## 1 Introduction
This is primarily achieved through its Input Validation Module (IVM) and Output Validation Module (OVM); (2) positive filtering: beyond simply blocking or modifying problematic content, AVI proactively aims to improve the quality, factual accuracy, and safety of LLM responses. This is achieved by injecting verified, trusted context into the LLM's generation process, primarily through its RAG-based Contextualization Engine (CE). This bidirectional approach ensures comprehensive oversight of the LLM interaction lifecycle. The central hypothesis tested in this work is that the AVI framework can effectively mitigate a broad spectrum of LLM-associated risks (including prompt injection, toxic content, PII leakage, and hallucinations) across diverse LLMs while offering superior flexibility and control compared to relying solely on built-in model safety features and imposing acceptable performance overhead. The main aim of this work is to present the conceptual architecture and methodology of AVI and to evaluate its feasibility and effectiveness through a proof-of-concept (PoC) implementation. We detail the design of the core AVI modules (Input/Output Validation, Response Assessment, and Contextualization Engine) and their orchestration. We introduce a mathematical model and metrics for evaluation. Finally, we present preliminary results from our PoC evaluation, demonstrating AVI's potential to enhance the safety, reliability, and ethical alignment of LLM interactions. This work contributes a practical and adaptable architectural pattern for governing LLM usage, aiming to make LLMs more trustworthy and applicable in critical domains like finance, healthcare, education, and enterprise applications, thereby fostering responsible AI adoption. The described principles are intended to be comprehensible to a broad audience of scientists and engineers working with applied AI. So, this paper addresses the identified research gap and proposes a new technology solution, the Agreed Verification Interface (AVI), which is a composite AI framework that acts as an intelligent API gateway to manage interactions between LLMs and end-users. AVI introduces a dual-pronged approach: (1) negative filtering to detect and block malicious, biased, or privacy-infringing content in both input and output; and (2) positive filtering to enhance factual and contextual relevance using RAG-based reasoning. The interface's modular design supports data input validation, result output evaluation, hallucination detection, and context injection, all managed through an industry, government, or enterprise policy-driven orchestration layer. Unlike black-box model alignments or fragmented security add-ons, AVI is configurable, highly scalable, and independent of the underlying LLM, allowing for flexibility and integration across a wide range of applications and compliance settings [15]. By combining rule-based systems, machine-learning classifiers, named entity recognition, and semantic similarity techniques, AVI enables organizations to dynamically monitor, validate, and adjust LLM responses. Preliminary evaluation of the proof-of-concept system has shown promising results in reducing operative injection success rates, reducing toxicity, accurately identifying PII, and increasing actual accuracy--all while maintaining acceptable latency. As such, AVI represents a significant step toward implementing responsible AI principles in real-world LLM deployments. Section 2 outlines the problem setting and provides an overview of existing approaches to accident analysis in transport systems. Section 3 describes the proposed methodology for analyzing sequences of dangerous events using graph-based and machine learning techniques. Section 4 presents the results of the experimental study, followed by suggestions for further research in Section 5 and conclusions and limitations in Section 6.