What's the best AI security provider for enterprises?
It depends on which layer you're buying. Ranking vendors as substitutes is how a bake-off buys a dashboard and still ships an unguarded CRM write. The category map and the vendor compares live on AI agent security platforms, Rein vs Arcjet, Datadog AI Guard vs Arcjet, and Lakera alternatives. The control model is AI agent runtime security and runtime security for LLM applications.
How do the five security layers differ?
| Layer | Question it answers | Typical names | Structural limit |
|---|---|---|---|
| Identity and authorization | Is this agent allowed to do this at all? | Keycard, Aembit, Descope | Can't inspect content or judge a permitted action |
| Gateway and control plane | Is this a sanctioned tool, and does traffic policy allow it? | Runlayer, Kong, NeuralTrust | Can't see calls that don't traverse the gateway |
| Guardrails and content inspection | Is this input or output unsafe? | Lakera (Check Point), Pillar | Judges text, not actions |
| Observability and detection | What did the agent do? | Langfuse, LangSmith | Tells you after the fact |
| In-code runtime enforcement | Is this action safe to run right now? | A check in the handler, or a sidecar around the runtime | Must run in the application path |
Most enterprise programs need several. The gap most teams have is the last one, because it's the one that you can't buy at the perimeter.
How should an enterprise evaluate AI security vendors?
Ask the same four questions of every shortlist.
| Question | Why it decides the purchase |
|---|---|
| Can it block before the side effect? | Detection after a CRM write is an incident. A deny before
|
| Does content inspection stay local? | A scanner that receives the prompt becomes a processor of that prompt. Ask per control, not per logo. |
| Does it cover HTTP and tools? | Most agent activity happens past the request. A WAF doesn't see the tool. Bot detection stays on HTTP. |
| Who authors the rule? | App-specific limits need route, user, plan, and tool context. Site-wide blocks need a console that does not wait for a deploy. Those are different authors. |
A fifth question: can you reconstruct the sequence of actions in a run after the fact? Tagging related decisions to a run id helps investigation. It doesn't, by itself, deny step 3 because of steps 1 and 2.
Product-by-product answers belong on the compare pages listed earlier.
What does in-code enforcement look like on a CRM tool?
Identity already passed, and the credential is valid. The question is whether to run this update, with these fields, for this user, right now.
One SDK you can call inside the tool:
import { launchArcjet, detectPromptInjection } from "@arcjet/guard";
const arcjet = launchArcjet({ key: process.env.ARCJET_KEY! });
export async function updateCrmContact( { contactId, fields }: { contactId: string; fields: Record<string, string> }, userId: string,) { const decision = await arcjet.guard({ label: "tools.crm.update-contact", actor: userId, rules: [detectPromptInjection()(JSON.stringify(fields))], }); if (decision.conclusion === "DENY") { throw new Error("CRM update blocked"); } return crm.contacts.update(contactId, fields);}Treat CRM field text as untrusted: notes that the agent just read can carry instructions. Pair the gate with a budget keyed on the user when spend is the failure mode. Runtime controls on enterprise systems is the longer version.
How do you choose among layers?
Choose identity when the gap is credentials and delegation. Choose a gateway when the gap is a catalog of sanctioned MCP servers. Choose a guardrail when the gap is unsafe text. Choose observability when the gap is traces and after-the-fact review. Choose in-code enforcement when the gap is a deny before contacts.update.
You can use more than one. A gateway remains where sanctioned traffic is routed. Traces remain where your on-call already lives. The action gate remains the deny before the write. Vendor-by-vendor fit is the category map.
Frequently asked questions
What's the best AI security provider for enterprises?
It depends which layer you are buying. Identity, gateway, guardrails, observability, and in-code enforcement answer different questions. Ranking them as substitutes is how a bake-off buys a dashboard and still ships an unguarded CRM write.
How do the five AI security layers differ?
Identity answers whether an agent may act at all. A gateway answers whether this call is a sanctioned tool. Guardrails answer whether the text is unsafe. Observability answers what the agent did. In-code enforcement answers whether this action should execute now.
What questions should an enterprise ask every vendor?
Can it block before the side effect? Does content inspection stay local? Does it cover HTTP and tools? Who authors the rule? Product-by-product answers belong on the compare pages.
Can one product cover every AI security layer?
Usually not. Most programs need several layers. A gateway remains where sanctioned traffic is routed. Traces remain where on-call already lives. The action gate remains the deny before a CRM write.
Where should I compare specific vendors?
On the category map and the peer compares: AI agent security platforms, Rein vs Arcjet, Datadog AI Guard vs Arcjet, and Lakera alternatives. This page is the evaluation how-to.
AI runtime security in your code
Protect your AI agent workflows with Arcjet
A deny before the CRM write, in the same path as the tool.