AI adoption is moving fast in every enterprise. Teams are using copilots, plug-ins, agents, and AI-enabled SaaS tools to write code, automate work, analyze data, and move faster.
That speed is great for productivity. But it creates a new security problem most organizations are not ready for: shadow AI. Just like shadow IT, shadow AI often grows outside security’s visibility and control. The difference is that with AI, the risk can scale much faster.
What is AI Observability?
AI observability is the ability to continuously see and understand what AI systems are doing in production, so you can detect risk, prove compliance, and troubleshoot issues. Instead of “we approved this AI tool once,” observability answers “what is it doing right now, and has anything changed?”
What AI observability typically covers
- Usage and activity: who (or which agent) is using the AI, how often, and for what workflows.
- Data exposure signals: what kinds of data are being sent to the model (sensitive, regulated, customer data), and where it’s going.
- Access and actions: which systems the AI can access (via OAuth/apps/agents) and what actions it takes (read/write/export/delete).
- Behavior drift: changes over time, like new permissions, new integrations, new tools, or an agent suddenly doing unusual things.
- Logging and audit trails: keeping evidence of prompts/outputs (where appropriate), tool calls, and access events for investigations and audits.
- Quality and safety signals (when relevant): hallucinations, policy violations, toxic content, or inconsistent outputs, depending on the use case.
How it’s different from “AI discovery”
- AI discovery: “What AI tools/agents exist and what are they connected to?”
- AI observability: “What are those tools/agents doing over time, and is anything risky or abnormal happening?”
The Real Risks of AI in the Enterprise
Most security leaders aren’t worried about AI because it’s “new.” They’re worried because AI changes how work gets done and how data moves.
Here are the core risks showing up today:
1) Data exposure (often accidental)
Employees paste sensitive info into AI tools. AI plug-ins connect to business systems. Agents summarize internal documents. If the tool is not approved, or data handling isn’t clear, sensitive data can leak quickly.
2) Unclear access and permissions
Many AI tools connect through OAuth, API tokens, service accounts, or integrations. That means they can gain real access to systems like Google Workspace, Salesforce, Jira, GitHub, Slack, and cloud environments.
When access is broad, the blast radius is broad.
3) Compliance and audit gaps
Security teams are increasingly asked: Which AI tools are used? Who approved them? What data do they touch? Can we prove controls?
For many organizations, the honest answer is: not really.
4) Fast adoption, slow security review
AI adoption spreads in days. Traditional security processes move in weeks. That mismatch is exactly how shadow risk becomes normal.
What Makes Shadow AI Worse Than Shadow IT
AI tools and agents don’t just exist as “software.” They often create:
- OAuth grants
- API tokens
- service accounts
- bot users
- agent identities
Once those exist, they can persist quietly, even if the original “experiment” ends. That’s how organizations end up with unknown access paths, excessive permissions, and “zombie” AI identities.
In a large enterprise, that can become thousands of agent-linked identities in a short time. And if you don’t know they exist, you can’t secure them.
The Big Problem: You Can’t Protect What You Can’t See
Most AI security conversations jump straight to policy: what should be allowed, what should be blocked, how to govern.
But policy doesn’t work without visibility.
If you don’t know:
- which AI tools are in use,
- which identities they created,
- what systems they can access,
- and what permissions they have,
then you’re trying to manage risk blind.
The Solution: AI Discovery (Wing’s Approach)
The fastest way to reduce shadow AI risk is to start with one thing: AI Discovery.
Wing helps security teams discover AI usage and AI-connected access across the enterprise so you can bring shadow AI into the light and control it.
With AI Discovery, you can answer the questions CISOs get asked every day:
- What AI tools are being used right now?
- Where did they connect?
- What access did they get?
- Which connections are risky or excessive?
- Who owns each tool or integration?
Discovery gives you the foundation to take action, whether that action is tightening access, removing risky connections, enforcing approvals, or creating a safer path for teams to use AI.
From Shadow AI to Safe AI
The goal isn’t to stop AI adoption. That won’t work, and it would hurt the business. The goal is to make AI adoption visible, controlled, and secure.
Shadow AI is only dangerous when it’s invisible. With AI Discovery, security teams can get ahead of the sprawl before it becomes unmanageable. If AI is already spreading across your environment, start with discovery. Because the risks aren’t theoretical anymore, and the only way to govern AI is to first see it.
To learn more – schedule a demo today