Using Veza AI Agent Security to Discover and Manage Shadow AI

In 2026, the rise of Shadow AI poses a significant security threat to enterprises, with a majority of AI-related breaches stemming from inadequate access controls. While many organizations plan to deploy agentic AI, few have established robust governance models, leading to a growing issue of unmonitored AI agents operating within cloud platforms like AWS, Azure, or GCP. These agents, often launched without the knowledge of IT or security teams, create vulnerabilities as they autonomously interact with internal resources and external tools, forming an expanding attack surface. Shadow AI can manifest through private Model Context Protocol (MCP) servers, public tool sprawl, and silent permissions, making it challenging for organizations to maintain visibility and control. To combat these risks, solutions like Veza's Access Graph offer tools to unmask and audit AI agents, identify orphaned AI identities, and enforce accountability by mapping agents to human owners. With the prediction that over 50% of enterprise data access will soon be facilitated by autonomous agents, the need for comprehensive visibility, monitoring, and governance of AI activities is imperative to secure sensitive data against unauthorized access.