Top AI agent security platforms in 2026

What are the top AI agent security platforms in 2026?

Arcjet publishes this guide and appears in the shortlist. It's a map of the market by the job each product does, not an independent ranking. Competitor details come from each vendor's public documentation as of September 2026.

No single platform covers every part of AI agent security, so a single "top 7" ranking rarely helps you buy. Most ranked lists put discovery tools, red-teaming services, prompt scanners, MCP gateways, container sensors, and in-code enforcement side by side, even though a buyer would never choose between them for the same job.

A more useful way to shortlist is by control point: the place in an agent's life where a product can see and stop a problem. Start with the failure you're trying to prevent, find the control point that sees it, and compare two or three vendors there.

When you shortlist, check who owns the product today. As of September 2026, several specialists had joined larger platforms, according to each acquirer's announcement: Protect AI and Portkey moved into Palo Alto Networks Prisma AIRS, Robust Intelligence into Cisco AI Defense, CalypsoAI into F5, Lakera into Check Point, and Prompt Security into SentinelOne.

Control points that decide the shortlist

For a longer evaluation checklist with source links, see AI agent security platforms.

Named shortlist by job

Agent inventory and posture

• Zenity – Observability, AI-SPM, runtime boundaries, and MCP security across SaaS, low-code, custom, and coding agents. A good fit when the security team needs a catalog before it can set policy. For a side-by-side comparison, see Zenity vs Arcjet.
• Straiker – Discovery, adversarial testing, and runtime defense sold as one program. A good fit when you'd rather buy "find, test, and defend" from one vendor than assemble three products.
• Arthur – Discovery, tracing built on OpenTelemetry, continuous evaluation, and guardrails, often deployed in your own cloud. A good fit when you need to show that agents behave reliably, not only that they're filtered.
• Microsoft Agent 365 – An agent registry with governance through Entra, Purview, and Defender. A good fit for Microsoft-centric organizations; Defender for Endpoint can also screen some coding-agent hooks on Windows.
• Noma Security – Posture, detection and response, and red teaming in one platform, with a self-hosted option.
• Palo Alto Networks Prisma AIRS – Inventory, gateway (from Portkey), runtime protection, red teaming, and model supply-chain scanning (from Protect AI). A good fit when you're consolidating on Palo Alto.
• Cisco AI Defense – Model validation, an AI firewall, and runtime guardrails from Robust Intelligence. A good fit when Cisco already runs your network security.

Posture products answer "what agents do we have?" To stop a specific action, such as a call to issueRefund, you also need their runtime module wired into that code path, or a separate enforcement layer.

Adversarial testing before production

• Straiker Ascend – Tests your agent for prompt injection, tool misuse, and multi-step exploit chains.
• Prisma AIRS red teaming – Model and agent assessments for Palo Alto customers.
• Noma and F5 AI Guardrails – Red teaming bundled with their broader platforms.
• Promptfoo and other evaluation tools – Open-source harnesses for scoring prompts and agents in CI. They catch regressions but don't block anything in production.

Red teaming tells you how an agent breaks before you ship it. After you ship, you still need a control that can deny the action in production.

Agent identity and access

• Astrix Security (Cisco) – Non-human identity discovery, extended to agents and MCP servers.
• Linx Security – An identity graph with an MCP gateway that can allow or deny inline.
• Entro and Oasis – Secrets management and just-in-time access for non-human and agent identities.
• CrowdStrike – Identity across people, non-human identities, and agents, following the SGNL acquisition.

Identity tools limit the damage an agent can do: an agent without a write token can't write, however it's prompted. They can't judge whether an action the agent is allowed to take makes sense in context.

Gateways, MCP proxies, and action runtimes

• Arcade.dev – An action runtime that handles delegated user OAuth, per-action permission checks, token storage, and governed tool execution. A good fit for multi-user agents where the hard question is "which person authorized this call?"
• Prisma AIRS AI Gateway (Portkey) – A managed gateway for LLM, MCP, and agent-to-agent traffic inside Prisma AIRS.
• LiteLLM – An open-source proxy with guardrail hooks. You run it and choose which detectors it calls.
• Kong AI Gateway – AI and agent-to-agent plugins for teams that already run Kong in front of their services.
• Runlayer, MintMCP, and Lasso Security – MCP control planes that decide which servers and tools a client may reach.

Gateways work well when traffic already flows through one hop, or when you need one place to cut off a third-party MCP server. They can't see a local tool, a queue job, or a coding-agent action that never crosses the proxy. For more information, see AI gateways vs Arcjet.

Content and retrieval inspection

• Lakera Guard (Check Point) – A dedicated API for screening prompts, tool inputs and outputs, and data leakage, with a self-hosted option. For other options, see Lakera alternatives.
• Azure AI Content Safety Prompt Shields – User-prompt and document shields for Azure customers.
• Amazon Bedrock Guardrails – Managed filters through ApplyGuardrail, including for models hosted outside Bedrock.
• Prompt Security (SentinelOne) – GenAI runtime protection within an endpoint-focused platform.
• HiddenLayer and Pillar Security – Enterprise screening APIs for GenAI and agent traffic.
• Datadog AI Guard – An evaluator you call from services that Datadog already traces, with policy managed in Datadog.
• F5 AI Guardrails (CalypsoAI) – Guardrails and red teaming within F5's application security products.
• Guardrails AI and NVIDIA NeMo Guardrails – Open-source frameworks you host. Detection quality depends on the validators or rails you enable.
• Sunglasses – A local Python library that matches known attack patterns in prompts, files, and MCP metadata without a network call. Pair it with a model-based detector to catch rephrased attacks.

Screen more than the chat box: retrieved documents and tool results carry most indirect prompt injection. A clean score still isn't authorization, so pair any detector with a check on the action itself. For more information, see best prompt injection detection tools.

Enforcement before the side effect

• Arcjet – An SDK for HTTP routes and custom agents, plus hooks for Claude Code, GitHub Copilot, Cursor, and OpenAI Codex. Covers bot detection, rate limits, Shield, prompt injection, PII, and Guard policies on tool calls.
• Rein Security – A code-native sidecar for business-critical enterprise agents, with full execution tracing. For a side-by-side comparison, see Rein vs Arcjet.
• Straiker Defend – The runtime component of Straiker's platform.
• Datadog AI Guard – Returns allow, deny, or abort. It only blocks an action if your code waits for the verdict before running it. For a side-by-side comparison, see Datadog AI Guard vs Arcjet.
• Framework hooks – Mastra beforeToolCall, Claude Agent SDK PreToolUse, and Vercel AI SDK wrappers. They cost nothing, and you write and maintain the policy.

Start here when the incident you're worried about is an unauthorized tool call rather than an unknown agent. For more information about the six boundaries in an agent workflow, see best tools for securing agentic AI workflows.

Adjacent jobs that appear in the same "top platforms" SERP

These products show up in AI agent security searches, but they're built for a different buyer or problem:

• Endor Labs and other AppSec for AI-generated code – Reachability analysis and governance for the code and dependencies that coding agents produce. Important for supply-chain risk, but they don't block a tool call at runtime. For runtime control of coding agents, see coding agent security.
• Agentic SOC tools – Prophet Security, Palo Alto Cortex AgentiX, SentinelOne Purple AI, and Vectra use AI to help security analysts triage alerts. That's a different problem from securing the agents your product runs.
• Workforce AI governance – WitnessAI, Harmonic, Aim Security, Reco, and the AI modules from Zscaler and Netskope control which AI tools employees can use. They don't secure agents you build.
• Open-source research frameworks – LlamaFirewall and similar projects are useful for experiments and internal benchmarks. Before using one in production, plan who writes the policy, who operates it, and what happens when it times out.

Platforms that appear in ranked lists but solve something else

• Sysdig, Aqua, Falco, Wiz, and Tracee – Container and cloud workload security. Essential, but they can't decide whether an agent may refund an invoice. For more information, see application vs container runtime security.
• Snyk, Checkmarx, Semgrep, and Aikido ASPM – Code and dependency scanning. They secure what you ship, not the decisions an agent makes after deployment.
• Langfuse, LangSmith, Arize, and Braintrust – Observability and evaluation. They show you what an agent did last week; they don't stop what it's about to do unless you add blocking yourself.

If a list ranks CrowdStrike Falcon, Lakera, and Falco together without saying which job each one does, use it to discover names, not to make a decision.

What do AI agent security platforms cost?

Most platforms at the posture, red-teaming, identity, and enterprise gateway control points are sold through sales on annual contracts, so expect a quote rather than a price list. The exceptions are mostly at the content and enforcement control points:

• Open source: LiteLLM, Promptfoo, Guardrails AI, NVIDIA NeMo Guardrails, and Sunglasses have no license fee; you pay to host and operate them.
• Usage-based cloud services: Azure Prompt Shields bills per 1,000 text records after a free allowance of 5,000 a month, and Amazon Bedrock Guardrails bills per 1,000 text units.
• Free tiers: Lakera's Community plan includes 10,000 requests a month. Arcjet has a 15-day trial, then a free plan capped at 10,000 requests a month, with paid plans from $25 a month plus usage.
• Preview: Datadog AI Guard is in Preview and Limited Availability, enabled per organization on request.

If a free tier covers your first workflow, you can prove a control on real traffic before starting a procurement cycle for a platform. Prices reflect public pricing pages as of September 2026.

Proofs to demand before you buy

Vendor pages tend to use the same vocabulary: discovery, guardrails, governance. Ask each vendor to show you the following on your own workload:

1. Discovery of unregistered agents. Can it find an agent that sends no telemetry, or only agents that already report through OpenTelemetry?
2. Fast containment. Can you stop one agent, or revoke one MCP server, without coordinating across three teams?
3. A blocked action. Show a denied tool call with the authenticated user and the arguments in the decision, not only a blocked chat prompt.
4. Screening of documents and tool results. Indirect injection arrives through retrieved content and tool output. Confirm the product screens both.
5. Data handling per control. Find out which checks send raw content to another cloud, and whether you can choose per rule.
6. Timeout behavior. Find out what happens when a check doesn't return in time, and whether you can fail open or closed per action.
7. Current packaging. After an acquisition, confirm the product is still sold under the name you evaluated.

How to choose in one afternoon

1. Describe your last incident or near-miss in one sentence.
2. Find the control point in the preceding table that would have seen it.
3. Shortlist two vendors at that control point, plus one at a neighboring control point if you're missing discovery, identity, or red teaming.
4. Run the proofs in the preceding section with each vendor.
5. Expect to combine layers. Identity, content inspection, and action-path enforcement together is a common setup for teams running agents in production.

When Arcjet is not the top pick

• You need an inventory of agents across Salesforce, Copilot Studio, and unsanctioned SaaS first. Start with Zenity, Straiker, Arthur, or Microsoft Agent 365.
• You need a dedicated red team for your agents before launch. Choose Straiker Ascend, Prisma AIRS, or Noma, and add production enforcement afterward.
• You need to govern non-human identities and map credential exposure. Choose Astrix, Linx, or your existing identity platform.
• You need delegated per-user OAuth and hosted tool execution for multi-user agents. Choose Arcade.dev or a similar action runtime.
• You only need content inspection and already buy from Check Point, Microsoft, AWS, Cisco, Palo Alto, F5, or Datadog. Use that vendor's detector.
• You need syscall-level container security. Choose Sysdig, Falco, or Aqua.
• You need to control employee use of ChatGPT and Copilot. Choose WitnessAI, Harmonic, or your secure web gateway vendor.
• You need AppSec for code that coding agents generate. Choose Endor Labs or your ASPM platform, and add coding-agent hooks for runtime control.

Arcjet is the right pick when you already know which agents matter and need a decision in the path of each action – in your handlers and in coding-agent hooks – without deploying a proxy.