How Do You Detect Shadow AI Agents Before Your Next Audit Does?

Shadow AI detection is the process of finding AI tools and AI agents running in an organization's environment without IT or security approval. It combines automated methods, such as SaaS-log and OAuth monitoring, platform-native agent inventories, and identity-aware tracking, with a policy that requires every new agent to be registered rather than relying on employees to self-report.

Key Takeaways

• Organizations continue to struggle with AI visibility. AvePoint's State of AI 2026 Report found that the share of organizations unable to determine whether employees are using unsanctioned AI tools nearly tripled, from 6.3% in 2025 to 17.6% in 2026; for AI agents specifically, 21.1% can't say whether unsanctioned agents exist at all.
• Shadow AI and shadow AI agents are related but not identical. Shadow AI is unauthorized tools used by people while shadow AI agents are unregistered, autonomous agents built inside approved platforms.
• No single detection method catches everything. SaaS-log and OAuth analysis, platform-native agent inventories, spend-based discovery, and identity-aware monitoring each close a different blind spot.
• Self-reporting fails at scale. Users can build agents on platforms like Copilot Studio, Power Platform, or Gemini Enterprise, making manual registration increasingly difficult to maintain.
• Detection has to span multiple cloud platforms. Agent-building surfaces exist inside Microsoft 365 and Google Workspace alike, and a scan that covers only one environment can leave significant visibility gaps in the other.
• Finding a shadow agent is the start, not the end. Every agent detected still needs an owner, a permission review, and a register-restrict-or-retire decision.
• Investment is shifting toward continuous detection. 62.4% of organizations plan to increase spend on tools that monitor agent actions for policy alignment over the next 12 months, ahead of buying more agent licenses.

What Is Shadow AI Detection?

Shadow AI detection is the process of finding AI tools and AI agents operating in an organization's environment without going through IT or security review. It covers two related but distinct problems: employees using unapproved AI tools, and AI agents that get created inside approved platforms without ever being registered.

Shadow AI detection builds on the concept of shadow IT: technology adopted without going through standard IT, procurement, or security review processes. Historically, shadow AI has focused on unauthorized AI tool use. As organizations adopt AI agents, many are extending those same visibility and governance practices to unregistered agents operating alongside approved tools.

What Is the Difference Between Shadow AI and Shadow AI Agents?

Shadow AI and shadow AI agents are related but distinct governance challenges. Shadow AI is unauthorized use of an AI tool by a person, such as an employee pasting company data into a consumer chatbot. A shadow AI agent is an unregistered, autonomous agent that takes actions on its own, often built inside a platform IT already approved. The distinction matters because most detection tools focus primarily on unauthorized AI use and may provide limited visibility into autonomous agents.

Understanding the difference helps organizations choose the right controls, because tools designed to detect shadow AI use may not identify unregistered agents operating inside approved platforms. Treating them as the same problem can leave gaps in monitoring, governance, and oversight.

Why Has Shadow AI Detection Become Urgent for AI Agents Specifically?

Shadow AI detection has become urgent because agent creation is becoming easier across business and IT teams. AvePoint's State of AI Report 2026 found that the share of organizations unable to determine whether employees are using unsanctioned AI tools nearly tripled year over year, from 6.3% to 17.6%. For AI agents specifically, that blind spot is even larger: 21.1% of organizations cannot say whether unsanctioned agents exist in their environment at all.

The gap is widening because agent creation has been democratized faster than governance has caught up. A developer with an API key, a business user inside Copilot Studio, and a citizen developer inside Power Platform can all create an agent without any of them going through the same approval path, and none of the three shows up automatically in the other's records.

According to the same report, 46.9% of employees rely on AI agents daily or weekly to complete work tasks. At that scale, detection cannot depend on someone remembering to file a request. It has to be a continuous, automated process, or the count of undetected agents keeps climbing quietly in the background.

What Detection Methods Actually Work?

Effective shadow AI and AI agent detection combines platform-native agent inventories, SaaS-log and OAuth monitoring, spend-based discovery, and identity-aware tracking. Each method catches a different blind spot, and relying on only one leaves the others uncovered.

• Platform-native agent inventories. Copilot Studio, Power Platform, and Gemini Enterprise increasingly expose their own agent lists. Pulling from every one of them into a single view is the first layer, and the one organizations skip most often because it requires going platform by platform.
• SaaS-log and OAuth monitoring. Tracking API calls and OAuth grants surfaces agents connecting to known AI endpoints, even when they were never formally registered.
• Spend-based discovery. Billing and expense data surfaces AI tools and agent platforms that network monitoring alone misses, particularly pay-per-use services procured outside a normal software budget.
• Identity-aware monitoring. Mapping agent activity back to a specific user or service identity, rather than an anonymous connection, is what turns a detection into an actionable finding with an owner attached.

None of these methods are sufficient alone. A program built only on network logs will miss an agent created through a platform's own low-code interface, and a program built only on platform inventories will miss an agent connecting through a developer's personal API key. Effective detection combines platform visibility, identity monitoring, and network telemetry to create a more complete picture of AI activity.

What Is a Step-by-Step Shadow AI Detection Process?

A working shadow AI detection process inventories every platform that can create an agent, pulls a live list from each one, cross-references that list against what's actually registered, assigns an owner to every gap found, and re-runs continuously rather than once a year.

1. Inventory every platform that can create an agent. Include Copilot Studio, Power Platform, Gemini Enterprise, and any developer API access that can spin one up.
2. Pull a live agent list from each platform. Start with the visibility each platform already provides rather than relying on a single central tool.
3. Cross-reference against your approved agent inventory. Compare approved and discovered agents to identify your shadow AI agent count.
4. Assign an owner to every undocumented agent found. Hold someone accountable for each agent the same way you would for a newly discovered user account.
5. Decide: register, restrict, or retire. Apply one of these three outcomes to every shadow agent instead of simply documenting it.
6. Re-run the scan continuously. Repeat the process regularly because agent inventories can become outdated quickly.

What Mistakes Let Shadow Agents Go Undetected?

The most common mistake is buying a detection tool built for human tool-use (browser and DLP monitoring) and assuming it also covers autonomous agents, which need platform-native and identity-aware detection instead. The second most common mistake is treating detection as a one-time project rather than a continuous process.

• Treating shadow AI and shadow AI agents as one problem. They need different detection methods, and a tool built for one rarely covers the other well.
• Relying on policy without technical detection. A written acceptable-use policy doesn't surface a single unregistered agent by itself.
• Scanning once instead of continuously. Agent creation doesn't pause between audit cycles, so neither should detection.
• Detecting without a remediation path. Shadow agents with no owner or process to act on them just produce a longer, still-ungoverned list.
• Assuming Microsoft 365 coverage is enough. Agents built in Google Workspace stay invisible unless detection explicitly covers that environment too.

What Does Shadow AI Detection Look Like Across Microsoft 365 and Google Workspace?

Shadow AI detection has to run natively inside both Microsoft 365 (Copilot Studio, Power Platform, SharePoint agents) and Google Workspace (Gemini Enterprise and connected Google Cloud services), not just the environment an organization adopted first. Each platform surfaces agents differently, and a detection program tuned to only one will read as complete while missing the other entirely.

Many governance programs begin by monitoring the environments they can see most easily. That creates a false sense of coverage: a detection process that only watches Microsoft 365 is not covering shadow AI agents, it is covering shadow AI agents in the one cloud it happened to look at.

AvePoint AgentPulse is built to close that specific gap: it inventories agents across Microsoft 365 and Google Cloud/Workspace from a single view, so a shadow agent created in either environment surfaces in the same place rather than requiring two separate detection processes.

What Should You Do After You Detect a Shadow Agent?

After detecting a shadow agent, assign it an owner, review and correct its permissions, and decide whether to formally register it, restrict what it can access, or retire it. Detection without one of these three outcomes just produces a longer list, not a safer environment.

Detection is the first capability in a complete AI agent management program, not the whole program. Once an organization can reliably find every agent, the next questions are about governance, what to track in an ongoing agent inventory, and how to evaluate a full AI agent management platform if detection alone isn't closing the gap fast enough.

AvePoint's AgentPulse continuously inventories every AI agent across Microsoft 365 and Google Cloud/Workspace, so shadow agents surface automatically instead of waiting for a self-reported request. 

Frequently Asked Questions

What is shadow AI detection?

Shadow AI detection is the process of finding AI tools and AI agents running in an organization's environment without IT or security approval. It uses a combination of automated methods rather than relying on employees to self-report what they've created or adopted.

What is the difference between shadow AI and shadow AI agents?

Shadow AI is unauthorized use of an AI tool by a person, such as pasting data into a consumer chatbot. A shadow AI agent is an unregistered, autonomous piece of software that takes actions on its own, often built inside a platform IT already approved. Most detection tools  focus primarily on unauthorized AI tool usage and may provide limited visibility into autonomous AI agents.

How common is shadow AI in enterprises today?

Visibility into unsanctioned AI activity remains a challenge for many organizations. AvePoint's State of AI 2026 Report found the share of organizations unable to determine whether employees are using unsanctioned AI tools nearly tripled from 6.3% to 17.6% year over year, and 21.1% cannot say whether unsanctioned AI agents exist in their environment at all.

What detection methods catch the most shadow AI agents?

Platform-native agent inventories, such as pulling agent lists directly from Copilot Studio, Power Platform, and Gemini Enterprise, catch the most agents created through approved low-code tools. SaaS-log and OAuth monitoring, spend-based discovery, and identity-aware tracking each add coverage the others miss.

Can employees create AI agents without IT knowing?

Yes. Most modern agent-building tools, including Copilot Studio, Power Platform, and Gemini Enterprise, let any authorized user create an agent in minutes without a purchase order or a formal IT request, which is exactly why manual registration processes fall behind.

What does shadow AI detection mean for Microsoft 365 and Google Workspace?

It means running detection natively inside both environments, since agent-building tools exist in each and agents don't automatically appear in the other platform's inventory. A program that only watches Microsoft 365 will miss every agent created in Google Workspace, and the reverse is also true.

How often should you scan for shadow AI agents?

Scans should be done continuously. Agent inventories change quickly, making periodic reviews insufficient on their own. A scan that only runs once a year can miss months of new, unregistered agents.

What happens after you find a shadow AI agent?

Every shadow agent found needs an assigned owner, a permission review, and one of three decisions: register it formally, restrict what it can access, or retire it. Detection without a next step just produces a longer unmanaged list.

What is the difference between shadow AI detection and AI agent governance?

Shadow AI detection identifies unauthorized AI tools and unregistered AI agents in the environment. AI agent governance is the broader framework of policies, ownership, and risk controls that determines how those agents are evaluated, managed, and monitored. Detection helps organizations find shadow AI and shadow AI agents; governance determines what happens after they’re found.

What should you look for when evaluating a shadow AI detection solution?

Look for platform-native coverage of both shadow AI and shadow AI agents across every cloud where AI tools and agents are used or built. It should provide continuous rather than point-in-time scanning, identity-aware findings that name an owner rather than an anonymous connection, and a clear path from detection to remediation.

Related Questions

→ How do you choose an AI agent management platform?
→ What is AI agent governance, and how do you build a framework for it
→ What should you track in an AI agent inventory?
→ How do you see every AI agent in your environment?
→  What is AI agent security, and how do you prevent agent-related breaches?