Why Control & Prevention Are Needed in the AI Era

As employees adopt more AI tools and assistants in their daily work, the endpoint has become the primary place where sensitive data is accessed, processed, shared, and sometimes unintentionally exposed. Files are summarized by AI, customer records are pasted into prompts, internal strategies are discussed with chatbots, and code is reviewed by AI copilots. All of this is happening at the edge, on endpoints you already manage – but in ways traditional endpoint security programs were never designed to see or control.

This shift creates a new class of risk:

• Data leaving the organization through AI prompts and plugins

• AI tools with unclear data retention and training policies

• Embedded AI features in SaaS and productivity apps that bypass existing controls

• Non-human identities and agents making decisions and acting on data autonomously

Most legacy endpoint and EDR approaches were built to detect malware and respond to incidents after they occur. They were not built to understand AI usage patterns, data flows into and out of models, or how AI changes the behavior of users and applications at the endpoint. As a result, you face new visibility gaps, blind spots in your detections, and increased exposure of regulated and business-critical data.

We will explores how endpoint security must change from reactive incident response to proactive, continuous risk reduction before exposure occurs. We will look at what it means to treat the endpoint as the primary gateway to AI and data, and how to design controls that protect that gateway without slowing down innovation and productivity.

By the end of this session, you will have a clear, actionable view of how to modernize your endpoint strategy so it keeps pace with AI adoption across the business. You will leave with concrete steps to close visibility gaps, strengthen prevention where work actually gets done, and build an endpoint program that supports both security and innovation in the AI era.