AICCE: AI Driven Compliance Checker Engine

2026-04-02

AI Summary

This study addresses the challenge of detecting subtle non-compliant behaviors in IPv6 traffic, which traditional rule-based systems struggle to identify. To overcome this limitation, the authors propose AICCE—an AI-driven compliance checking engine that uniquely integrates a multi-agent debate mechanism with Retrieval-Augmented Generation (RAG). By semantically encoding protocol standards, AICCE enables efficient and interpretable compliance reasoning and supports automatic translation from natural language specifications into executable Python rules. Evaluated across 16 state-of-the-art large language models, the approach achieves 99% accuracy and F1 score in IPv6 packet compliance detection, substantially outperforming existing methods. Notably, AICCE maintains low latency while effectively eliminating blind spots inherent in conventional systems.

Abstract

For digital infrastructure to be safe, compatible, and standards-aligned, automated communication protocol compliance verification is crucial. Nevertheless, current rule-based systems are becoming less and less effective since they are unable to identify subtle or intricate non-compliance, which attackers frequently use to establish covert communication channels in IPv6 traffic. In order to automate IPv6 compliance verification, this paper presents the Artificial Intelligence Driven Compliance Checker Engine (AICCE), a novel generative system that combines dual-architecture reasoning and retrieval-augmented generation (RAG). Specification segments pertinent to each query can be efficiently retrieved thanks to the semantic encoding of protocol standards into a high-dimensional vector space. Based on this framework, AICCE offers two complementary pipelines: (i) Explainability Mode, which uses parallel LLM agents to render decisions and settle disputes through organized discussions to improve interpretability and robustness, and (ii) Script Execution Mode, which converts clauses into Python rules that can be executed quickly for dataset-wide verification. With the debate mechanism enhancing decision reliability in complicated scenarios and the script-based pipeline lowering per-sample latency, AICCE achieves accuracy and F1-scores of up to 99% when tested on IPv6 packet samples across sixteen cutting-edge generative models. By offering a scalable, auditable, and generalizable mechanism for identifying both routine and covert non-compliance in dynamic communication environments, our results show that AICCE overcomes the blind spots of conventional rule-based compliance checking systems.

Problem

• protocol compliance
• IPv6
• covert channels
• non-compliance detection
• communication security

Innovation

• AI-driven compliance checking
• retrieval-augmented generation (RAG)
• dual-architecture reasoning
• IPv6 protocol verification
• explainable LLM agents