AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI
Abstract
This paper introduces the Agentic AI Governance Assurance & Trust Engine (AAGATE), a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional Application Security (AppSec) tooling for improvisational, machine-speed systems, AAGATE operationalizes the NIST AI Risk Management Framework (AI RMF). It integrates specialized security frameworks for each RMF function: the Agentic AI Threat Modeling MAESTRO framework for Map, a hybrid of OWASP's AIVSS and SEI's SSVC for Measure, and the Cloud Security Alliance's Agentic AI Red Teaming Guide for Manage. By incorporating a zero-trust service mesh, an explainable policy engine, behavioral analytics, and decentralized accountability hooks, AAGATE provides a continuous, verifiable governance solution for agentic AI, enabling safe, accountable, and scalable deployment. The framework is further extended with DIRF for digital identity rights, LPCI defenses for logic-layer injection, and QSAF monitors for cognitive degradation, ensuring governance spans systemic, adversarial, and ethical risks.
Cite
@article{arxiv.2510.25863,
title = {AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI},
author = {Ken Huang and Kyriakos Rock Lambros and Jerry Huang and Yasir Mehmood and Hammad Atta and Joshua Beck and Vineeth Sai Narajala and Muhammad Zeeshan Baig and Muhammad Aziz Ul Haq and Nadeem Shahzad and Bhavya Gupta},
journal= {arXiv preprint arXiv:2510.25863},
year = {2025}
}