Unapproved tools
AI applications may be adopted outside established review and inventory processes.
Shadow AI
Bring unmanaged AI use into view.
Employees and developers can adopt AI quickly. Security teams need to see where that use appears and how it connects to the enterprise.
Direct definition
What is Shadow AI?
Shadow AI refers to AI applications, models, and services used inside an organization without complete visibility or governance from security and IT teams.
AxLoop knowledge base
The visibility problem
Evidence must come before control.
Unknown destinations
Teams may not know which external AI endpoints enterprise devices communicate with.
Missing context
Cloud-only views can miss local applications, agents, and models operating on devices.
How AxLoop helps today
Discover from the endpoint outward.
AxLoop identifies supported AI applications, agents, local models, runtimes, and MCP configuration evidence. Each finding keeps installed, configured, running, and independently verified states distinct.
- Identify supported software and configuration evidence
- Preserve device and user scope
- Monitor meaningful state changes
Questions teams ask
- Q1Where is Shadow AI appearing across our endpoints?
- Q2Which new AI applications appeared this week?
- Q3Which external AI services receive connections from devices?
Common questions
Answers, briefly.
- Is Shadow AI always a policy violation?
- No. Much Shadow AI is well-intended: employees and developers adopt tools that make them faster. The risk comes from missing visibility and ownership. Finding it first lets teams approve useful tools, replace risky ones, and set clear guidance.
- Where does Shadow AI usually appear?
- Common places include desktop AI assistants, browser-adjacent apps, AI coding agents and CLIs, IDE extensions, locally run models, and MCP servers configured directly on a workstation. Many of these never pass through a central proxy or approved vendor list.
- How do security teams find Shadow AI?
- Endpoint-first discovery observes supported installed and running AI software and configuration evidence on managed devices. That produces evidence teams can investigate instead of relying only on surveys or network guesses.
- What should teams do after finding Shadow AI?
- Assign an owner, confirm what the tool connects to, decide whether it should be approved, restricted, or removed, and keep monitoring for new instances. The goal is a living inventory rather than a one-time audit.
Agent Interaction Observability