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AICCE: AI Driven Compliance Checker Engine

Mohammad Wali Ur Rahman, Martin Manuel Lopez, Lamia Tasnim Mim, Carter Farthing, Julius Battle, Kathryn Buckley, Salim Hariri

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.

AICCE: AI Driven Compliance Checker Engine

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.

Paper Structure

This paper contains 77 sections, 26 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Articles
  3. Network Protocol Validation
  4. Multi-Agent Reasoning
  5. Methodology
  6. Semantic Encoding of Protocol Standards
  7. Document Preprocessing and Chunking
  8. Transformer-Based Embeddings
  9. Semantic Similarity
  10. Vector Database and Semantic Retrieval
  11. HNSW Graph Indexing
  12. Query-Time Retrieval
  13. Reconstructed Sections
  14. Model Families and Architectural Diversity
  15. Dual-Architecture Generative Compliance Evaluation
  16. ...and 62 more sections

Figures (9)

  • Figure 1: Basic Architecture Diagram of a) Llama, b) GPT and c) Mixtral 8x7b
  • Figure 2: Visualization of ANN search with the Hierarchical Navigable Small World (HNSW) algorithm.
  • Figure 3: AICCE Framework in Explainability Mode
  • Figure 4: AICCE Framework in Script Execution Mode
  • Figure 5: Model accuracy comparison under ablation vs. debate-enabled settings for Architecture A. Debate consistently improves accuracy across models, particularly for weaker baselines.
  • ...and 4 more figures