Securing Agentic AI Across Enterprise Environments

AI risk used to center on data exposure from user inputs and AI outputs. Agentic AI adds autonomous behavior, creating security requirements around what agents can access, connect to, execute, and be influenced by.

This creates exposure across employee AI usage and homegrown AI applications, which map to two agent models: local agents running on machines and devices, and managed agents operating through cloud-based platforms.

Agents can interact with tools and APIs, run workflows, operate with limited human direction, and introduce a new attack path where malicious or manipulated content from external tools can influence decisions and actions.

This eBook gives security and AI leaders a practical understanding of where agent risk lives, what behavior must be governed, and how to apply consistent controls across enterprise environments.

What’s inside:

• Understand how agentic AI changes enterprise risk: How autonomous actions expand security considerations beyond prompts, outputs, and data exposure.
• Identify where agent risk emerges: How employee AI usage and homegrown AI applications create different security and governance challenges.
• Understand local and managed agents: How different agent models operate across machines, devices, cloud platforms, and enterprise systems.
• Build a governance model for agent behavior: The actions, access, workflows, and external influences that require visibility and control.
• Apply consistent controls across agentic environments: How Cato delivers visibility, monitoring, protection, and runtime enforcement across AI usage and homegrown AI applications.