentlessly, integrating with source code repositories, data platforms, and endpoints rather than sitting in the traffic.
Its foundation is static. Discovery scans repositories, platforms, and developer endpoints to catalog agents, models, prompts, tools, and MCP servers, and its AI-SPM maps the connections between those assets to visualize the attack surface and flag misconfigurations, prompt, model, tool, and supply-chain risks. Red teaming then probes that surface before deployment, and adaptive guardrails run at runtime to monitor inputs and outputs. That is a build-time and shift-left model: the posture is assembled from analysis of code and configuration, with a runtime guardrail layer added on top. It holds SOC 2 Type II and role-based access controls, so it meets the enterprise bar. But its posture describes the attack surface an agent's code exposes, which is a different thing from the behavior the agent actually exhibits in the path.
Native AI Gateway Integration: Brokering Every Call vs Agentless Scanning
Posture is only as good as the vantage point it is built from. Brokering every call through a gateway gives you the interaction itself. Scanning code and platforms agentlessly gives you the structure around it.
TrustLens is built on TrustGate, NeuralTrust's AI gateway, which brokers every LLM, MCP, and tool call. That gateway is the point the traffic passes through, and it feeds TrustLens the actual requests and responses, so posture, discovery, and behavior tracking all draw from the live interaction.
Pillar has no gateway of its own. It treats the gateway as something to secure rather than something to provide, connecting agentlessly to repositories, platforms, and endpoints to scan for AI assets and risks. That agentless model is quick to connect, but it means posture is assembled from scanning artifacts around the agent rather than from brokering the calls the agent makes. A gateway that carries the traffic sees the interaction, where an agentless scan sees the code and configuration that produced it. TrustLens sits at the gateway.
Flexible Deployment: Private and Cloud vs Agentless SaaS
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 an outside 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.
Pillar is an agentless platform that integrates with your code, AI, and data platforms from its service and scans them. That model is convenient to stand up, but it operates as an outside service connecting into your environment rather than posture deployed and running inside it. For organizations that want their agent observability and posture running privately, in their own infrastructure, an agentless service that reaches in is not the same thing. TrustLens gives you the private option.
Directly Integrated in the Interaction Path: In the Path vs Agentless Connectors
The difference between sitting in the path and connecting agentlessly is the difference between watching the interaction and inspecting what surrounds it. Posture built in the path does not have to infer behavior from artifacts.
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. Nothing depends on a scan reaching the right repository or a platform exposing the right detail.
Pillar is agentless by design, integrating with source code repositories, data platforms, and endpoints. That is effective for cataloging assets and mapping the attack surface, but it inspects the environment around the agent rather than the traffic the agent generates. Its picture of an agent is built from what those agentless integrations can reach and read, not from the live interaction flowing through a path it sits on. Connecting to the code is not the same as being on the call. TrustLens is on the call.
Observed Behavior, Not Static Posture: What Agents Do vs What Code Declares
This is the heart of AI-SPM. Static analysis says what an agent's code and configuration make possible. Observed behavior says what the agent did. The gap between the two is exactly where risk hides: the tool an agent invoked that its configuration never predicted, the data path it took that no scan flagged.
TrustLens builds posture from observed behavior. Because it sits in the path, it reports posture on what agents actually did: which tools they called, which data they touched, which services they reached, captured as the real interaction. When an agent accesses a resource, TrustLens knows because it saw the call, and posture reflects that live record rather than a projection of what the code allows.
Pillar's discovery and AI-SPM are static. They scan repositories, platforms, and endpoints, catalog assets, and map the attack surface and misconfigurations, which describes the posture an agent's code and configuration expose. Pillar does add runtime guardrails that monitor inputs and outputs, but its posture management is anchored in that static analysis, so the picture it reports is what the agent is set up to be able to do, refreshed by scans, rather than the behavior it exhibited in the moment. Static posture is a map of the possible. Observed behavior is the record of the actual. TrustLens reports the record.
Coverage of Every Agent's Real Tool-Call Execution: The Live Call vs Static Discovery
An agent's risk shows up in the tool-call execution, the moment it reaches data and acts. Posture that captures that execution for every agent sees the real surface. Posture assembled from scanning sees the map of it.
TrustLens covers every agent's real tool-call execution because every LLM, MCP, and tool call flows through the gateway it observes. Whether an agent is a packaged assistant or a custom internal build, the actual execution passes through the path TrustLens watches, so its calls, inputs, and outputs are captured the same way for all of them, completely and firsthand.
Pillar's coverage of real execution depends on what its agentless integrations can reach. Its discovery and posture are built from scanning repositories, platforms, and endpoints, and while its guardrails observe inputs and outputs at runtime, the completeness of that runtime view is bounded by where those agentless integrations are wired in rather than by a single path every call flows through. Scanning where an agent lives is not the same as capturing every call it makes. TrustLens captures the calls, because they all pass through it.
Final Verdict
AI-SPM comes down to whether posture reflects observed behavior or static analysis, and that depends on how the platform is integrated. A platform in the interaction path reports what agents did. An agentless platform that scans code and connects to platforms reports the attack surface an agent's configuration exposes.
TrustLens is integrated directly in the path. It builds posture from observed behavior, tracks how every agent actually acts because all agent traffic flows through NeuralTrust's gateway and into TrustLens, covers every agent's real tool-call execution firsthand, and 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, so posture leads to prevention rather than to a report.
Pillar Security is an agentless, full-lifecycle platform built on static analysis. It discovers assets by scanning repositories, platforms, and endpoints, maps the attack surface, red-teams before deployment, and adds runtime guardrails on top. It is enterprise-ready and it covers the build side well, but its posture describes what an agent's code and configuration make possible, its integration is agentless rather than in the path, it has no gateway of its own, and it operates as an outside service reaching into your environment.
Both clear the enterprise bar, so that is not the deciding factor. The decision is whether you want posture built on what agents actually do, from a platform integrated directly in the path and running in your own environment, or posture built on static analysis, from an agentless platform that scans your code and connects to your platforms. If you want to know what your agents did because you saw it, NeuralTrust is built for exactly that.
Frequently Asked Questions about NeuralTrust vs. Pillar Security
1. What is the main difference between NeuralTrust and Pillar Security for AI-SPM?
NeuralTrust TrustLens is integrated directly in the interaction path and builds posture from observed behavior, what agents actually do. Pillar Security is an agentless platform whose discovery and AI-SPM are built from static analysis of source code repositories, data platforms, and endpoints, describing the attack surface an agent's configuration exposes. One reports the record of what happened, the other maps what the code makes possible.
2. Does Pillar Security observe live agent behavior?
Pillar adds runtime guardrails that monitor inputs and outputs, but its discovery and AI-SPM are anchored in static, agentless scans of code, platforms, and endpoints. NeuralTrust sits in the interaction path through its gateway, so its posture is grounded in the live interaction, the actual calls, payloads, and responses, captured firsthand rather than projected from static analysis.
3. Does Pillar Security have its own AI gateway?
No. Pillar treats the gateway as something to secure and connects agentlessly to your code and platforms, rather than providing a gateway that brokers agent traffic. NeuralTrust is built on TrustGate, its AI gateway, which is both the enforcement point and the source of truth for posture.
4. Can both products run in a private environment?
NeuralTrust TrustLens runs in your private environment or in the cloud, so posture and observability can live inside your perimeter. Pillar is an agentless platform that connects into your code and platforms from its service, so it operates as an outside service reaching in rather than posture deployed and running inside your own environment.
5. Are both platforms enterprise-ready?
Yes. Both NeuralTrust and Pillar Security meet the enterprise bar, with certifications and access controls expected of production security platforms, so enterprise readiness is a point of parity. The meaningful differences are observed behavior versus static posture, direct in-path integration versus agentless scanning, a native AI gateway, and coverage of every agent's real tool-call execution.
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.