AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI

TL;DR

This work tackles the governance gaps for autonomous agentic AI by introducing AAGATE, a Kubernetes-native platform that operationalizes the NIST AI RMF. It combines MAESTRO-based mapping, AIVSS/SSVC-based measurement, and CSA Red Teaming for proactive management, augmented by zero-trust networking, explainable policy (Rego), and on-chain accountability (ETHOS/DIRF). Key innovations include the Janus Shadow-Monitor-Agent for continuous runtime evaluation, ZK-proofs for verifiable compliance, purpose-bound credentials via OAuth Relay, and a unified model that bridges policy, security, and AI development. The platform promises continuous, auditable governance across system, data, and agent ecosystems, with an open-source MVP designed to be adopted and adapted across enterprises.

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.

Figures (6)

• Figure 1: Kubernetes-native architecture with service mesh and observability.
• Figure 2: AAGATE operationalizes the four core functions of the NIST AI RMF (Govern, Map, Measure, Manage) with specific security frameworks and implementations.
• Figure 3: MAESTRO Threat Mapping & AAGATE Mitigations.
• Figure 4: AAGATE system architecture showing real-time data flows within the zero-trust Kubernetes environment and the governance process.
• Figure 5: Agent Identity Management.