Artificial intelligence (AI) is rapidly becoming embedded in everyday business operations. Employees use it to write code, analyze contracts, summarize research, and automate workflows. Business units deploy AI assistants inside software-as-a-service (SaaS) platforms. Developers experiment with open source models. Agents act autonomously across systems.
All of this promises speed and innovation. It also introduces a problem that security leaders have seen before: You cannot secure what you cannot see.
In a modern enterprise, AI extends far beyond a single chatbot. It includes models hosted in cloud environments, coding assistants running on endpoints, agents executing tasks, embedded AI inside enterprise platforms, and connections to public generative AI services. It also includes the infrastructure supporting them, such as model hosting environments and agent orchestration services.
Equally important is AI usage itself. Telemetry from endpoints, network traffic, SaaS access, and cloud environments reveals how employees interact with AI tools and what data flows through them. Without aggregating these signals, organizations lack a complete picture of their AI footprint.
Why AI visibility and shadow AI are harder to tackle
AI activity spans endpoints, browsers, SaaS applications, and cloud platforms. Most security tools capture only a portion of that activity. Endpoint tools cannot see SaaS traffic. Cloud scanners miss user behavior. Network controls lack insight into embedded AI features inside enterprise apps.
This fragmentation creates blind spots. Without centralized visibility, security teams cannot confidently answer a basic question: What AI is actually in use?
Shadow IT once referred to unsanctioned apps. But now, Shadow AI includes unsanctioned tools but also encompasses models, agents, and AI infrastructure deployed outside oversight. Organizations often discover hundreds of AI apps in use despite approving only a handful.
The gap between perceived usage and reality is where risk accumulates.
Risks of unseen AI usage
When AI operates outside security oversight, multiple risks emerge:
- Sensitive data may be pasted into public AI tools, exposing proprietary or regulated information.
- Unsanctioned tools may introduce malicious links or harmful content.
- Vulnerable open source models may be deployed internally.
- Autonomous agents may access data far beyond their intended scope.
AI also creates new compliance challenges. Regulations and frameworks such as the EU AI Act, National Institute of Standards and Technology (NIST) risk management guidance in the U.S., and emerging AI security standards expect organizations to demonstrate governance and ongoing monitoring. Unknown AI usage undermines both.
The first step: Know what you have
Every security discipline begins with asset visibility. AI is no different. Organizations need the ability to observe AI interactions in line with traffic flows while also discovering when infrastructure and models are operating out of band. Once visibility is established, teams can assess risk, benchmark models, define policy, and monitor usage over time.
AI can accelerate innovation and productivity. But without visibility, it also accelerates risk.
Enterprises that want the benefits must first illuminate the full scope of their AI environment — from public generative AI tools and coding assistants to embedded SaaS AI, autonomous agents, and the infrastructure hosting the models themselves. Only with complete visibility can organizations ensure that every AI interaction, model, and workflow operates within security, governance, and compliance guardrails.
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