The 9 Best AI Runtime Security Platforms for Enterprise AI Security in 2026

AI agents are now in production inside the enterprise, and they do not just answer, they act. They call tools, trigger workflows, and take real actions on real systems. Runtime security is the layer that stands between an agent's next tool call and your data, and it is decided at the moment of execution, not before deployment and not after an incident.

As agents read databases, send messages, and act on a user's behalf, the runtime layer is where the attacks that matter are either stopped or missed: prompt injection arriving inside a tool response, an agent reaching for a tool it should never touch, data leaving through a tool input, a multi-turn manipulation that no single message reveals. The question for every platform in this space is where it sits when the agent acts, and how much of the agent it can actually see.

This guide compares nine of the most relevant AI runtime security platforms for enterprise AI security in 2026, starting with NeuralTrust TrustGuard and then covering eight other platforms, so security and platform leaders can understand where each one fits and what it was built to do.

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TL;DR

• AI runtime security is the control layer that inspects and enforces on an agent's live interactions, its prompts, tool calls, and tool responses, and stops attacks at the moment of execution rather than flagging them afterward.
• NeuralTrust TrustGuard is the only platform in this list built to extend across every AI agent, with one policy model spanning gateways, SDKs, browsers, and platforms, first-party inline enforcement on the tool call, agent-to-tool authentication, and stateful multi-turn detection.
• Most platforms here were adapted from another category and carry its shape: guardrail APIs, MCP governance proxies, broad AI suites, endpoint detection and response, SASE, and application security.
• Several are now inside a larger platform or an acquisition: Lakera in Check Point, Prompt Security in SentinelOne, SPLX in Zscaler, and Prisma AIRS in Palo Alto, where agent runtime security competes for attention with a broad portfolio.
• Endpoint, network, and detection-first platforms (CrowdStrike, Netskope, HiddenLayer) reach agents from the vantage point they already owned, whether the endpoint, the network, or the SOC.
• The dimension most buyers underweight: whether a platform extends across every agent, or only the surface it came from.

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What Is AI Runtime Security?

AI runtime security is the discipline of protecting AI agents at the moment they act. Where posture management assesses how an agent is configured before it runs, and detection and response investigates after something has happened, runtime security sits on the live interaction and decides, in-flight, whether the agent's next action is safe to execute. It is the control point for the prompt, the tool call, the tool response, and the identity behind them.

A capable runtime security platform does several things at execution time. It inspects every interaction across the surfaces where agents run. It detects the AI-specific threat classes: direct and indirect prompt injection, sensitive-data exposure, unsafe or unauthorized tool use, data exfiltration, and behavioral attacks that unfold across turns. It enforces a decision inline, allowing, blocking, or transforming the action before it reaches your systems. And it does this with enough context, session, identity, and protocol, to catch attacks that a single isolated check would miss.

The platforms in this comparison differ most in where they sit and what they were built for. Some are in the agent's traffic path and enforce on the call itself. Others observe from the endpoint, the network, or a platform's API, and some are guardrail services an application has to call. Many were adapted from an adjacent category, an endpoint agent, a SASE proxy, an application security scanner, a guardrail API, and inherit its reach and its blind spots. Understanding that origin is the key to choosing well, and it is the lens this guide uses throughout.

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Comparison at a Glance

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The 9 Best AI Runtime Security Platforms in 2026

1. NeuralTrust TrustGuard

Most platforms in this list can protect the surface they came from. TrustGuard is built on a different premise, that runtime security has to reach every agent an enterprise runs, and enforce the same way on all of them.

TrustGuard inspects every interaction and stops attacks at the moment of execution, on every surface where agents run: gateways, SDKs, browsers, and platforms. One policy model covers the full threat class at once, injections, sensitive data, unsafe actions, and behavioral attacks, and enforcement happens in-flight, allow, block, or transform, before the action reaches your systems. It reads the session, the identity, and the protocol together, so it catches the indirect injection hidden in a tool result, the agent reaching for a tool it should not use, the automated loop burning through tool calls, the exfiltration buried in a tool input, and the multi-turn attack that no single message reveals.

Because enforcement is first-party and inline, TrustGuard acts on the tool call itself rather than reporting on it afterward. It integrates natively with TrustGate, NeuralTrust's own AI gateway, and extends across SDKs, browsers, and platforms with the same policy model, so coverage does not depend on a per-tool or per-framework integration. It authenticates the agent to the tool at runtime, tying each tool call to the agent's identity, and it runs in your private environment or in the cloud.

NeuralTrust is an independent company focused on AI security, and TrustGuard is built for agent runtime, not adapted from an endpoint agent, a network proxy, or a scanner. That focus is why it is the first runtime security built to extend across every AI agent without a custom integration for each one.

• Agent coverage: Every surface, gateways, SDKs, browsers, and platforms, one policy model
• Enforcement: First-party and inline on the tool call, before execution
• Detection: Stateful, multi-turn analysis across the whole session
• Agent-to-tool authentication: Generally available, tied to agent identity at runtime
• Deployment: Private and cloud
• Compliance: EU AI Act, HIPAA, GDPR, FedRAMP/IL4 pathway
• Analyst recognition: Gartner AI Gateways and Guardian Agents, KuppingerCole Leadership Compass

Best for: Teams that want runtime security that enforces on the tool call and spans every agent they run, in their own environment.

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2. Lakera (Check Point)

Lakera is a guardrail 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 its runtime work now sits inside a much larger security portfolio.

Lakera Guard is a runtime guardrail delivered through the Guard API. An application sends prompts, model outputs, and tool calls to the API, running as a hosted service or a self-hosted container, and acts on the verdict it returns. By Lakera's own documentation, native platform runtime integrations are on the roadmap, so today the runtime layer is a detection service the application has to call at every screening point rather than an enforcement layer that sits on the traffic on its own. For agents, it screens for denied tools and off-task use, which is deny-list policy applied to a call rather than authentication of the agent to the tool.

• Enforcement model: A Guard API the application calls, native runtime integrations on the roadmap
• Agent-to-tool control: Deny-list and off-task policy
• Deployment: SaaS or self-hosted container
• Best for: Adding a guardrail to individual applications

Related article: NeuralTrust vs Lakera: AI Runtime Security Comparison 2026

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3. Prompt Security (SentinelOne)

Prompt Security is an AI-usage governance platform acquired by SentinelOne and folded into its Singularity platform. Its agent protection is built around an MCP gateway and centers on the interactions agents have with Model Context Protocol servers.

It discovers MCP servers, risk-scores them, and enforces allow or block policies on MCP interactions through a security proxy, with data loss prevention and searchable audit logs. Its broader coverage is delivered as separate modules per surface, a browser extension for employee tools, a reverse proxy or endpoint agent for homegrown apps, and the MCP proxy for agents, rather than one runtime layer spanning them all. Its agent controls are allow or block by user, server, or action, which is authorization rather than authentication of the agent's identity to the tool.

• Enforcement model: Security proxy on MCP interactions
• Agent-to-tool control: Allow or block by user, server, or action
• Deployment: On-prem and SaaS
• Best for: Governing access to public MCP servers

Related article: NeuralTrust vs Prompt Security: AI Runtime Security Comparison 2026

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4. Palo Alto Prisma AIRS

Prisma AIRS is Palo Alto Networks' AI security offering, and it is the most complete platform in this list on paper. Much of its AI capability was brought in through acquisition, primarily Protect AI, with agentic endpoint capabilities dependent on the pending acquisition of Koi, integrated onto Palo Alto's broader platform.

Its runtime pieces are an AI Runtime Firewall that inspects AI traffic and an API Intercept that embeds scanning into application code, alongside model security, red teaming, and an AI Gateway. It supports on-prem and cloud deployment. Two things separate it from a platform built for agent runtime. Its detection scans each API call and groups those scans into sessions for logging, which is partial session awareness rather than stateful multi-turn detection. And the component that enforces agent runtime and identity, its AI Agent Gateway, is in limited preview, with agentic endpoint security waiting on a pending acquisition, so the parts most specific to securing autonomous agents at runtime are not yet generally available. It is a broad AI security suite assembled across a large portfolio rather than a single platform built for agent runtime.

• Enforcement model: Inline firewall plus API intercept, assembled from separate components
• Agent-to-tool control: Agent runtime and identity in limited preview
• Detection: Per-call scanning with session logging, partial session awareness
• Deployment: On-prem and cloud
• Best for: Palo Alto customers wanting a broad AI security suite

Related article: NeuralTrust vs Palo Alto Networks: AI Runtime Security Comparison 2026

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5. CrowdStrike Falcon AIDR

CrowdStrike Falcon AI Detection and Response extends CrowdStrike's endpoint detection and response platform to AI. Its stated premise is that the endpoint is the epicenter of AI security, so its protection is anchored on the Falcon sensor.

The sensor captures the commands, scripts, file activity, and network connections of AI applications on the endpoint, and Falcon AIDR uses that telemetry to detect suspicious AI behavior, trace it to a process, and respond, including by isolating the endpoint and routing the incident into the SOC through Fusion SOAR and Next-Gen SIEM. Around that core it adds shadow AI discovery, prompt-layer detection for desktop AI apps, guardrails for Copilot Studio agents, and an MCP proxy that observes and applies policy to tool calls. Its model is detection and response built on endpoint telemetry, so its primary response to a threat is containment and investigation after behavior is observed, and its AI runtime protection is tied to where the Falcon sensor is installed.

• Enforcement model: Detect, trace, and respond via the Falcon sensor
• Agent-to-tool control: Tool-use inspection and policy through an MCP proxy
• Deployment: Cloud-delivered, anchored on the endpoint sensor
• Best for: Falcon customers extending endpoint detection and response to AI

Related article: NeuralTrust vs CrowdStrike: AI Runtime Security Comparison 2026

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6. Zscaler (SPLX)

Zscaler entered AI security by acquiring SPLX, a red-teaming and testing startup, and folding it into the Zero Trust Exchange. Its AI capabilities carry SPLX's origin in testing and discovery.

SPLX centers on automated red teaming, running large batches of attack simulations against AI systems, alongside asset discovery, prompt hardening, and governance. Its runtime piece is a set of input and output guardrails during live deployments, plus threat inspection that works by scanning LLM logs in near real time, which is analysis after the interaction rather than enforcement on the agent's tool calls as they execute. Its agentic security ships as a static scanner that inspects agent workflows before they run. Layered onto Zscaler's cloud-delivered platform, this is a stack built to test, discover, and monitor, rather than to enforce inline across the agent's execution path.

• Enforcement model: Input and output guardrails plus log-scanning threat inspection
• Agent-to-tool control: Discovery, risk scoring, and static testing of tools
• Deployment: Cloud-delivered
• Best for: Zscaler customers adding AI red teaming and guardrails

Related article: NeuralTrust vs Zscaler: AI Runtime Security Comparison 2026

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7. Netskope

Netskope is a cloud-delivered SASE platform whose AI security extends its cloud access security brokering and data loss prevention heritage. Its agentic piece is the Agentic Broker, and it inherits the shape of a CASB.

The Agentic Broker is a proxy that decodes MCP traffic and centers on public MCP servers: discovering them, risk-scoring them, allowing or blocking access to cataloged servers, applying DLP to the workflow, and logging sessions for retrospective investigation. It enforces least-privilege access to those servers, which is authorization at the access layer rather than authentication of the agent to the tool. Its agentic protection is scoped to the public MCP servers and employee AI tools it brokers, and its session handling is an audit trail read after the fact rather than real-time multi-turn detection. It governs access to AI and protects data around agents, delivered from Netskope's cloud, rather than enforcing on the agent's own tool calls wherever the agent runs.

• Enforcement model: Access brokering plus DLP on public MCP servers
• Agent-to-tool control: Least-privilege access to cataloged public MCP servers
• Deployment: Cloud-delivered
• Best for: Governing employee AI usage and public MCP access

Related article: NeuralTrust vs Netskope: AI Runtime Security Comparison 2026

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8. HiddenLayer

HiddenLayer is an AI security company that grew out of model scanning into a detection and response platform, marketed as non-invasive. Its runtime work reflects that origin: it is built to observe and respond rather than to enforce inline on its own path.

Its agentic protection gains runtime visibility through LiteLLM proxy interception, SDK instrumentation, and inspection of a gateway you already run, rather than a gateway of its own. It detects prompt injection, inspects tool use, watches memory and context, and reconstructs agent activity across sessions so analysts can investigate after the fact. Because it is non-invasive by design, its enforcement rides on third-party interception points it does not own, and its strength is detection, investigation, and response for the SOC rather than first-party inline enforcement on the agent's tool calls.

• Enforcement model: Detection and response riding on third-party interception
• Agent-to-tool control: Tool-use inspection, no agent-to-tool authentication
• Deployment: SaaS, on-prem, or air-gapped
• Best for: Detection and response for the SOC

Related article: NeuralTrust vs HiddenLayer: AI Runtime Security Comparison 2026

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9. Mend.io (Mend AI)

Mend.io is an application security platform, and its AI offering, Mend AI, extends that heritage to the AI layer. Its center of gravity is the pre-deployment and governance side of the lifecycle rather than runtime enforcement on the agent.

Mend AI discovers AI components and builds an AI-BOM, hardens system prompts with weakness scoring, runs automated red teaming in the CI/CD pipeline before a build ships, and applies governance policies. Its runtime piece is an in-app guardrail that sits between users and models in production, filtering unsafe output before it reaches a user. That is output filtering on the model's response, not enforcement on the agent's tool calls, tool responses, or identity, and its treatment of agents is largely discovery and testing rather than runtime control of the execution path.

• Enforcement model: A user-to-model output guardrail, with discovery and red teaming pre-deployment
• Agent-to-tool control: Discovery and testing, no agent-to-tool authentication
• Deployment: Cloud service
• Best for: AppSec teams adding AI discovery and testing

Related article: NeuralTrust vs Mend.io: AI Runtime Security Comparison 2026

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Best AI Runtime Security Platforms of 2026: The Final Overview

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How to Choose the Right AI Runtime Security Platform

Most buyers approach this decision by the vantage point they already own: "we run CrowdStrike, extend it to AI", "we run Netskope, use the agentic broker", "we run Palo Alto, evaluate Prisma AIRS". That framing is convenient, but it decides your agent security by the tool you happened to buy for something else, and it inherits that tool's reach and its blind spots.

The honest question to ask about every platform in this list is: does it enforce on the agent's tool call, and does it reach every agent I run, or only the surface it came from?

• TrustGuard extends across every agent with one policy model, enforces first-party and inline on the tool call, authenticates the agent to the tool, and detects multi-turn attacks across the session. It is the one platform here built for agent runtime rather than adapted to it.

• Lakera is a guardrail you call from your application, now inside Check Point. It is a fit for adding a check to individual apps, with native runtime integrations still on the roadmap.

• Prompt Security governs access to public MCP servers through a proxy, now inside SentinelOne. Its coverage is modular per surface.

• Palo Alto Prisma AIRS is the broadest suite, assembled from acquisitions, but its detection is per-call with session logging and its agent runtime and identity gateway is in limited preview.

• CrowdStrike Falcon AIDR anchors on the endpoint sensor and responds by detecting and isolating. If your agents live where the sensor runs and you want SOC-centric response, it fits that model.

• Zscaler brings SPLX red teaming and guardrails onto its cloud platform, strong on testing, with runtime detection by log scanning.

• Netskope governs employee AI usage and public MCP access with brokering and DLP, from its cloud, rather than enforcing on the agent's own calls.

• HiddenLayer is detection and response for the SOC, non-invasive, riding on third-party interception rather than a first-party gateway.

• Mend.io is application security extended to AI, strong on pre-deployment discovery and testing, with a user-to-model output guardrail at runtime.

The one dimension every team underweights: whether the platform extends across every agent, or only the surface it came from. Agents multiply and move, and a runtime layer bound to an endpoint, a network, or a single protocol will always be chasing the next surface. Enforcement that follows the agent is the one that keeps up.

See how TrustGuard compares to your current runtime security

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Key Takeaways

• AI runtime security protects agents at the moment they act, inspecting and enforcing on prompts, tool calls, and tool responses in-flight, rather than assessing configuration before deployment or investigating after an incident.
• The nine platforms come from distinct origins: in-path agent runtime security, guardrail APIs, MCP governance, broad AI suites, endpoint detection and response, SASE, detection and response, and application security.
• NeuralTrust TrustGuard is the in-path option built for agent runtime, extending across every agent with one policy model, enforcing first-party and inline on the tool call, authenticating the agent to the tool, and detecting multi-turn attacks across the session.
• Several platforms are now inside a larger portfolio or an acquisition, including Lakera in Check Point, Prompt Security in SentinelOne, SPLX in Zscaler, and Prisma AIRS in Palo Alto, where agent runtime competes with a broad set of priorities.
• Endpoint, network, and detection-first platforms reach agents from the vantage point they already owned, which shapes both their coverage and their blind spots.
• The right choice follows from one question: whether the platform enforces on the agent's tool call and reaches every agent you run, or only the surface it was adapted from.

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Frequently Asked Questions

1. What is AI runtime security?

AI runtime security protects AI agents at the moment they act. It sits on the live interaction, the prompt, the tool call, the tool response, and the identity behind them, and decides in-flight whether the action is safe to execute, allowing, blocking, or transforming it before it reaches your systems. It is distinct from posture management, which assesses configuration before deployment, and from detection and response, which investigates after something has happened.

2. How is runtime security different from AI posture management?

Posture management discovers AI assets and assesses how they are configured, their permissions, misconfigurations, and exposure, largely before or outside of execution. Runtime security acts during execution, on the agent's actual calls, stopping an unsafe tool call as it fires. Posture tells you what an agent could do. Runtime security governs what it does. Many enterprises run both, with posture upstream and runtime security on the live path.

3. Which platform is best for enterprise AI runtime security?

For a mandate to enforce on the agent's actions across every agent, NeuralTrust TrustGuard is built for it: one policy model spanning gateways, SDKs, browsers, and platforms, first-party inline enforcement on the tool call, agent-to-tool authentication, and stateful multi-turn detection. Platforms adapted from endpoint, network, SASE, or application security can secure the surface they came from, which is a different model from one built to reach every agent.

4. Why does it matter whether a platform was adapted from another category?

Because origin sets reach. An endpoint platform protects AI where its sensor is installed. A network or SASE platform sees AI as traffic crossing the network. An application security tool inspects code and configuration. A guardrail API protects the apps that call it. Each is effective on its home surface and bounded off it. A platform built for agent runtime is designed to follow the agent across surfaces rather than to extend one tool's vantage point to a new problem.

5. What is agent-to-tool authentication and why does it matter?

Agent-to-tool authentication verifies the identity of the agent when it reaches for a tool and enforces authentication and authorization together at runtime, so an agent reaches only the tools it is entitled to, as the identity it actually is. Many platforms enforce authorization, allow or block by policy, but do not authenticate the agent's identity to the tool. Without authentication, an access rule sees the request but not who is really behind it.

6. Do endpoint or SASE platforms cover agents that run on servers?

Coverage depends on the vantage point. An endpoint platform is anchored on a sensor, so agents running on servers or in production without that sensor fall outside its primary model. A SASE or network platform sees agent traffic that crosses the network it controls. Runtime security that sits on the agent's own calls, through a gateway and across SDKs, browsers, and platforms, covers agents regardless of where they run, because coverage follows the traffic rather than the sensor.

7. Is detection and response the same as runtime enforcement?

No. Detection and response observes behavior, flags what looks malicious, and responds, often by isolating a host or routing an incident to the SOC, after the behavior has occurred. Runtime enforcement decides on the action before it executes and blocks the unsafe one in-flight. Both are valuable, but a detect-and-respond model is damage control after the fact, while inline enforcement prevents the action. Some platforms in this list are primarily detection and response.

8. What does stateful multi-turn detection add over per-request checks?

Attacks on agents often span turns: a jailbreak that fails once and lands later, an injection split across messages, a manipulation that escalates slowly. A per-request or per-interaction check judges each message on its own and misses the sequence. Stateful multi-turn detection tracks the conversation and catches the attack that only becomes visible across turns. Session logging for later investigation is not the same, because it reconstructs after the fact rather than blocking in the moment.

9. Can one runtime security platform cover every kind of agent?

That is the design goal of a platform built for agent runtime. NeuralTrust TrustGuard uses one policy model across gateways, SDKs, browsers, and platforms, so a packaged assistant, a custom internal build, and an agent calling MCP tools are all governed the same way. Platforms adapted from another category tend to cover their home surface well and require additional tools or integrations for the rest, which is the coverage gap this comparison highlights.

10. What should I evaluate first when choosing a runtime security platform?

Start with reach and enforcement: does it extend across every agent you run, and does it enforce on the tool call itself rather than report on it afterward. Then check detection depth (stateful and multi-turn, or per-request), agent-to-tool authentication (identity, or authorization only), deployment (private and cloud, or cloud only), and whether the platform is built for agent runtime or adapted from an adjacent category. Matching those to your agent roadmap matters more than any single feature count.

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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.