Shadow AI discovery lets you gain visibility into the use of generative AI and AI-powered services that are not formally approved or governed by your organization. It helps you understand where and how users interact with AI tools, so you can assess potential data exposure and risk before defining enforcement or governance policies.
Shadow AI discovery focuses on awareness and insight. It does not block traffic by default, and it does not inspect the content of AI prompts or responses.
Shadow AI discovery identifies interactions with AI services across Internet traffic, including:
- Public generative AI tools
- AI-powered features embedded within SaaS applications
- Direct user interactions with AI services from sites or remote users
These interactions may occur without explicit IT approval and outside of sanctioned enterprise AI platforms.
Shadow AI discovery is based on network-level visibility and traffic analysis.
Cato identifies AI services using a combination of application signatures, domains, and behavioral patterns. This includes both standalone AI applications and AI capabilities embedded within broader SaaS platforms.
Traffic is continuously analyzed and classified to detect AI-related activity based on protocol, destination, and usage behavior. This approach does not rely on API integrations with AI vendors or agent-based inspection on endpoints.
AI usage is then correlated with network context, enabling visibility into who is using AI services, from where, and on which devices, without inspecting the content of user interactions.
In addition, Cato provides discovery, configuration visibility, and risk assessment for AI models embedded within SaaS applications (such as Microsoft 365 Copilot or Google Workspace AI features). These embedded AI capabilities are identified as part of the same analysis process, ensuring they are not overlooked.
Cato AI Security evaluates the risk posture of these embedded AI features, including the specific plans or configurations in use, and enables organizations to enforce policies governing how AI is accessed and used within these applications.
Shadow AI discovery collects metadata and contextual information related to AI usage. The information collected includes:
- AI application or service identity
- Application category and AI classification
- Source context, such as user, site, or IP address
- Destination AI service
- Frequency and volume of AI-related activity
Shadow AI discovery insights are presented in a dedicated Shadow AI page in the Cato Management Application. This page is designed to give you an aggregated, risk-aware view of AI usage across your organization.
The Shadow AI page helps you answer key questions such as:
- Which AI applications are being used in the environment
- How widely each AI application is used, based on users and interactions
- Which AI applications represent higher risk and require closer review
Use these insights to understand AI adoption patterns, identify unsanctioned or high-risk AI services, and decide whether additional governance or policy controls are required.
ABC Company lets employees access the Internet freely to support productivity and collaboration. Over time, users begin adopting public generative AI tools and AI-powered features to help with tasks such as content creation, research, and coding. These services are often used without formal approval or visibility from IT.
As an IT admin, you use Shadow AI discovery to analyze Internet traffic routed through the Cato Cloud and identify interactions with AI services. The Shadow AI page builds an inventory of AI applications in use, shows how widely they are adopted, and highlights applications that may introduce higher risk.
Using these insights, you can understand how AI is being used across your organization, identify unsanctioned or high-risk AI services, assess potential data exposure, and make informed decisions about AI governance and sanctioned AI platforms.
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