Zero Trust AI
Zero-Trust AI Security Framework
Mission Statement
To democratize AI security by creating an open, educational framework that enables developers to build, evaluate, and secure specialized AI agents from the ground up—applying zero-trust principles to ensure safe collaboration in the emerging agentic AI ecosystem.
Project Description & Overview
The Zero-Trust AI Security Framework is a staged, educational project designed to address the critical security challenges facing interconnected AI systems. As AI agents increasingly communicate through protocols like Model Context Protocol (MCP) and operate in collaborative multi-agent environments, traditional security approaches are insufficient. Zero-Trust AI provides both the tools and the knowledge to build security into AI systems from their foundation, adhering to the core principle: never trust, always verify.
Model access controls
Core Objectives
Develop a Zero-Trust AI Architecture
Enable Learning Through Stages
Build Reusable Templates
Foster a Security-Conscious AI Community
Share knowledge openly, encourage contributions and testing, and make AI security accessible to developers without formal security training.
Why This Matters
As agents become interconnected via MCP and other protocols, the attack surface expands across systems.
Perimeter-based security fails in collaborative, multi-agent environments—zero-trust is required.
Most developers lack a framework tailored to agentic AI—Zero-Trust AI provides practical, open guidance.
Zero-Trust Principles Applied to AI
Verify every agent interaction – No implicit trust between agents
Assume compromise – Design systems to remain secure even if agents are compromised
Least-privilege access – Agents get only the minimum permissions needed
Continuous monitoring – Real-time evaluation of agent behavior and communications
Context-aware security – Dynamic policy enforcement based on behavior patterns
Approach
The project follows a build-in-public, staged methodology:
Each stage introduces new zero-trust security concepts and capabilities
All components emphasize security-by-design rather than security-as-afterthought
RAG integration provides flexibility to adapt to emerging threats while maintaining verification
Templates and patterns are designed for reusability across domains
Open-source under AGPL v3 to ensure transparency and community benefit
Vision
A future where AI developers have accessible, practical tools to build zero-trust AI systemswhere every agent interaction is verified, every communication is secured, and security is not a barrier to innovation but a foundation that enables safe, collaborative AI ecosystems to flourish.
Never trust. Always verify. Build secure AI.
