## 6 Conclusions  
This study addresses the issue of high-quality design and implementation of filter interfaces in artificial intelligence to reduce the risks of outputting low quality or erroneous data. The paper presents a proof-of-concept (PoC) experiment evaluating the Aligned Validation Interface (AVI), which demonstrates significant effectiveness of the filter in mitigating critical risks of using large language models (LLM). In particular, AVI reduced the injection success rate by 82%, reduced the average toxicity of output by 75%, and achieved high accuracy in detecting personally identifiable information (PII) with an F1-score of approximately 0.95. In addition, a qualitative evaluation of the RAG-based Contextualization Engine showed improvements in the neutrality, factual accuracy, and ethical consistency of the model output in the given parameters and context, while a simplified hallucination detection module flagged 75% of incorrect answers in supervised tests. Despite introducing moderate latency--an average of 85 ms without RAG and 450 ms with RAG--the system demonstrated the ability to maintain acceptable performance for real-time applications. These results validate the ability of AVI to improve the security of linguistic models, reliability, and compliance with regulatory (government, industry, or enterprise) requirements of generative AI deployments in critical domains such as healthcare, finance, and education [56]. Since deploying large language models (LLMs) in real-world business and technology environments raises critical concerns regarding data security, privacy, and ethical compliance, in this study, the authors presented the Agreement Validation Interface (AVI), a modular API gateway that serves as a policy enforcement broker between LLMs and user applications (Figure 5). The design and proof-of-concept evaluation of AVI provides strong initial evidence that combining bidirectional filtering with contextual enhancement can significantly reduce common LLM risks, including flash injection, output toxicity, hallucinations, and personally identifiable information (PII) leakage [68]. Despite the positive results, several limitations require attention. First, the proof-of-concept evaluation was conducted on a narrow set of benchmarks and synthetic inputs, which may not fully reflect the complexities of real-world language use. Expansion to broader datasets, including multilingual and informal content, is needed. Second, the hallucination detection mechanism (Section 4.5) relied on rudimentary keyword matching and was not benchmarked against state-of-the-art semantic verification techniques, such as NLI or question answering-based consistency models [69]. The achieved 75% accuracy on a limited test set, while demonstrating basic functionality, also implies a significant 25% error rate (potentially encompassing both false positives and false negatives), underscoring the need for more sophisticated approaches. A comprehensive analysis of these errors will be critical for future improvements. Furthermore, the evaluation of the PII detection module (Section 4.3) was conducted using a small synthetic dataset. While this allowed for a controlled initial assessment of  
Figure 5: Governance-oriented framework for safe LLM deployment via AVI. Source: made by authors. the module's performance, it does not fully represent the diversity and complexity of PII encountered in real-world applications across different languages and contexts. Validating the PII detection capabilities on larger, more diverse, and real-world datasets is a crucial next step. Similarly, while the RAG module's effectiveness in improving response neutrality and factual grounding was qualitatively demonstrated (Section 4.4, Table 7), a comprehensive quantitative evaluation using specific metrics such as Context Relevance and Context Recall (as outlined in Section 4) was not performed in this PoC and remains an important area for future work to rigorously assess its impact.