N-able launches shadow AI visibility across UEM and security platforms to curb unmonitored data leakage

In a direct response to the explosive rise of unsanctioned generative AI use in the workplace, global cybersecurity and business resilience vendor N-able has launched Shadow AI Visibility, a native monitoring capability integrated across its entire endpoint management and security operations ecosystem.

The software rollout embeds identity-attributed tracking directly into N-able’s flagship Unified Endpoint Management (UEM) solutions, N-central and N-sight, as well as its Adlumin-powered Security Operations platform.

By analyzing traffic at the endpoint and network layers simultaneously, the architecture allows Managed Service Providers (MSPs) and internal IT administrators to detect and categorize unapproved AI interactions across corporate infrastructure without needing to install specialized secondary agents or purchase supplementary data-loss prevention (DLP) tools.

Exposing the hidden AI attack surface

The launch targets a widening operational blind spot within enterprise networks. Internal data from a recent Gartner survey of global cybersecurity leaders revealed that 69% of modern organizations have either documented or heavily suspect that their staff are routinely routing corporate data through prohibited, public generative AI platforms to complete their daily tasks.

When employees copy proprietary software code, sensitive customer datasets, or internal financial spreadsheets into unvetted consumer chatbots to speed up workflows, they unknowingly create severe compliance exposures and untraceable decision trails.

N-able’s updated telemetry engine addresses this behavior by automatically identifying a broad spectrum of shadow AI vectors, including standalone web applications, browser extensions, developer tools, command-line interfaces, and underlying API service connections.

Inside the real-time classification engine

The raw activity data pulled by the updated agents is fed into a centralized governance dashboard that organizes detected AI tools based on a structured metadata hierarchy:

• The Model Family & Vendor Layer: Grouping activity by provider, such as OpenAI, Anthropic, or Google, to identify corporate dependencies.

• The Operational Classification Layer: Separating general-use text assistants from domain-specific tools like autonomous coding agents or code interpreters.

• The Approval Status Matrix: Enabling administrators to dynamically tag applications as Approved, Restricted, or Expressly Prohibited based on regional compliance policies.

Importantly, the system pairs these application footprints with strict identity and device attribution. Instead of simply alerting an MSP that an endpoint is communicating with an external LLM, the dashboard flags the exact corporate identity, localized user account, and background process driving the data exchange.

This granularity allows security teams to map out distributed patterns of shadow AI adoption and isolate high-risk users who are actively inputting restricted data into public training models.

Driving new compliance revenue streams for MSPs

The strategic update arrives as N-able undergoes an aggressive corporate pivot, intentionally rebranding itself from a traditional remote monitoring and management (RMM) vendor to a comprehensive security and business resilience provider. The addition of automated shadow AI detection directly follows N-able’s strategic acquisition of cloud-native SOC platform Adlumin, which has accelerated the company’s fastest-growing revenue segment at a 17.5% year-over-year clip.