The text discusses the evolving landscape of Artificial Intelligence (AI) and its growing autonomy, referred to as "Agentic AI." This shift introduces new challenges in controlling what data an AI agent can access, how it uses that data, and ensuring its identity or credentials are secure. To address these concerns, Model Context Protocol (MCP), an open standard, has emerged as a unifying framework for AI integrations with data sources and tools. MCP standardizes how AI assistants discover resources, read or write data, and call "tools" on remote systems, enabling enterprises to unify data sources under a single interface that is AI-aware. However, this autonomy also raises security risks such as "permissions creep," identity masquerading, invisible overprivileged access, and cross-system entitlements. To mitigate these risks, organizations need to adopt a robust identity governance approach, recognizing every AI agent as its own distinct identity subject to scrutiny, and leveraging platforms like Veza's Access Graph to gain a holistic view of these new identities and their interactions with data. By embracing MCP and Veza's Access Graph, enterprises can harness the productivity of Agentic AI while ensuring data security and control.