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

2025-10-29

AI Summary

Language-model-driven autonomous agents face critical safety and governance challenges in production environments. Method: This paper introduces AAGATE—the first end-to-end governance platform for autonomous AI agents—grounded in the NIST AI Risk Management Framework (AI RMF). It integrates MAESTRO threat modeling, hybrid AIVSS/SSVC risk scoring, and red-teaming, while introducing three novel components: the Digital Identity Rights Framework (DIRF), Logic-layer Protection and Control Instrumentation (LPCI), and Quantitative Semantic Adversarial Fidelity (QSAF) monitoring for cognitive degradation. Built on Kubernetes-native infrastructure, zero-trust service mesh, explainable policy engine, behavioral analytics, and decentralized accountability hooks, it enables fine-grained, verifiable, and adaptive governance. Contribution/Results: Experiments demonstrate that AAGATE significantly enhances security, traceability, and regulatory compliance of autonomous AI systems, enabling trustworthy, enterprise-scale AI deployment.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Governs autonomous AI agents' security and governance challenges
• Operationalizes NIST AI Risk Management Framework for agentic systems
• Provides continuous verifiable governance across systemic adversarial ethical risks

Innovation

• Kubernetes-native control plane for autonomous AI governance
• Operationalizes NIST AI RMF with specialized security frameworks
• Integrates zero-trust service mesh and behavioral analytics