Best AI security tools for developers

What are the best AI security tools for developers in 2026?

Arcjet publishes this guide and appears in the shortlist. We rank tools by whether a developer can call them from a handler without a procurement cycle, and we include a section on when Arcjet is the wrong pick.

Most "best AI security tools for developers" roundups recommend code scanners: Checkmarx, Semgrep, Snyk, Aikido, and Cycode. Those tools find vulnerabilities in the code you ship, which matters. They don't decide whether a prompt, tool call, or refund is allowed to go ahead once your application is running.

This guide focuses on runtime AI security: tools you call from your application before a prompt reaches the model or a tool runs. It also covers the neighboring categories that show up in the same searches, so you can tell which job each one does.

Vendor details in this guide reflect public documentation as of September 2026.

Developer shortlist: tools you call before the hop

Older tutorials still recommend LLM Guard and Rebuff. Both are archived, so don't start a new integration on either one. For more information, see best prompt injection detection tools.

What do developer AI security tools cost?

Most tools on this shortlist let you start without a sales call. The pricing model matters more than the headline price, because it decides what grows your bill: request count, characters screened, or infrastructure you run yourself.

Two things catch teams out. Character-based billing counts everything you screen, so a 3,000-character prompt with retrieved context costs three units, not one. And open-source detectors are free to license but not to run: someone has to host, tune, and update them, and a detector nobody updates is how teams ended up relying on archived projects. Prices reflect public pricing pages as of September 2026.

Tools that win other "AI security for developers" lists

A shortlist built only from code-scanning comparisons leaves your chat route unscreened in production. Install a runtime check on that route first, then add dashboards and scanners around it.

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

Run your checks before each hop: screen the prompt before it reaches the model, screen each retrieved chunk before it enters the context, and check each tool call before it runs. Where you can, keep the raw content in your own process.

1
import arcjet, { detectPromptInjection, sensitiveInfo } from "@arcjet/next";
2

3
const aj = arcjet({
4
  key: process.env.ARCJET_KEY!,
5
  rules: [
6
    detectPromptInjection({ mode: "LIVE" }),
7
    sensitiveInfo({
8
      mode: "LIVE",
9
      deny: ["CREDIT_CARD_NUMBER", "EMAIL"],
10
    }),
11
  ],
12
});
13

14
export async function POST(req: Request) {
15
  const { messages } = await req.json();
16
  const lastMessage = messages.at(-1)?.content ?? "";
17

18
  const decision = await aj.protect(req, {
19
    detectPromptInjectionMessage: lastMessage,
20
    sensitiveInfoValue: lastMessage,
21
  });
22

23
  if (decision.isDenied()) {
24
    return new Response("Please rephrase your message.", { status: 400 });
25
  }
26

27
  const reply = await callProvider({ messages });
28
  return Response.json({ reply });
29
}

Retrieved documents and tool results never pass through that route handler, so screen them where they enter the agent loop. guard() takes a string and needs no Request, which lets you call it in the retriever or the tool:

1
import { detectPromptInjection, launchArcjet } from "@arcjet/guard";
2

3
const arcjet = launchArcjet({ key: process.env.ARCJET_KEY! });
4
const promptInjection = detectPromptInjection();
5

6
export async function retrieveContext(
7
  query: string,
8
  session: { userId: string },
9
) {
10
  const chunks = await vectorStore.search(query);
11
  const safe: string[] = [];
12

13
  for (const chunk of chunks) {
14
    const decision = await arcjet.guard({
15
      label: "rag.chunk",
16
      actor: session.userId,
17
      rules: [promptInjection(chunk.text)],
18
    });
19

20
    if (decision.conclusion === "ALLOW" && !decision.hasFailedOpen()) {
21
      safe.push(chunk.text);
22
    }
23
  }
24

25
  return safe;
26
}

This example drops a chunk when the check fails open, which suits retrieval: losing one document costs less than passing an unscreened one to the model.

Keep the denial message generic so it doesn't tell an attacker which rule fired. A clean PII or injection score isn't authorization: whether this user may call this tool with these arguments is a separate check. For more information about separating injection, data exfiltration, and unsafe actions, see runtime security for LLM applications.

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

Start with one workflow. List every string that enters it from outside – user messages, retrieved documents, tool results – and every tool it can call. Put a detector on each incoming string, and an allow-or-deny check on each tool call, in the same process and with the user you already authenticated. Run in dry-run mode for a week, review the results, then enforce.

That approach gets protection onto a real workflow faster than deploying a control plane and waiting for tool calls to appear in it. For a longer definition, see what is AI agent runtime security?. If you first need an inventory of agents or a view of their credentials, see top AI agent security platforms.

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

A WAF sees HTTP traffic: IP addresses, headers, and sometimes the request body. It never sees a function call such as refundInvoice(invoiceId, actor). An AI gateway sees the traffic routed through it, usually model or MCP calls. A local tool, a queue consumer, or a direct API call from your code never passes through it.

An SDK runs in your process. You pass it the prompt or the tool arguments, and it returns allow, deny, or a redacted copy before the next step runs. Each layer has its place: a WAF for abuse on HTTP routes, a gateway for approving which MCP servers can be used, and an SDK for decisions about the action itself.

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

Ask these questions of each candidate:

• Can you import it and call it on a specific string or set of tool arguments?
• Does it return allow, deny, or a redacted copy before the next step runs?
• Can it use the user identity you've already authenticated?
• Does raw content have to be sent to another vendor?
• Where are rules written: in pull requests, in a console, or both?
• What happens if the check doesn't finish in time, and can you choose that behavior per action?
• Does it work on code paths that have no HTTP request?

When Arcjet is the wrong pick

• You need code scanning like Semgrep or Snyk. Choose an ASPM tool; Arcjet isn't a static analysis product.
• Your prompts are regulated and can't leave your environment for injection scoring. Use Lakera's self-hosted inspector, Guardrails AI, or NeMo Guardrails. Arcjet's prompt-injection detection runs in Arcjet Cloud.
• You've standardized on AWS and only need managed content filters. Bedrock Guardrails is built into AWS, so it adds no new vendor. You still need an authorization check on tool calls.
• You already use Datadog APM and want traces and blocking from one vendor. Datadog AI Guard fits, as long as your code waits for its verdict before acting.
• You don't use JavaScript, TypeScript, Python, or Go. Choose a detector with an HTTP API, or a runtime agent that covers more languages, such as Aikido Zen.
• You only need tracing. Langfuse or LangSmith covers that.

Arcjet is a strong fit when one engineer needs to protect a chat route and its tool handlers this sprint, and wants that protection to be easy to maintain six months later.