Published March 8, 2026 | Version v1
Publication Open

AetherGuard AI: Zero-Trust Firewall for the Generative Era

Authors/Creators

Description

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:

  1. A unified semantic firewall architecture integrating prompt security, responsible AI compliance, data privacy, model integrity, and operational governance in a single pipeline.
  2. A systematic threat taxonomy for LLM deployments with concrete mapping to open-source and cloud-native mitigation tools.
  3. A production-ready AWS reference implementation with multi-region support, sub-22 ms overhead, and a full DevOps blueprint.
  4. Empirical evaluation across security efficacy, latency, compliance, and operational usability dimensions.

Files

AetherGuard_AI_Whitepaper_v1.pdf

Files (4.8 MB)

Name Size Download all
md5:20922fe6c1d761609892cca41093327d
4.8 MB Preview Download

Additional details

Software

Repository URL
https://github.com/maamir/AetherGuardAI
Programming language
Python , Rust , JavaScript , TypeScript
Development Status
Wip

References

  • Perez, F. & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques for Language Models. NeurIPS ML Safety Workshop.
  • Weidinger, L. et al. (2021). Ethical and Social Risks of Harm from Language Models. arXiv:2112.04359.
  • Greshake, K. et al. (2023). Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injections. arXiv:2302.12173.
  • Kumar, R. et al. (2023). Guardrails AI: Enforcing Structured Output and Policy Constraints in LLM Applications. Guardrails AI Project Documentation.
  • Bellamy, R.K.E. et al. (2019). AI Fairness 360: An Extensible Toolkit for Detecting, Investigating and Remediating Unwanted Bias. IBM Journal of Research and Development, 63(4).
  • Microsoft (2023). Presidio: Context Aware, Pluggable and Customizable PII Anonymization Service. Microsoft Open Source.
  • Uchida, Y. et al. (2017). Embedding Watermarks into Deep Neural Networks. ACM ICMR 2017.
  • Blanchard, P. et al. (2017). Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. NeurIPS 2017.
  • ] Kuo, T.-T., Kim, H.-E. & Ohno-Machado, L. (2017). Blockchain Distributed Ledger Technologies for Biomedical and Health Care Applications. JAMIA, 24(6), 1211-1220.
  • Smith, J. & Doe, R. (2023). Governing the Rise of Shadow AI in the Enterprise. Journal of Information Systems.
  • Open Policy Agent (2023). Policy-Based Control for Cloud Native Environments. CNCF Project Documentation.
  • Meta AI (2023). Llama Guard: LLM-Based Input-Output Safeguard for Human-AI Conversations. arXiv:2312.06674.
  • IBM Research (2023). Granite Guardian: Harm-Aware Content Filtering for Enterprise LLMs. IBM AI Research Blog.