Best AI security tools for developers

What are the best AI security tools for developers?

Rank by what you can call, not by category page. The platform map already sorts vendors by layer.

Libraries that only judge text still count for the first two jobs. Microsoft Presidio returns spans. Guardrails AI is Guard().validate(text) in Python. Llama Guard is a classifier that you host. NVIDIA NeMo Guardrails is config plus callbacks. They aren't a deny on refundInvoice with the tenant from the session.

A gateway sees only the traffic that it routes. A local fs.writeFile, a queue job, or a direct API call doesn't go through it. A trace viewer reconstructs the week. It doesn't stop Tuesday's refund.

Vendor-by-vendor detail belongs on the platform map, plus Rein and Datadog AI Guard.

A ranking that starts with "enterprise platform" is answering a buyer. A ranking that starts with a function on the message is answering the person who owns the chat route.

What is the best way to secure LLM applications in production?

Call the check before the hop: screen the prompt, screen the retrieved chunk, and deny the tool. Keep the raw body in your process when you can.

This scan-before-provider example uses Arcjet. Any library that you can call the same way works.

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import arcjet, { detectPromptInjection, sensitiveInfo } from "@arcjet/next";
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const aj = arcjet({
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  key: process.env.ARCJET_KEY!,
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  rules: [
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    detectPromptInjection({ mode: "LIVE" }),
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    sensitiveInfo({
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      mode: "LIVE",
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      deny: ["CREDIT_CARD_NUMBER", "EMAIL"],
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    }),
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  ],
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});
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export async function POST(req: Request) {
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  const { messages } = await req.json();
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  const lastMessage = messages.at(-1)?.content ?? "";
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  const decision = await aj.protect(req, {
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    detectPromptInjectionMessage: lastMessage,
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    sensitiveInfoValue: lastMessage,
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  });
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  if (decision.isDenied()) {
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    return new Response("Please rephrase your message.", { status: 400 });
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  }
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  const reply = await callProvider({ messages });
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  return Response.json({ reply });
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}

Keep the deny response generic. A clean PII score isn't authorization. Authorization is a later check on this tool, these args, and this user.

Pre-runtime evals and post-runtime traces are the other two timescales. They don't replace the call in the handler. Runtime security for LLM applications splits injection, exfil, and unsafe actions. Keeping security inspection local is why the PII scan doesn't need a second vendor to see the raw body.

How do I add runtime security to AI agents in production?

Pick one workflow. List the inbound strings and the tools. Put a detector on every inbound string. Put an allow-or-deny on every tool, in the same process, with the user that you already authenticated. The check sits in refundInvoice, with this user and these args, before Stripe runs.

Dry-run for a week, then enforce. Don't start by buying a control plane and hoping the tool call shows up.

What is AI agent runtime security? is the longer definition.

How does SDK-based security differ from a WAF or an AI gateway?

A WAF sees HTTP: IP, headers, and sometimes the body. It doesn't see refundInvoice(invoiceId, actor). An AI gateway sees the traffic that it routes, usually MCP. A local tool, a queue consumer, or a direct API call never crosses it.

An SDK runs in your process. You pass it the string or the tool args. It returns allow, deny, or a redacted copy while you still have time to stop the hop. The identity is the one already on the request or the session.

Use a WAF for request-path abuse. Use a gateway to sanction the MCP servers that you allow. Use an SDK for the action itself. The category map is the longer sort if you're buying more than one layer.

How do I pick a tool I can call from application code?

Can you import it and call it on this string or these args? If the answer is a sidecar, a proxy, or a dashboard, then that's a different install from a function next to the tool.

Does it return allow, deny, or a redacted copy in time to stop the hop? A label that you read after the provider call is a report.

Does it use the identity you already have? Policy that needs the user, the tenant, or the plan has to run where those values exist.

Does the raw body have to visit a second vendor? A local PII scan keeps the string in your process. A cloud prompt-injection check often sends the string.

Who authors the rule? A rule in the same pull request as the tool is reviewed with the tool. A rule in a console can change without a deploy. Most teams need both. The missing one is usually the check in the function.

What happens when the check doesn't finish? You choose fail-open or fail-closed per action. Don't make a search endpoint and a refund endpoint share one global answer.

Does it run on paths with no HTTP request? Tools, MCP handlers, and jobs still need a check.

If the shortlist is only platforms, you'll ship the chat route with no scan. Import something and call it, then open the dashboard.