AI security still treats intelligence like infrastructure. We secure ports, protocols, identities, and access controls, then hope the agent behaves. But agentic AI is fundamentally language-mediated. Agents interpret goals, explain decisions, negotiate constraints, invoke tools, and delegate work through language. That gives defenders a new control surface: trust itself.

Humans already build trust dynamically. We observe whether another person is consistent, honest, competent, transparent, and aligned with our expectations. Trust grows when behavior matches those expectations and contracts when it does not. This session applies those same relational mechanics to AI, converting human trust dimensions into measurable signals that can be evaluated continuously as an agent acts.

The result is a model that goes beyond deterministic security. Instead of asking only whether an agent has permission to perform an action, we can ask whether its language and behavior continue to justify that permission. Trust becomes something that can be earned, monitored, challenged, reduced, and restored in real time.

Because the framework evaluates language and behavior rather than a model’s specific skills or architecture, it remains portable across vendors, model upgrades, and new generations of agents. Once trust can be measured, it can support stronger security decisions, more defensible governance, clearer accountability, and eventually insurable AI risk.

This session empowers the audience to learn:
1) How to define what trustworthy AI behavior really means
2) Three distinct trust exercises that quantitatively measure trust within AI structures
3) Convert behavior into a security and governance decision