Zero Trust Agentic AI Security

Runtime Defense, Governance, and Risk Management for Autonomous Systems

Agentic AI systems have shifted from generating text to executing actions. They call APIs, query databases, and modify infrastructure without human intervention.

This autonomy creates a critical attack surface that static analysis and traditional firewalls cannot cover. Zero Trust Agentic AI Security defines the architecture needed to secure these autonomous workloads where it matters most: at runtime.

This book moves beyond high-level theory to practical implementation. It details the specific risks inherent in autonomous execution, Model Context Protocol (MCP) exposure, and multi-cloud AI workloads. You will learn to apply the A2AS framework and operationalize the NIST AI RMF using strict runtime controls.

What You Will Learn:

• The Shift to Agentic Risks: Understand how autonomous executors differ from predictive models and why they require distinct threat modeling.
• Zero Trust Implementation: Apply Zero Trust principles specifically for AI agents using AccuKnox AI-SPM and AI-DR.
• Runtime Enforcement: Use KubeArmor to enforce system-level policies that prevent unauthorized file access, network calls, and process execution.
• Prompt & Response Defense: Implement firewalls to sanitize user inputs and block malicious model outputs before they cause damage.
• Continuous Governance: Maintain an up-to-date AI-BOM and automate compliance checks against evolving standards.

This guide targets security architects, DevSecOps engineers, and CISOs building defense-in-depth for AI. Stop relying on passive monitoring or “human in the loop” safety nets. Start enforcing runtime policies that block threats before they execute.