Exposure to Response: A Continuous Security Model for the AI era

AI is changing the attack surface, empowering the attacker, and increasing the blast radius faster than traditional assessment cycles were designed to handle. As a result, security posture can no longer be assessed by periodic point-in-time reports. Practitioners must adopt an approach that combines discovery and exposure reduction with detection and response into a continuous feedback loop.

This session explores how security teams can connect these functions into a closed-loop operating model that continuously adapts to change. The objective isn't zero risk. It's reducing exposure continuously and shrinking the time between something changing, something going wrong, and action.

• The Problem - Frontier AI Breaks the Old Cadence
• Why Point-in-Time Assessments Fail
• Continuous Exposure Management: Finding and Reducing Risk
• Why Exposure Reduction Alone Isn't Enough
• Continuous Detection & Response for an AI-Accelerated Threat Landscape
• The Combined Lifecycle - Continuous Exposure Reduction (left) + Continuous Detection & Response

"Reduce the risks you can control. Detect and respond fast to the threats you can't."