Setting Security Boundaries for Agentic AI: From Concept to Implementation

Gal Helemski January 12, 2026

2 min read

How policy-based authorization governs autonomous AI at enterprise scale

Join this webinar to:

Understand the core security risks introduced by agentic AI in enterprise environments
Identify the critical control points across the agentic AI lifecycle
Learn how policy-based authorization enforces intent, scope, and allowed actions
Evaluate whether multiple layers of control are necessary, and how to design them effectively
Clarify whether to control human identities, agent identities, or both
Explore why dynamic, context-aware authorization is essential for scalable and secure agentic AI

John Tolbert, Lead Analyst at KuppingerCole will frame the discussion within the broader identity and authorization landscape, examine emerging patterns in agentic AI security, highlight architectural control points, and provide independent guidance on aligning AI autonomy with Zero Trust and policy-based access strategies.

Gal Helemski, CPO & Co-founder at PlainID will explore real-world agentic AI risks, explain how policy-based authorization enforces intent and scope, demonstrate layers of control across agents and humans, and share practical approaches to preventing data leakage while enabling scalable AI-driven innovation.

Agentic AI promises massive efficiency gains by autonomously executing complex business workflows. Yet as autonomy increases, so does risk. Without enforceable boundaries, AI agents can overreach, accessing sensitive data, triggering unauthorized actions, or disrupting critical systems at machine speed.

Establishing secure agentic AI requires intent-aware, policy-based controls embedded across the entire agentic flow. Modern authorization architectures enable dynamic, context-aware decisions that govern what agents can access, when, and under which conditions, aligning autonomy with enterprise security, compliance, and operational resilience.