Like many IT and security professionals, you’re probably concerned about the extent to which AI is used day-to-day in your organization. You’ve probably even reviewed the available platforms months ago, created policies around usage, listed the approved AI tools, and briefed your departments.

Despite all of your efforts, chances are employees are running unapproved and unmanaged AI services that impact or come into contact with sensitive or proprietary company data. This major blind spot is known as “shadow AI” and it is an exponentially growing problem you can’t afford to ignore.

The good news is the combined discovery and governance features of Lansweeper and Atlassian can help you close the growing shadow AI gap in your environment.

AI Governance Requires Total AI Visibility

Most enterprises have already invested in IT service management (ITSM). They’ve created structured workflows for change requests, incident response, and risk tracking. For Atlassian shops, Jira Service Management handles that operational layer, and Assets gives teams a system of record for the assets and configuration items those workflows touch.

That architecture works well for the assets you already know about.

The AI governance challenge is different. Managing known tools through existing workflows is only half of the equation. The new challenge is finding AI tools, services, dependencies, and connections that were never submitted for review. Organizations need visibility into operations that exist entirely outside the formal process, to bring them under control and prevent exposures.

It turns out that 60-70% of organizations are believed to be exposed to shadow AI according to Technology Radius. The problem is, none of this activity is logged, tracked, or, when necessary, remediated. Technology Radius also found a massive governance gap in the same report with 87% of organizations lacking mature shadow AI detection capabilities.

Under the existing framework, software inventories only show what’s been installed through managed channels. Creating asset logs requires a lot of manual intervention and these efforts lack cross-asset context. Meanwhile, browser-based AI usage and personal instances of ChatGPT or Claude running on employee hardware is essentially invisible to most organizations.

The gap this creates is real: IT and security teams can see what’s approved but they’re missing all of the unmanaged AI setting up shop in their environment.

What Lansweeper and Atlassian Deliver Together

Before you can govern anything, you need visibility. Lansweeper offers continuous, AI asset discovery, creating that data foundation. With a single toggle in a Lansweeper discovery action, IT teams get full visibility across managed endpoints (Windows, macOS, and Linux) into three categories of AI activity that most organizations cannot currently see:

• AI applications installed on devices. ChatGPT desktop clients, Claude Desktop, Microsoft Copilot, and other AI tools installed directly on endpoints. Whether or not they went through a formal software request.
• External AI services devices are connecting to. Network-level connections to AI APIs and external model providers, surfaced at the asset level so you can see which devices are reaching outside the environment and to what services.
• Local LLMs running internally. Models being run directly on employee hardware, which is one of the fastest-growing and least-visible categories of shadow AI in enterprise environments today.

Each finding is tied to a specific asset, with full context: device type, owner, operating system, network location, and existing risk signals. It’s not a log entry or an alert. It’s an asset-level picture of what AI is actually doing in your environment, ready to flow directly into Atlassian.

That is where the visibility Lansweeper provides is transformed into actionable governance. Atlassian offers a strong foundation for an AI governance workflow. Jira Service Management handles incident creation, change management, and remediation tracking so unmanaged AI assets are properly addressed and measured against existing policy.

These newly identified and classified AI asset records serve as the source of truth for governance and policy enforcement. What’s more, Rovo agents are able to reason across Atlassian data to accelerate response and assessment, allowing organizations to bridge the shadow AI gap as quickly as possible.

Constructing the End-to-End Governance Chain

Together, Lansweeper and Atlassian create a three-layer AI governance chain that runs continuously, without manual effort:

• Lansweeper surfaces shadow AI risk. Discovery runs across the environment. When an unapproved AI application appears, when a device starts connecting to an external AI service, or when a local model is detected, Lansweeper picks it up immediately, tied to a specific asset and user.
• Atlassian Assets become the system of record. Lansweeper feeds discovered AI tools and services directly into Atlassian Assets. IT admins classify each item (approved, unapproved, low risk, high risk) and filter the dashboard by policy status. Atlassian becomes the authoritative record of AI operating in the environment and their classifications.
• JSM automates the response. Based on classification and policy, JSM workflows trigger automatically:
  • incident for a high-risk unapproved tool is surfaced → a change request populates for a tool pending review or → a remediation ticket is created for a device with policy violations

By combining Lansweeper and Atlassian, AI governance moves from a periodic exercise to a continuous operational capability, all inside the ecosystem your teams already use.

Getting a Leg Up on Shadow AI

For enterprises evaluating how to approach AI governance, the Lansweeper and Atlassian combination delivers something that’s hard to replicate with other toolchains.

Endpoint-level AI discovery (covering installed applications, external service connections, and local models) combined with Atlassian’s workflow and service management capabilities creates an end-to-end governance architecture that works across Windows, macOS, and Linux without introducing a new platform or requiring teams to change how they work.

For organizations already using Lansweeper alongside Jira Service Management and Assets, extending into AI governance means enabling AI discovery and connecting existing workflows, not a new deployment or a new vendor relationship.

More Certainty and Fewer Assumptions

The organizations that prioritize AI governance won’t be bragging about the most detailed policies. That is an ego metric. What really matters is visibility and translating that intelligence into effective AI governance.

Without discovery, AI usage policies are built on the assumption that employees will follow protocol at all times. Without accurate inputs, governance workflows exist as process for its own sake. Answering tough compliance questions from auditors, regulators, and leadership require defensible strategies grounded in real data.

Lansweeper and Atlassian give organizations the data and those actionable workflows all inside an ecosystem their teams already trust.