Govern the AI your people already run
Mondoo is closed-loop AI security for the machines your work actually happens on. Find every AI agent, skill, MCP server, and model installed across your fleet, judge what each one can reach, and govern what is allowed to run before it runs.
Part of Mondoo's continuous threat exposure management
What AI security means once the agents are installed
AI security is the practice of finding, assessing, and controlling the AI systems operating inside an organization, including the agents, skills, plugins, MCP servers, and models running on company machines. It answers a narrower question than AI safety: not whether a model behaves well, but what the AI installed on your estate is able to reach, change, and send.
Most enterprises adopted AI the way they adopted SaaS. Coding agents, IDE assistants, and browser extensions arrived team by team, approved by nobody, because they looked like productivity tools. They are not. An AI agent installed by an employee holds that employee's permissions, and it acts on them without asking:
- Executes commands
- Reads local secrets
- Reaches cloud infrastructure
- Modifies code
- Chains actions across systems
That makes the employee laptop an execution layer for enterprise AI. Most security teams cannot yet answer the basic questions about it:
- Which AI agents are installed?
- Are they approved?
- What permissions do they hold?
- Can they run shell commands?
- What data can they reach?
- Can they be removed?
If you cannot list the AI running on your estate, you are not governing it. You are hoping.
AI security moved to the endpoint
AI security was defined around the model. Is the model safe, is the training data clean, is the endpoint exposed. Those are real questions, and they are well covered.
While the industry was answering them, the exposure moved. It is now a coding agent on a developer's laptop with a loaded skill nobody reviewed, an MCP server pointed at production, and a set of permissions inherited from whoever installed it. The model is not the thing acting on your infrastructure. The agent on the machine is.
Watching is not governing
Most tooling that reaches the endpoint observes. It records what AI did, flags anomalies, and raises an alert. By the time the alert exists, the skill has loaded, the command has run, the data has moved.
Mondoo governs the installed state instead. Approval is expressed as policy, and the policy decides before execution: a malicious skill never loads, a banned agent never operates, an unapproved model never touches company data.
Watching tells you what happened. Governing decides what is allowed to happen.
That is the same discipline Mondoo applies everywhere else: find it, judge it by what it can actually reach, and close it. AI is a new estate, not a new methodology.
Every AI in your estate, in one AI-BOM
An AI Bill of Materials, or AI-BOM, is the inventory that makes everything else possible. Mondoo builds one automatically from your fleet: every installed agent, every loaded skill and plugin, every configured MCP server, every model in use, collected from the machines themselves rather than from a survey.
That AI inventory covers what people approved and, more usefully, what they did not.
Agents
Coding agents, IDE assistants, and desktop AI tools, with version, configuration, and privilege level.
Skills and plugins
Every loaded skill checked against Mondoo's skills database, so malicious and over-permissioned skills surface by name.
You can check a skill against that database yourself, free, before anyone installs it.
MCP servers
Locally configured servers, what they connect to, and whether any of them reach production.
Models
Which models are in use and where, including local models operating outside any approved service.
One AI inventory, enterprise wide, from the first scan.
Shadow AI covers the discovery and governance detail.
How Mondoo closes the loop on AI
AI findings do not get their own console. They enter the same loop, the same queue, and the same remediation workflow as the rest of your exposures.
Detect
Continuous collection across your fleet builds and refreshes the AI-BOM, surfacing new agents, skills, MCP servers, and models as they appear.
Prioritize
Every component is scored on what it can actually reach and do, not on raw severity, so an over-privileged agent with production access outranks a harmless browser extension.
Ship
Mondoo delivers the change: policy that blocks an unapproved agent, removal of a malicious skill, a configuration correction, executed through the endpoint management and device tooling you already operate. No new agent on every machine.
Verify
Every change is re-checked, proven closed, and written back as evidence, so your AI governance record stays current instead of being reconstructed for an audit.
That is the difference between a scanner and a service: we sell the fix, not the finding.
- 60%
- fewer vulnerabilities
- <16 days
- MTTR
- 10x
- faster than manual
- 300+
- customers, including Fortune 50
What enterprise AI security covers
Enterprise AI security is an inventory problem before it is a policy problem. These are the six pieces Mondoo covers.
Fleet-wide AI discovery
Every machine, not a sample. Agents, skills, plugins, MCP servers, and models, collected continuously.
Approval as policy
Define which AI tooling is permitted once, as version-controlled policy, and have it enforced across the fleet instead of restated in a memo.
Privilege and access review
What each agent can execute, which credentials sit within its reach, whether local secrets are exposed to it, and whether any MCP server reaches production.
Malicious skill detection
Loaded skills checked against Mondoo's skills database, so a compromised skill is named rather than discovered later.
Remediation through tooling you own
Removal, restriction, and reconfiguration executed through the endpoint management you already run, not through another agent you have to deploy.
Evidence for regulators
A current, queryable record of what AI runs where, under what approval, with what permissions.
AI security best practices that hold up
Most published guidance targets model builders. If your exposure is the AI your staff installed, the practices that matter are narrower and more boring.
- 1
Inventory before policy. A written AI policy without an AI-BOM is unenforceable. Build the list first.
- 2
Judge capability, not category. "Is this an approved vendor" is the wrong question. "What can this reach and execute" is the right one.
- 3
Govern the installed state. Preventing an agent from loading is cheaper and more reliable than reacting to what it does after it loads.
- 4
Treat skills as dependencies. A third-party skill is untrusted third-party code holding your permissions. Scan it accordingly.
- 5
Use the tooling you already have. AI remediation that requires a new agent on every endpoint will not get deployed.
- 6
Keep the evidence current. Governance you cannot demonstrate is governance you do not have.
Generative AI security best practices for teams building models are a separate discipline with a separate toolset. This page covers the estate side: the AI your organization runs.
Where AI security fits
AI security is one lane inside exposure management, not a separate program. Here is how the pieces relate, and where to go next.
Buy an AI tool on its own and you get another inventory. Run AI security inside Mondoo and your AI estate joins one closed remediation loop.
Common questions about AI security
Find out what AI is running on your estate
An assessment builds your first AI-BOM and shows what Mondoo would close first.
- · Enterprise-wide AI inventory from the first scan
- · Governance before a skill reaches an agent
- · Remediation through the endpoint tooling you already run