Accelerating AI adoption with SASE

Traditional perimeter-based security methods that focus on blocking and filtering cannot address those risks. For example, they cannot prevent data leakage from cloud-based AI tools or AI agents that are connected to a variety of data sources. Instead, organizations should build a new security model for the AI era using a cloud-based secure access service edge (SASE) platform. A SASE platform places security between users, AI agents, large
language models (LLMs), and data centers. It provides identity-aware controls at the cloud edge, conducting deep inline inspection of AI application usage, preventing data leaks, and securing user interactions across locations and devices.