There's a quiet revolution happening on your employees' laptops and workstations right now, and most security teams have only the faintest idea of how big it's grown.
Walk through any office (or peek into any Zoom background) and you'll find people getting wildly more productive thanks to AI: Sales reps drafting outreach with ChatGPT; engineers autocompleting code with a half-dozen different copilots; marketers running briefs through Claude; finance summarizing board decks with Gemini; HR using some tool nobody's heard of to screen resumes. And somewhere, a well-meaning developer just installed a local LLM on their workstation because "it's faster than waiting for IT."
None of this is in your asset inventory, none of it went through procurement, and almost none of it is being monitored by anyone with a security badge.
Welcome to Shadow AI, the natural, inevitable, and slightly terrifying evolution of Shadow IT. This one is already installed on a workstation near you.
Shadow IT has been around forever. Someone signs up for a free Trello account, IT eventually finds out, and you have a meeting about it. Not great, but manageable. Shadow AI is a different beast for three reasons.
Data going in is way more sensitive. When an employee uses an unsanctioned project tool, the worst-case leak is usually project names and task lists. When that same employee pastes source code, a customer contract, a patient record, or an internal forecast into a random AI chat, you may have just handed your crown jewels to a third party—possibly to train someone else's model.
Tools are everywhere and generally free. AI is now embedded in browser extensions, note-taking apps, meeting recorders, IDE plugins, and basically every productivity tool with a marketing department. A workforce user doesn't have to "decide" to adopt Shadow AI. They click "Yes, summarize this for me," and suddenly, your meeting transcript is in someone else's cloud.
AI agents act on behalf of users. This is the part security folks tend to underestimate. The newer wave of AI assistants can read mailboxes, click buttons, call APIs, move files, and execute workflows. The moment an employee grants OAuth access to one of these tools, that agent inherits some slice of the user's identity and permissions. Now you've got a non-human identity (NHI) nobody approved, doing things nobody sanctioned, with access nobody reviewed.
Most of the "AI risk" conversation has focused on big, scary, abstract themes: model bias, hallucinations, geopolitical AI race, etc. Those are real, but they're not the things that are most likely to ruin a Tuesday afternoon for a security team. Here's what is:
When an employee pastes proprietary text into a public AI tool, it's gone. You can't pull it back. Even if the vendor says "we don't train on your data," the prompt may be logged, cached, indexed by third-party plugins, or sitting in a debugging table somewhere. Forensically reconstructing what was leaked, when, and to whom is borderline impossible without endpoint visibility.
Developers paste API keys, connection strings, and private tokens into AI chat windows constantly, often by accident, while debugging. Those keys can authenticate against your production systems. Most organizations have no monitoring that flags that "your employee just shared a secret with a chatbot."
Local LLMs like Ollama, LM Studio, and the open-source models employees download "to play around with" often require administrative privileges, eat huge amounts of disk and GPU, and run as persistent processes. This happens outside any patch, vulnerability, or compliance program.
Regulated industries don't get to plead ignorance. Protected health information (PHI) fed into a non-business-associated agreement (BAA)-covered AI tool constitutes a HIPAA violation. An EU citizen personally identifiable information (PII) going into a model with no Data Protection Authorities (DPA) in place? Hello, GDPR. Material non-public information through a third party with no audit trail? Securities regulators would like a word.
When an AI assistant connects to a user's Microsoft 365, Google Workspace, GitHub, or Salesforce via OAuth, that integration is effectively a long-lived non-human identity. It can read everything the user can read, act on their behalf, and persist long after the human stops using the tool. AI agent sprawl is going to make service-account sprawl look quaint.
You don't ban AI. That ship has sailed, and trying to ban it just pushes usage further underground while putting your organization at a competitive disadvantage. The goal is visibility, then sensible guardrails, then enablement, in that order.
Here are four moves that go a long way.
You can't govern what you can't see. Most organizations are surprised when they finally do a real inventory of AI tools running on employee workstations: dozens of browser extensions, multiple installed apps, local model runtimes, and OAuth grants to AI services nobody has heard of.
Endpoint privilege management and application control tools can give you a real-time view of what's installed, what's launching, and what's escalating privileges, including the new AI binaries you didn't know existed.
If your only response to Shadow AI is "thou shalt not," people will route around you. Instead, give the workforce a short, curated list of approved AI tools, with the data-handling questions already answered, the contracts already signed, and the integration already secured. You might even have a few enticing AI ‘skills’ already built to help entice them to use tools of officialdom!
When sanctioned tools are better than the shadow ones with faster login, integrated with corporate identity, no copy/paste workaround needed, then adoption follows. The catalog should be easy to find, easy to request additions to, and reviewed often (this space moves fast).
This is where things get interesting. AI agents aren't going to stop multiplying; they're going to explode. Every OAuth grant, every browser extension, every "let this assistant access your inbox" is creating a non-human identity with some slice of access.
Treat these like you would any other privileged account. They need to be discovered, inventoried, scoped to least privilege, monitored for unusual behavior, and rotated or revoked when they're no longer needed. The privileged access management (PAM) muscle your organization has been building for years applies directly here; you're just extending it to a new and rapidly growing class of identity. If an AI agent doesn't need access to your CRM, don't give it access to your CRM. If a developer's local model wrapper doesn't need admin rights to do its job, remove them.
Last one, and it might be the most important: Most Shadow AI usage isn't malicious. It's people trying to do their jobs better with a tool that's genuinely useful. The fastest way to make the situation worse is to treat employees like they're the problem.
Run short, practical training that teaches what data is okay to share with public AI, what isn't, and why. Make the sanctioned path the path of least resistance. And keep a feedback loop open so the workforce can tell you what they need before they go find it on their own.
Finally...
AI on the endpoint isn't a future problem, it's a Tuesday afternoon problem, and it's already here. The organizations that get ahead of it won't be the ones that say "no." They'll be the ones who see clearly, govern thoughtfully, and treat every new AI tool and every new AI agent as just another identity that needs to be discovered, scoped, and watched.
Delinea Privilege Manager can discover any process or executable running on an endpoint. The latest release of Privilege Manager includes pre-built monitoring rules, using filters and policies, to help you identify the leading AI tools running on your employees’ laptops and PCs. It’s an easy way to gain that visibility and begin to understand what shadow AI you may have running on your endpoints.
Your employees will keep using AI. The only real question is whether you can see it.