Shadow AI refers to AI tools adopted by employees without formal IT approval, vendor review, or data-handling policy. Unlike shadow IT of the past, shadow AI connects directly into business-critical systems (like email, CRM, file stores) so every unapproved connection is a potential route for data exfiltration or attacker access.

Traditional network monitoring and SaaS discovery tools miss a growing share of AI activity, especially browser extensions, locally-installed models, and agentic tools that operate with user-level permissions. Endpoint telemetry from a platform like Field Effect AIDR is currently the most reliable way to get a complete picture of your AI footprint.

They can be, and disproportionately so. Code assistants operate on developer endpoints that carry elevated permissions, access to internal source code, and connections to production systems. And because downloading files and running scripts is their normal behavior, traditional endpoint controls are often configured to exclude them entirely to keep them functioning.

Visibility first. You can't govern what you can't see, and most organizations currently can't see their AI footprint at all. Start by inventorying what's actually running across your endpoints, then layer in policy, vendor review, and employee education from that baseline. The eBook covers what a practical starting framework looks like.

Blocking is part of the answer, but it's not sufficient on its own. Browser-based AI tools, extensions, and locally-installed models are difficult to block completely, and aggressive blocking tends to drive adoption further underground rather than eliminating it. The more durable approach is visibility first, knowing what's running and who's using it, followed by a governance framework that distinguishes high-risk tools from lower-risk ones, so your blocks are targeted and your policies are actually enforceable.

Field Effect's 2026 survey found that the biggest barriers are process-related. Teams cited three issues most often: no clear guardrails on which tools can be used or which workflows require human oversight; limited employee understanding of why AI risk controls exist; and shadow AI adoption that makes policy enforcement impossible before visibility is established. 

AI Detection and Response, included as part of Field Effect MDR, gives security teams a live view of AI tool activity across their organization. It helps uncover which tools are running, which employees are using them, which data sources are connected, and where shadow AI is operating outside policy. Rather than adding a standalone dashboard, AIDR builds AI context into the broader MDR picture, so AI-related detections are investigated alongside everything else. It's designed to turn the AI footprint from a blind spot into something you can actually act on.