MACAW is built on peer-reviewed research in distributed systems security. Our core insight: instead of trying to detect AI attacks (which doesn't work), we bound agentic systems deterministically using the same cryptographic techniques that secure distributed systems. The result - breaking your AI requires breaking cryptography, not crafting clever prompts.

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We're building in the open. If you're a practitioner or researcher working on agentic AI security, we'd love to connect. research@macawsecurity.com

TypeTitle
PaperAuthenticated Workflows: A Systems Approach to Protecting Agentic AI

The foundational paper. Why detection-based AI security fails, how to bound agentic systems deterministically, and the four attack surfaces (prompts, tools, data, context) that cover all attack vectors.

PaperProtecting Context and Prompts: Deterministic Security for Non-Deterministic AI

How to make prompts and context cryptographically authentic. Lineage tracking for derived prompts, hash-chained context for tamper-evidence, and why permissions can only narrow as workflows execute.

White PaperZero Trust Agentic Identity

The missing infrastructure layer for enterprise AI. Why traditional identity fails for AI agents.

GuideMAPL Policy Guide

Complete tutorial for the MACAW Agentic Policy Language. Policy structure, inheritance, parameter constraints, and attestations.

ArchitectureIdentity Flow Architecture

MACAW's layered identity architecture. How JWT tokens flow through adapters and clients with multi-user isolation.

ReferenceClaims Mapping Reference

Mapping enterprise identity provider claims to MACAW's policy model. Examples for Keycloak, Okta, Azure AD, and Google.

GuideDelegated Authentication

How AI agents act on behalf of users with scoped permissions, audit trails, and cryptographic verification.