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Built on the first Agent Gateway with native security, NeuralTrust protects what agents do: tool calls, reasoning, and MCP connections, not just what they're told. One control plane. Any agent. No custom integration per platform.
Enterprise AI deployments don't run a single agent on a single platform. They run dozens spread across frameworks, providers, and vendors, each connected to a growing web of external tools via the Model Context Protocol (MCP). Until today, securing that landscape meant stitching together point solutions: one custom integration per agent, per platform, per provider.
NeuralTrust is changing that architecture entirely.
We have just unveiled Runtime Security Mesh, the first security layer built to scale across every agent in an enterprise's stack without requiring a bespoke integration for each one. At our core is NeuralTrust's Agent Gateway, the industry's first gateway with native security built in rather than bolted on.
"Every agent security product until now has been a point solution. That does not scale, because the agent ecosystem is not going to consolidate around a single provider."
— Joan Vendrell, Co-founder & CEO, NeuralTrust
Why Existing AI Security Approaches Break at Scale
The challenge isn't protecting one agent, it's protecting an entire fleet of them behaving unpredictably across production environments. Traditional security tools were designed for deterministic software: APIs with known inputs and outputs, flows that don't reason, tools that don't chain together autonomously.
AI agents are different. They reason. They call external tools mid-task. They receive responses from those tools that can themselves contain malicious instructions. They maintain memory across sessions. And crucially, they operate across platforms that will never converge to a single vendor. A point solution that covers one provider leaves every other exposed.
🔍 The scalability problem
A single enterprise deployment can involve dozens of AI agents across multiple frameworks and providers. Point-solution security (one integration per agent) creates compounding operational overhead that collapses at enterprise scale.
How the Runtime Security Mesh Works: Six Stages of Agentic Traffic Inspection
NeuralTrust's platform intercepts and inspects agentic traffic at every critical point in an agent's lifecycle, not just at the input prompt, but throughout the full chain of action, reasoning, and response.
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- Incoming Prompt Inspection: Detects prompt injection attempts, exposed secrets, toxic content, and anomalous input patterns before they reach the agent.
- Tool Call Inspection: Scans outbound tool calls for code injection payloads and credentials that the agent may be inadvertently exposing.
- Tool Response Inspection: Analyzes responses coming back from external tools for indirect prompt injection, a vector where a malicious data source hijacks the agent's next action.
- Tool Access Enforcement: Verifies every tool invocation against authentication and authorization policies, ensuring agents only access what they're permitted to.
- Agent Reasoning Monitoring: Tracks reasoning chains across full sessions to detect behavioral drift, when an agent's decision-making deviates from its intended purpose.
- Final Response Validation: Validates outbound responses for policy compliance and detects any sensitive data that the agent may be surfacing to end users.
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The entire mesh integrates natively with enterprise SIEMs, delivering real-time alerts, full audit logs, and behavioral analytics, giving security teams the visibility they need to govern agentic AI the same way they govern the rest of their stack.
MCP Security: Closing the Fastest-Growing Attack Surface in AI
The Model Context Protocol (MCP) has rapidly become the standard for connecting AI agents to external tools, data sources, and services. That growth has also made it one of the fastest-expanding attack surfaces in enterprise AI deployments.
NeuralTrust's Runtime Security Mesh provides native MCP connection monitoring, inspecting both the tool calls agents make and the responses they receive through MCP channels. This addresses indirect prompt injection, a class of attack where a malicious data source embeds instructions in a tool response, effectively hijacking the agent's reasoning without ever touching the original prompt.
Analyst Recognition
🏆 Gartner: Named a Representative Vendor in 3 Gartner Hype Cycles.
- NeuralTrust Named in Gartner Hype Cycle for Infrastructure Security 2026
- NeuralTrust named Sample Vendor in Gartner Hype Cycle for Application Security 2026
- NeuralTrust Named a Sample Vendor in Gartner Hype Cycle for Data Security 2026
🏆 KuppingerCole: Recognized as a Leader in the 2025 Leadership Compass for Generative AI Defense.
NeuralTrust's enterprise clients include global banks, airlines, energy companies, and government agencies.
One Platform, Any Agent, Any Platform
The defining characteristic of the Runtime Security Mesh is its extensibility. Rather than requiring security teams to maintain a separate integration for every agent framework they adopt (LangChain, AutoGen, CrewAI, OpenAI Agents, or any future platform) NeuralTrust's Agent Gateway acts as a universal control plane that sits in front of agentic traffic regardless of its origin.
This matters not just operationally but strategically. The AI agent ecosystem is fragmenting, not consolidating. New frameworks, new MCP servers, and new provider-specific toolchains are emerging faster than security teams can track them. A platform that requires custom integration with each one is not a security solution, it's a permanent maintenance burden.
Available Now
NeuralTrust's Runtime Security Mesh is available now. We are also exhibiting at Black Hat USA 2026, Booth 8106, in Las Vegas, August 4-6.
Frequently Asked Questions
1. What is a Runtime Security Mesh for AI agents?
A Runtime Security Mesh for AI agents is a security layer that monitors and enforces policy on agentic traffic in real time (covering incoming prompts, tool calls, tool responses, agent reasoning, and final outputs) across any AI agent platform, without requiring a custom integration per agent or provider.
2. What is indirect prompt injection in AI agents?
Indirect prompt injection is an attack where a malicious instruction is embedded in data that an AI agent retrieves from an external source (such as a tool response, a web page, or a database record) rather than in the original user prompt. The agent processes the malicious content as legitimate context, allowing an attacker to hijack the agent's actions without direct access to the original prompt.
3. Why can't existing security tools protect AI agents?
Traditional security tools were built for deterministic software with predictable inputs and outputs. AI agents reason, chain tool calls autonomously, operate across multiple external services via MCP, and maintain memory across sessions. These behaviors create attack vectors (prompt injection, tool call manipulation, reasoning drift) that conventional firewalls, WAFs, and API gateways were not designed to detect or prevent.
4. Has NeuralTrust been recognized by Gartner?
Yes. NeuralTrust has been named a Representative Vendor in two separate Gartner Market Guides related to AI security. The company has also been recognized as a Leader in KuppingerCole's 2025 Leadership Compass for Generative AI Defense.
5. What is the Model Context Protocol (MCP) and why does it need security?
The Model Context Protocol (MCP) is an open standard that allows AI agents to connect to external tools, APIs, and data sources in a standardized way. Unsecured MCP connections are a primary vector for indirect prompt injection, unauthorized tool access, and credential exposure. NeuralTrust's Runtime Security Mesh provides native MCP monitoring as part of its six-stage inspection framework.
About the Author
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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