Ask a security team what software is running on a laptop and they will usually get you an answer. Ask them whether the person using that laptop resigned last Thursday, pulled four repositories outside their team scope on Monday, and pasted a customer list into a personal AI account this morning, and the answer takes three days and five browser tabs.

The device data is rarely the missing piece. Most organizations already have it, sitting in a digital employee experience platform that was bought to keep laptops healthy and employees productive. What they don’t have is any way to put that inventory next to the behavior of the human being holding the device.

Anzenna now connects to Nexthink. If Nexthink is already deployed across your fleet, Anzenna reads your device inventory through it and joins that inventory to the identity and behavioral signals already sitting in the Anzenna security context graph. Your endpoints are not asked for anything new.

An Inventory Is Not an Explanation

Endpoint tooling is good at telling you what exists. Here is the machine, here is its operating system version, here are the 214 applications installed on it, here is the last time it checked in. That inventory answers the question it was designed to answer.

Insider risk is a different question. It is almost never about a single object. An engineer downloading repositories two weeks before their last day is not doing anything an inventory can see. A sales rep exporting their book of business to personal cloud storage the night before they resign has installed nothing new. The application that matters is often one that has been sitting on the device for months, quietly acquiring plugins and permissions nobody reviewed.

The Ponemon Institute put the average cost of an insider risk incident at $19.5 million in its 2026 Cost of Insider Risks Global Report, and the average time to contain one at 67 days. Most of those 67 days are behavioral buildup. Your device inventory was present for all of it and had no way to say so, because an inventory records state and insider risk lives in the sequence.

What turns inventory into explanation is context: who this device belongs to, what that person’s peer group does, what changed in the last fourteen days, and whether any of it lines up with a resignation or a credential that showed up in a breach feed. Those signals live in your identity provider, your HR system, your SaaS logs, and your browser. Nexthink holds one column of the answer. Anzenna’s job is to hold the rest and put them in the same row.

The Wrong Answer Is Another Agent

There is a well-worn path here, and it goes badly. The security vendor arrives, explains that it needs its own view of the endpoint, and asks for an agent. IT points out that the fleet already carries an EDR agent, an MDM agent, a patch agent, and a DEX collector. Change management schedules the rollout for next quarter. Ninety days later, coverage sits at 78% and nobody is quite sure which machines are missing.

Every agent added to an endpoint costs something real: CPU, memory, a support surface, a compatibility matrix, and a conversation with the person who has to approve it. Agents also compete. Two tools hooking the same system calls produce failures that are miserable to diagnose and always seem to surface on an executive’s laptop.

Anzenna has been agentless since the beginning, and the Nexthink integration follows the same principle. Nexthink is already deployed, already trusted by your IT organization, and already talking to every managed device you own. It does not need Anzenna’s help to reach an endpoint. Anzenna needs Nexthink’s reach, and Nexthink needs the context Anzenna has been building around identity and behavior. The integration is that trade, and the endpoint never notices it happened.

The AI Governance Problem an Install List Can’t Solve

Here is where the integration earns its place. The fastest-growing risk on your endpoints does not show up as an installed application, so no inventory, Nexthink included, can see it.

An employee runs an AI coding assistant and wires an MCP server into it that reaches a production database. Another connects an AI client to a personal account and pipes internal documents through it. A third grants a browser-based AI tool OAuth access to the corporate Google Workspace. None of that is a program in the applications list. It is configuration and connections layered on top of tools that may well be approved. The Model Context Protocol, the standard that lets AI agents connect to databases, file systems, and internal APIs, is exactly the kind of surface that never appears in a device audit.

To find it, you have to ask the endpoint questions an inventory was never built to answer. What MCP servers is this machine running, and what are they connected to? Which AI clients are installed, and against which accounts? Where is shadow AI usage happening, and who is behind it? Answering those normally means building and deploying your own agent to every endpoint, writing the audits, and maintaining the whole thing. That is a real engineering project, and it is the reason most AI governance programs stall at the policy stage.

Anzenna skips the project. It uses the Nexthink agent already on your fleet to run its own custom queries and audits, and returns the results into the security context graph. The agent is Nexthink’s. The questions are Anzenna’s.

Each audit is dispatched with a signing key scoped to one device and one run, so a token pulled off a single machine is worthless anywhere else and worthless on that machine an hour later. It is the same per-device signing design already running behind Anzenna’s AI audit hook. We did not invent a new trust model for this, and we are not asking you to review one.

The pitch is simple. Deploying an AI-governance agent is hard, and Anzenna removes that burden by riding the agent you already run. You get the answers without building the thing that produces them.

What Else the Integration Does

Beyond the AI surface, the same connection enriches everything Nexthink already knows about a device. Anzenna queries Nexthink through NQL, the Nexthink Query Language, pulls the inventory into the security context graph, and the data stops behaving like a device list and starts behaving like evidence:

See Your AI Surface Through the Agent You Already Run

Anzenna is SOC 2 Type II certified and pentested against Microsoft 365, and it runs more than 130 integrations without an endpoint agent in any of them. Most customers connect Nexthink and are looking at real findings inside the hour.

The MCP servers, AI clients, and shadow AI logins spreading across your fleet are already on those machines. Nexthink can reach every one of them and was never asked the questions that would surface them. Anzenna asks.

Request a demo and we will run your first AI governance audit through Nexthink on the call.