Enterprise AI is shifting from simple question-answering assistants to autonomous agents that can reason and complete complex tasks with minimal human oversight. This evolution introduces new security challenges beyond traditional issues like prompt injection. Existing security models focus on identity and access, answering "who" and "what," but fail to address the continuous reasoning and dynamic actions of AI agents. The critical new mindset needed is "runtime trust," which verifies what an AI agent is doing during its execution.Autonomous AI agents interact with numerous interconnected systems, expanding the attack surface and creating evolving risks. Runtime threats like goal drift, excessive tool invocation, memory poisoning, context manipulation, and multi-agent amplification exploit these vulnerabilities. Runtime trust addresses these by continuously validating AI behavior beyond initial authentication. Key capabilities include intent validation, behavioral monitoring, policy enforcement, least-privilege execution, and human oversight for high-impact decisions.This runtime trust approach extends to the entire AI ecosystem, including servers, tools, knowledge repositories, and persistent memory. Enhanced operational visibility through logging and analytics is crucial for understanding agent decision-making. Organizations should integrate runtime trust into existing governance by inventorying agents, applying least privilege, and monitoring for anomalies. As AI becomes more autonomous, security must shift from initial authentication to continuous verification of safe behavior throughout an agent's lifecycle. Future AI security will depend on establishing and measuring trust in real-time during AI decision-making.