What question did this study set out to answer?

The aim is to enhance AI security and compliance by developing a robust firewall that ensures data integrity and privacy.

March 10, 2026Open Access

AetherGuard AI: Zero-Trust Firewall for the Generative Era

Key Points

The aim is to enhance AI security and compliance by developing a robust firewall that ensures data integrity and privacy.
Developed a unified semantic firewall architecture.
Created a systematic threat taxonomy for LLM deployments.
Implemented a production-ready AWS reference system with multi-region support.
Conducted empirical evaluations of security efficacy, latency, and usability.
Achieved sub-22 ms overhead in the firewall system.
Demonstrated effective integration of prompt security and responsible AI compliance.
Provided a comprehensive threat mitigation mapping to open-source tools.

Abstract

AetherGuard AI is presented as a holistic AI Trust & Integrity Gateway that expands the typical AI firewall paradigm to offer real-time semantic inspection, cryptographic accountability, responsible AI compliance, and robust operational governance. The system operates as a transparent reverse-proxy between LLM clients and providers, intercepting every prompt and response for multi-dimensional analysis before permitting egress or ingress. This paper makes the following primary contributions: A unified semantic firewall architecture integrating prompt security, responsible AI compliance, data privacy, model integrity, and operational governance in a single pipeline. A systematic threat taxonomy for LLM deployments with concrete mapping to open-source and cloud-native mitigation tools. A production-ready AWS reference implementation with multi-region support, sub-22 ms overhead, and a full DevOps blueprint. Empirical evaluation across security efficacy, latency, compliance, and operational usability dimensions.

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