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Runtime security for AI agents is not the same product as an AI module bolted onto a usage governance platform. Agents do not just answer, they act. They call tools, trigger workflows, and take real actions on your systems, and prompt injection, poisoned tool results, and rogue tool calls all arrive as plain language. The question is not who can watch AI traffic. The question is who enforces inside the action, on every surface where your agents run.
TrustGuard is NeuralTrust's AI runtime security. It is the first runtime security built to extend across every AI agent, without a custom integration rebuilt for each new tool or platform. Prompt Security, now part of SentinelOne, took a different path. Its agent protection is an extension of a broader AI-usage governance suite, and it is built around Model Context Protocol interactions through a security proxy. This comparison covers both on the terms that matter for AI runtime security: where enforcement lives, how far it reaches, and whether it understands an agent as a conversation and an identity rather than a stream of isolated requests.
TL;DR
- NeuralTrust protects every AI agent with one policy model. TrustGuard enforces across gateways, SDKs, browsers, and platforms from a single policy fabric. Prompt Security's agent protection is centered on MCP interactions and split into a separate module per surface.
- NeuralTrust enforces inline inside a native AI gateway. TrustGuard runs on the live traffic path of a full production AI gateway. Prompt Security's agent layer is a security inspection proxy sitting beside MCP traffic.
- NeuralTrust runs on a single federated control plane across regions. Every TrustGuard deployment is governed from one place. Prompt Security's disconnected and air-gapped instances run in isolation, without a shared control plane over them.
- NeuralTrust is session-aware and authenticates the agent to the tool. TrustGuard tracks context across turns and controls how an agent identifies itself to MCP tools at runtime. Prompt Security inspects each interaction on its own and enforces allow or block policies.
NeuralTrust vs Prompt Security: AI Runtime Security at a Glance
| Capability | NeuralTrust | Prompt Security |
|---|---|---|
| Flexible deployment (private, cloud) | ✅ | ✅ |
| Secures every AI agent | ✅ | ❌ |
| Native AI gateway integrated | ✅ | ❌ |
| Federated control plane across regions | ✅ | ❌ |
| Agent-to-tool authentication | ✅ | ❌ |
| Session-aware detection | ✅ | ❌ |
NeuralTrust vs. Prompt Security: 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 one job, and it does that job the way agents actually behave.
The design starts from a single idea. An agent's risk does not live in one prompt. It lives in a conversation, in an identity, and in the tool call that is about to fire. So TrustGuard operates with one policy model that applies across 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.
What makes it runtime security and not a log viewer is that enforcement happens inside the action. TrustGuard sees the indirect prompt injection hidden in a poisoned tool result, the agent trying to authenticate to a tool it should never touch, the automated loop burning through tool calls, and the exfiltration attempt buried in a tool input. It catches these because it reads the session, the identity, and the protocol together, and it acts before execution rather than reporting after the fact.
And because it is the first runtime security built to extend across every AI agent, you do not rebuild an integration every time your teams adopt a new agent or platform. One policy model reaches all of them.
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What is Prompt Security?
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Prompt Security is an AI-usage governance platform acquired by SentinelOne in 2025 and folded into the Singularity platform. Its core story is visibility and control over how employees and developers use AI tools, with data leakage prevention and shadow AI discovery. Agent protection was added as one more module in that suite.
That agent module is built around the Model Context Protocol. It discovers MCP servers, scores their risk, and enforces allow or block policies on MCP interactions through a security proxy. For homegrown apps it sits inline as a reverse proxy or an endpoint agent and inspects requests and responses. It is AI-usage governance extended toward agents, delivered as a set of separate modules per surface rather than one runtime security layer that spans them all.
Secures Every AI Agent: One Policy Model vs a Module Per Surface
Agents do not live in one place. They run behind gateways, inside SDKs, in browsers, and on agent platforms, and new ones arrive every quarter. Runtime security that only reaches one of those surfaces leaves the rest exposed.
TrustGuard reaches all of them with a single policy model. The same rules on injections, sensitive data, unsafe actions, and behavioral attacks apply whether the agent is calling 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. Your teams write policy once and it protects every agent they deploy, including the ones they have not deployed yet.
Prompt Security does not work this way. Its agent protection is centered on MCP interactions, and its wider coverage is delivered as distinct modules, one per surface. That means separate deployment, separate policy surfaces, and gaps wherever an agent runs on something the MCP proxy does not sit in front of. When a new agent or platform enters the business, you are back to wiring up another module rather than extending protection you already have. What TrustGuard covers by design, Prompt Security covers piece by piece.
Native AI Gateway Integration: Inline Enforcement vs a Security Proxy
Where runtime enforcement lives determines what it can stop. If it runs on the live traffic path, it can block an action before it executes. If it sits beside the traffic, it is always one step behind the thing it is trying to prevent.
TrustGuard integrates natively with TrustGate, NeuralTrust's full AI gateway, so runtime enforcement runs inline on the actual data path where LLM and MCP traffic already flows. Routing, failover, and policy live in the same place the enforcement decision is made, which means TrustGuard blocks, transforms, or allows a tool call in-flight, before it reaches the target. Enforcement is part of the path, not an observer attached to it.
Prompt Security's agent layer is a security inspection proxy. It sits between AI applications and MCP servers and inspects what passes through, but it is a bolt-on proxy rather than runtime security fused into a production AI gateway. There is no native, full AI gateway underneath it carrying routing and failover with enforcement inline on the same path. The result is a security layer that watches MCP traffic, where TrustGuard is enforcement built into the road the traffic drives on.
Federated Control Plane Across Regions: One Place vs Isolated Instances
Enterprises run agents in 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 govern agent runtime security at enterprise scale instead of babysitting a fleet of disconnected installs.
Prompt Security cannot offer the same over its most sensitive deployments. Its on-premises and air-gapped agent security instances are marketed as running with no external connection, fully disconnected from anything outside the customer boundary. By definition those isolated instances are not governed by a single shared control plane spanning regions. Each one is its own island, configured and watched on its own. For a business running agents across many environments, that is a per-deployment operating burden that TrustGuard removes.
Agent-to-Tool Authentication: Identity at Runtime vs Allow or Block
Least-privilege for agents starts with a question most tools skip: is this really the agent it claims to be, and is it allowed to use this tool right now? Blocking a bad request is not enough if nothing verifies the identity behind the request in the first place.
TrustGuard controls how an agent authenticates to MCP tools at runtime. It ties the tool call to the agent's identity and enforces both authentication and authorization at the moment of execution, so an agent can only reach the tools it is actually entitled to, as the identity it actually is. Nobody gets to force a tool call through without control. Identity and permission are verified inline, in the same breath as the action.
Prompt Security operates at the level of policy decisions, not agent identity. It enforces allow or block rules by user, server, or action, which is authorization, but it does not authenticate the agent to the tool as a runtime identity control. That leaves the harder half of the problem open. Allow or block can stop a known-bad pattern, but it does not answer who the agent is when it reaches for a tool. TrustGuard closes that gap by making authentication part of the runtime decision.
Session-Aware Detection: The Conversation vs the Single Request
Attacks on agents rarely fit in one message. A jailbreak fails once and succeeds on the third try. An injection is split across turns so no single request looks dangerous. A manipulation escalates slowly. Security that inspects one request at a time cannot see any of it.
TrustGuard sees the conversation, not just the request. It tracks context across turns and factors identity, protocol, and collector metadata into every decision, so it catches multi-turn attacks that only become visible when you look at the whole session. It models behavior over time, which means probing, slow escalation, and gradual manipulation get flagged before the attack completes rather than after the damage is done. Session awareness is the foundation the rest of the detection is built on.
Prompt Security inspects each interaction in real time, prompt by prompt and response by response. That per-interaction model is fast, but it judges each request on its own, without the session-level view that multi-turn attacks are designed to exploit. Anything that depends on how a sequence of turns fits together lives in the space between requests, and a per-request inspector does not look there. TrustGuard does, by design.
Final Verdict
AI runtime security is not an AI-usage module added to a governance suite. Agents execute, and the attacks that matter live inside the action, across the session, and in the identity the agent presents to a tool. Security built for that behaves differently from a proxy pointed at MCP traffic.
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 inline inside a native AI gateway, it is governed by a single federated control plane across every region, it authenticates the agent to the tool at runtime, and it reads the whole conversation so multi-turn attacks and indirect prompt injection get stopped before they execute.
Prompt Security, now inside SentinelOne, protects agents as one module in a usage governance suite. Its coverage is centered on MCP interactions and split per surface, its enforcement runs through a security proxy rather than a production AI gateway, its most sensitive instances run isolated with no shared control plane, and its detection judges one interaction at a time. And since the acquisition, it is one line item inside a large endpoint, cloud, and SOC platform, where agent runtime security competes for roadmap attention against a much broader portfolio. NeuralTrust is an independent company built around AI security and nothing else, and TrustGuard is the product, not a feature bolted to the side of one.
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 was designed for how agents actually behave, or a governance module reaching toward them. If you are building on agents and you want protection that spans every one of them, enforces inside the action, and understands the session and the identity behind it, NeuralTrust is built for exactly that.
Frequently Asked Questions about NeuralTrust vs. Prompt Security
1. What is the main difference between NeuralTrust and Prompt Security for AI runtime security?
NeuralTrust TrustGuard is purpose-built AI runtime security that extends across every agent surface with one policy model and enforces inline before an action executes. Prompt Security protects agents as one module inside a broader AI-usage governance suite, centered on MCP interactions and delivered as a separate module per surface. One was built for agent runtime, the other reaches toward it.
2. Does Prompt Security protect every AI agent the way NeuralTrust does?
No. NeuralTrust protects agents across gateways, SDKs, browsers, and platforms from a single policy fabric, and it extends to new agents without a new integration. Prompt Security's agent protection is centered on MCP interactions and split into distinct modules per surface, so coverage is added piece by piece as new agents and platforms appear.
3. What does agent-to-tool authentication mean and why does it matter?
It means verifying the identity of the agent when it reaches for a tool and enforcing both authentication and authorization at the moment of execution. NeuralTrust controls how an agent authenticates to MCP tools at runtime. Prompt Security enforces allow or block policies by user, server, or action, which is authorization, but does not authenticate the agent to the tool as a runtime identity control. Without authentication, allow or block only sees the request, not who is really behind it.
4. Why does session-aware detection matter for agent security?
Because the most dangerous agent attacks span multiple turns. A jailbreak that fails once can succeed later, and an injection can be split across messages so no single request looks malicious. NeuralTrust tracks context across the whole conversation and flags gradual manipulation before it completes. Prompt Security inspects each interaction on its own, without the session-level view those attacks are built to exploit.
5. Can both products be deployed privately and in the cloud?
Yes. Both NeuralTrust and Prompt Security support private and cloud deployment, so deployment flexibility is not the deciding factor. The meaningful differences are architectural: NeuralTrust enforces inline inside a native AI gateway, governs every deployment from a single federated control plane across regions, authenticates agents to tools at runtime, and reads the full session, while Prompt Security runs a per-surface security proxy with per-interaction inspection and isolated instances in its most sensitive environments.
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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