Most organizations have a shadow AI problem—they just don't know how big it is. Employees are connecting to public AI services, developers are embedding foundation models into applications, and business units are deploying AI tools outside traditional security review processes. At the same time, organizations are standing up internal AI platforms that introduce entirely new assets to manage.

This session examines how security teams are discovering AI across their environments and building inventories that extend beyond applications to include models, datasets, APIs, agents, vector databases, and AI-enabled business processes. Panelists will discuss where AI exposure is most commonly hiding, the visibility gaps that create unnecessary risk, and how organizations are establishing continuous discovery rather than one-time assessments.

Not every AI exposure deserves the same level of attention. Security teams must distinguish between AI experiments with limited business impact and AI systems that can access sensitive data, make operational decisions, or execute privileged actions. Effective AI security requires understanding not just where AI exists, but how it interacts with enterprise data, identities, infrastructure, and business processes.

This session explores practical approaches for evaluating AI exposure based on context, privilege, business criticality, and potential impact. Rather than chasing every vulnerability or model weakness, panelists will discuss how organizations are identifying the AI risks most likely to create material business consequences.

Discovering and prioritizing AI exposure is only valuable if organizations can reduce it. As AI becomes embedded in business operations, security teams need repeatable controls that continuously monitor AI environments, enforce least privilege, detect abnormal behavior, and validate that security measures remain effective over time.

This session focuses on the operational side of AI exposure management. Panelists will share how they are integrating AI security into existing security operations, implementing continuous monitoring, preparing for AI-related incidents, and measuring whether AI exposure is actually decreasing as adoption grows.